MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS
|
|
- Barbra Hubbard
- 6 years ago
- Views:
Transcription
1 Statistica Sinica 8(998, MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS Holger Dette and Michael Sahm Ruhr-Universität Bochum Astract: We consider the maximum variance optimality criterion of Elfving (959 in the context of (nonlinear response models. Some practical guidelines for the construction of minimax optimal designs are given. In some cases this criterion yields one point designs as a consequence of different scales of the elements in the Fisher information matrix. As an alternative a standardized maximum variance criterion is introduced and applied to the calculation of efficient designs. The results are illustrated for inary response models and it is demonstrated that in these models standardized minimax optimality should e prefered to ordinary minimax optimality. Key words and phrases: Binary response models, maximum variance criterion, nonlinear regression models, optimal design, standardized variances.. Introduction Consider a response variale y having a distriution from an exponential family p(y x, θ with Fisher information I(θ, x forθ given an oservation at x. Here θ =(θ,...,θ m T denotes an m-dimensional vector of unknown parameters and x is the explanatory variale which varies in a design space X IR k. A design is a proaility measure on X with finite support and the matrix I(θ, ξ = I(θ, xdξ(x IR m m X is called the Fisher information matrix of ξ. A (locally optimal design maximizes an optimality criterion depending on I(θ, ξ (see Silvey (980, Atkinson and Donev (99 or Pukelsheim (993. In this paper we consider the prolem of constructing optimal designs for inary response models with respect to the partial maximum variance criterion introduced y Elfving (959 Φ(ξ =max i M et i I (θ, ξe i, (. where ξ is a design for which the parameters {θ i } i M are estimale, e i IR m denotes the ith unit vector (i =,...,m, M {,...,m} and I (θ, ξ denotes
2 50 HOLGER DETTE AND MICHAEL SAHM a generalized inverse of I(θ, ξ. In many cases the elements of the Fisher information matrix are of different scale. Therefore we also consider the minimum efficiency criterion ψ(ξ =max i M e T i I (θ, ξe i e T =/ min i I (θ, ηe eff θ i (ξ, (. i i M min η which compares the variances relative to their optimal value otainale y the choice of a design. More precisely, the quantities eff θi (ξ in (. are the efficiencies of the design ξ, relative to the optimal design for estimating the parameter θ i. Designs minimizing (. are called minimax optimal for the parameters {θ i i M}and have een discussed y many authors (see e.g. Murty (97, Torsney and López-Fidalgo (995, Krafft and Schaefer (995 or Dette and Studden (994. Following Dette (997 we call the designs minimizing (. standardized minimax optimal for the parameters {θ i i M}. If M = {,...,m} we riefly talk aout minimax or standardized minimax optimal designs. It is worthwhile to mention that a similar criterion was considered y Müller (995, who determined designs maximizing the minimum of A-efficiencies, where the minimum is taken with respect to the parameter θ. Note that in many cases of practical interest the optimality criteria (. and (. produce locally optimal designs which require prior knowledge of the unknown parameter θ. Some arguments in favour of local optimality criteria can e found in Ford, Torsney and Wu (99. For alternative optimality criteria see Wu (985, Chaloner and Larntz (989, Sitter (99. In Section we provide some theoretical ackground regarding minimax and standardized minimax optimality which is useful for the construction of optimal designs. We also present a procedure for determining minimax and standardized minimax optimal designs. Some guidelines for the construction of minimax optimal designs in inary response models are given in Section 3.. It turns out that for some parameter cominations the minimax optimal designs ecome inefficient in the sense that the numer of different dose levels is less than the numer of parameters in the model. This motivates the consideration of standardized minimax optimal designs for inary response models which are discussed in Section 3.. Finally, in Section 4 the results are illustrated for two inary response models, the doule exponential and the logistic regression model.. Constructing Minimax and Standardized Minimax Optimal Designs The equivalence theory of the minimax optimality criteria can e otained from Pukelsheim (993, page 75. More precisely, assume that {I(θ, x x X} is compact, consider a suset M {,...,m} and a design ξ for which e T j θ is
3 MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS 5 estimale for all j M. Define ρ k =min η e T k I (θ, ηe k (where the minimum is taken over all designs for which e T k θ is estimale and { } N (ξ:= j M e T j I (θ, ξe j =max i M et i I (θ, ξe i { } Ñ (ξ:= j M ρ j e T j I (θ, ξe j =max ρ ie T i i M I (θ, ξe i ; then the design ξ is minimax optimal for the parameters {θ i i M} if and only if there exists a generalized inverse G of I(θ, ξ and nonnegative numers α j,j N(ξ, with sum equal to such that j N (ξ α j e T j GI(θ, xge j e T j I (θ, ξe j, (. for all x X, where equality holds on the support of ξ. Similarly the design ξ is standardized minimax optimal for the parameters {θ i i M}if and only if there exists a generalized inverse G of I(θ, ξ and nonnegative numers α j,j Ñ (ξ, with sum equal to such that j Ñ (ξ α j e T j GI(θ, xge j e T j I (θ, ξe j (. with equality on the support of ξ. The following two Lemmata are ovious from these considerations. Lemma.. A minimax optimal design ξ for the parameters {θ i i M}is also minimax optimal for the parameters {θ i i N(ξ }. A standardized minimax optimal design ξ for the parameters {θ i i M}is also standardized minimax optimal for the parameters {θ i i Ñ ( ξ}. Lemma.. If for all A Mwith #A k<#m the (standardized minimax optimal design for the parameters {θ i i A}is not (standardized minimax optimal for {θ i i M}, then in the generalized inverse of the Fisher information matrix of the (standardized minimax optimal design for {θ i i M}the largest diagonal entry has at least multiplicity k +. Lemma. suggests an iterative procedure for determining minimax optimal designs for the parameters {θ i i M}. For n =,...,#M we proceed as follows.. For all susets M n Mwith #M n = n we calculate the minimax optimal designs ξ n for the parameters {θ i i M n } under the constraint that the corresponding entries in the diagonal of the inverse fisher information matrix
4 5 HOLGER DETTE AND MICHAEL SAHM are all equal and check if ξ n is already minimax optimal for the parameters {θ i i M}. By (. this is equivalent to showing max e T j j M I (θ, ξn e j =max n j M et j I (θ, ξn e j. (.3. If (.3 is satisfied, then ξn is minimax optimal for the parameters {θ i i M} and the procedure stops. Otherwise we put n = n + and repeat step (. Although this procedure appears to e cumersome (ecause in the worst case one has to go #M steps, the results otained in the literature (see Murty (97, Krafft and Schaefer (995, Dette and Studden (994 suggest that in many regression models the algorithm stops after the first or second step. For the standardized minimax optimality criterion the procedure is similar, replacing (.3 y max ρ j e T j j M I (θ, ξn e j =max ρ je T j n j M I (θ, ξn e j. Moreover, the following Lemma shows that for standardized minimax optimality one can always start with n =. Lemma.3. If ξ is a standardized minimax optimal design for the parameters {θ i i M}and #M, then #N (ξ. Proof. Let ξ denote the standardized minimax optimal design and assume #Ñ (ξ =. Without loss of generality we put Ñ (ξ ={}. By Lemma. ξ is also optimal for estimating the parameter θ ;inotherwords =ρ e T I (θ, ξ e =max ρ je T j I (θ, ξ e j min ρ je T j I (θ, ξ e j. j M j M This proves Ñ (ξ =M, contradicting the assumption #M. 3. Minimax and Standardized Minimax Optimal Designs for Binary Response Models In this section we demonstrate the calculation of minimax and standardized minimax optimal designs in inary response models. Consider a inary response variale y with proaility of success p(x, θ, where θ is a two dimensional parameter. We discuss two different parametrizations p (x, θ=f ((x µ θ =(µ,, x IR (3. p (x, θ=f (a + x θ =(a,, x IR (3. that are commonly used in practice. Here F is a known distriution function with symmetric density function f, which is assumed to e differentiale for x 0. Define f (x h(x = F (x( F (x ; (3.3
5 MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS 53 then the Fisher information for the parameter θ of a design ξ is given y ( I (θ, ξ = h((x µ (x µ (x µ (x µ dξ(x (3.4 if the parametrization (3. is used, and y ( x I (θ, ξ = h(a + x x x dξ(x if we use parametrization (3.. Locally optimal designs in these models for various differentiale optimality criteria have een discussed y several authors (see e.g. Wu (988, Adelasit and Placket (983, Sitter and Wu (993, Gaudard, Karson, Lindner and Tse (993. Note that the numer of support points of (locally optimal designs depends sensitively on the form of the function h in (3.3 (see e.g. Sitter and Wu (993. We will now give some general guidelines for the determination of minimax and standardized minimax optimal designs in these models; illustrative examples are presented in the following section. Throughout this paper we will assume that max h(t =h(0 (3.5 t IR and k =max t IR t h(t =c h(c,c >0 (3.6 which is satisfied for most of the commonly used inary response models. Moreover, the point c where the function t h(t attains its maximum is usually unique (up to its sign and can e otained from Tale 4 in Ford, Torsney and Wu ( Minimax optimal designs We concentrate on the parametrization (3.. Standard arguments show that there is a minimax optimal design symmetric with respect to the LD 50. Moreover, y the following lemma it suffices to consider symmetric designs with at most 4 support points. Lemma 3.. In a inary response model with parametrization (3. there exists a minimax optimal design with at most 4 support points which is symmetric to the LD50 µ. The proof is deferred to the appendix. Note, however, that this result does not depend on the inary response model ut holds for any regression model with two paramters having a corresponding symmetry property.
6 54 HOLGER DETTE AND MICHAEL SAHM Following the procedure in Section we first determine the optimal designs ξ µ and ξ for estimating the LD 50 and the slope. These are easily otained from Elfving s theorem (Elfving (95 and assumptions (3.5, (3.6 oserving ρ =inf ξ et I (θ, ξe = h(0 (3.7 ρ =inf ξ et I (θ, ξe = k. (3.8 This gives for ξ µ a one point design at µ and for ξ a two point design ( µ c ξ = µ + c, (3.9 where c>0 is defined y (3.6. Now ξ is minimax optimal if and only if e T I (θ, ξ e e T I (θ, ξ e which is equivalent to c. In the other case the inverse Fisher information matrix must have equal diagonal elements. We start with a two point design where the equality of the diagonal elements readily yields ( ξ0 µ µ+ =. (3.0 / / The minimax optimality of ξ0 can e checked via the inequality (. where the α j (N (ξ0 ={, } have to e chosen (due to equality in (. at the support points of the minimax optimal design and the differentiaility of h as α = α = h ( h(. If inequality (. is not fulfilled we increase the numer of support points successively. Thus in the next step we consider ξ w = ( µ z µ µ+ z w w w, (3. where z>0and h(z(z 4 w = 4 [h(0 h(z] + z h(z due to equality of the diagonal elements in I (θ, ξw. The optimal z minimizes h(0 z h(z z (3.
7 MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS 55 and the weights α,α in the equivalence theorem are given y α = α = [ z 4 z h(z ]. (3.3 4 h (z Note that the expression in (3. is independent of the parameters µ and and the minimizing z is usually also unique. If (. holds for these weights, then ξ w is minimax optimal, otherwise the procedure has to e continued. Thus in the last step we have to consider designs of the form ( µ ξ z = µ z µ + z µ + z w w The equality of the diagonal entries of the Fisher information matrix yields w h(z ( 4 z w = h(z ( 4 z +h(z (z 4. The optimal values for z and z maximize the function w h(z h(z (z z h(z ( 4 z +h(z (z 4 under the constraint that z > >z > 0 (i.e. 0 <w<. Furthermore, the differentiaility of h implies that either h(z =h(z andh (z =h (z =0or. z (h(z h (z = z (h(z h (z and h(z h (z h(z (h(z h(z = z z. z The quantities α and α in the equivalence theorem are given y 4 (h(z h(z α = α = h(z ( 4 z +h(z (z 4. Recent results in the literature suggest that for most inary response models the symmetric optimal designs will have either two or three support points and are given y (3.9, (3.0 or (3., respectively (see Sitter and Wu (993 for some results regarding A-, D- and Fieller-optimality and also for the construction of a model with a symmetric D-optimal design supported at four points. For the parametrization (3. the approach is very similar and illustrated in the second part of Section Standardized minimax optimal designs We demonstrate in Section 4 that minimax optimal desgins for inary response models may ecome inefficient in the sense that the numer of different
8 56 HOLGER DETTE AND MICHAEL SAHM dose levels of the optimal design is less than the numer of parameters in the model. This disadvantage is usually caused y the different size of the elements in the Fisher information matrix. The standardization used in the criterion (. makes elements of different scale etter comparale and does not yield degenerate optimal designs (see Lemma.3. It turns out that in inary response models the standardized minimax optimal designs can e easily otained from the minimax optimal designs for special parameter cominations. More precisely, for the parametrization (3. a minimax optimal design ξ can e otained from ξ (x =η((x µ where η minimizes c c 0 c max{ c,c 0 }, (3.4 c j = y j h(ydη(y (j =0,,. Similarly, the standardized minimax optimal design ξ is given y ξ(x = η((x µ where η minimizes c c 0 c max{h(0c,c 0 k } = h(0k { h(0 c 0 c c max c, k k h(0 c 0 }. (3.5 This follows easily from definitions (., (3.7 and (3.8. Note that the optimization prolem in (3.5 does not depend on the parameters µ and. Moreover, the maximum of (3.5 is otained y maximizing (3.4 for = k/ h(0 which proves the following result. Lemma 3.. Let ξµ, denote the minimax optimal design for a inary response model with parametrization (3. such that (3.5 und (3.6 are satisfied. Then the standardized minimax optimal design ξ for this model is given y ξ(x =ξ 0,s ( s (x µ, where s = k / (h(0 /4 and k is defined in (3.6. For the parametrization (3. a similar result can e estalished. The proof is omitted for the sake of revity. Lemma 3.3. Let ξa, denote the minimax optimal design for a inary response model with parametrization (3. such that S = { ( } h(ω ω IR ω (3.6 is convex and lim ω ω h(ω =0. (3.7
9 MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS 57 The standardized minimax optimal design for this model is given y ( x ξ(x =ξa,s, s where s = mc, c is defined in (3.6 and m = m (a =h(c inf ξ et I (θ, ξe = a c if a >c h(c h(a if a c. (3.8 The conditions (3.6 and (3.7 are satisfied for the commonly used regression models (see Sitter and Wu (993 or Ford, Torsney and Wu (99. It is also worthwile to mention that for oth parametrizations (3. and (3. the standardized minimax optimal designs are scale invariant in the sense that a scaling of the explanatory variale x transfers to the standardized minimax optimal design. The experimenter only has to scale the support points of the standardized minimax optimal design according to the transformation of the x- space. Moreover, y Lemma 3., the same argument applies for translations in inary response models with parametrization (3.. In this case the standardized minimax optimal designs are invariant with respect to linear transformations of the explanatory variale. This is a strong advantage of parametrization (3. over the alternative parametrization (3. in inary response models. 4. Examples In this section we illustrate the result with two examples; a doule exponential model with parametrization (3. and a logistic regression with parametrization (3.. We also demonstrate that the maximum variance criterion can produce inefficient designs for the inary response model in the sense that the numer of different dose levels of the minimax optimal design is less than the numer of parameters in the model. 4.. Doule exponential model Let F (t = +sign(t sign(t e t and consider the parametrization (3.. We calculate h(t =(e t and the values for k and c in (3.6 are easily otained as c.844 k (4. We now apply the general procedure of Section 3.. If c, the optimal design ξ for estimating the slope is also minimax optimal and given y (3.9. In a
10 58 HOLGER DETTE AND MICHAEL SAHM second step we consider the case c and check whether the design in (3.0 is minimax optimal using α = α = h ( /h( in the inequality (.. A straightforward calculation shows that (. is satisfied if and only if v 0 c where v 0 is the unique positive solution of v+e v =,v Finally, if <v 0 the minimax optimal design is given y (3. where z and w are defined y v 0 and w = (v 0 4 h(v 0 h(v 0 (v , (4. respectively (note that h(0 =. This can e shown y the equivalence theorem (. oserving the representation of α and α in (3.3. The minimax optimal designs are listed in Tale 4.. The standardized minimax optimal design are easily otained y Lemma 3.. More precisely we otain, for s = k /,(h(0 /4 = c h(c < v 0, which shows that the standardized minimax optimal design is always supported at three points, i.e. ξ = µ v 0 µ µ+ v 0 w w w where v and w is otained from (4. as, (4.3 w = (v 0 k h(v 0 k + h(v 0 (v0 k This design has equal efficiencies eff (ξ =eff µ (ξ =( w(v0 h(v 0(c h(c for the individual parameters and µ. Tale 4.. Minimax optimal designs ξ for the doule exponential model with parametrization (3. (first row. The second row gives the value α in the equivalence theorem (., while the third and fourth rows contain the efficiencies for estimating the individual parameters µ and. The tale shows the influence of the size of the slope parameter on the minimax optimal design. The weight w is given y (4., the constant c y (4. and v 0 minimizes (3. [c.844,v ]. ( µ v 0 ξ <v 0 v 0 c >c µµ+ v0 w w w α ( µ µ+ ( µ c µ + c ( + h( eff µ h( h(c h( eff c h(c
11 MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS 59 A rief comparison of the minimax and standardized minimax optimal designs in the doule exponential model might e appropriate at this point. The standardized minimax optimal designs are ased on more points than there are parameters in the model. As a consequence tests of goodness-of-fit of the doule exponential model can e performed. Note also that the minimax optimal design for a small slope quickly degenerates to a one point design (see Tale 4.. For example, if = 0., the minimax optimal design advises the experimenter to take 99.9% of the oservations at the LD 50 µ. However, this design does not allow any statistical inference regarding the slope parameter. These oservations can e explained y oserving the different scales of the elements in the Fisher information matrix (3.4. For small values of the slope the dominating element in I (θ, ξ is the element at position (,. Consequently the minimax optimal design is close to the optimal design for estimating the LD 50, which is usually the one point design concentrated at µ. Note that these phenomena contradict the intuition. If the proaility of success is slowly increasing with the dose level, a good design should cover a large range of dose levels. The standardization of the maximum variance criterion in (. produces efficiencies, which are of the same scale and therefore etter comparale than the pure diagonal elements of I (θ, ξ. As a consequence we do not oserve degenerate standardized minimax optimal designs at all. For example, if =0., the standardized minimax optimal design advises the experimenter to take 6.7% of the oservations at µ±5.93 and the remaining 46.6% of the oservations at the LD50 µ (see equation ( Logistic regression As a second example consider the logistic regression model with parametrization (3., i.e. F (t =[+e t ] e t and h(t = ( + e t, t IR and the value c>0maximizing t h(tisgivenyc We first determine the optimal designs for ξ a and ξ for estimating the individual coefficients a and using Elfving s theorem (Elfving (95. This gives inf ξ et I (θ, ξe = e T I (θ, ξ a e = m h(a inf ξ et I (θ, ξe = e T I (θ, ξ e = c h(c, (where m is defined y (3.8 and the optimal designs are given y ( 0 if a c ( a ξ a = ( a c a + c if a >c c a + c a ξ = c a + c.
12 60 HOLGER DETTE AND MICHAEL SAHM From the procedure in Section it follows that ξ is minimax optimal if and only if e T I (θ, ξ e e T I (θ, ξ e which is equivalent to a + c. Similarly we otain ξ a is minimax optimal a c. In all other cases ( a <c we must have equality of the diagonal elements of the inverse Fisher information matrix of the minimax optimal design. A reasonale guess for a design at two points is ( a ξ = v a + v w w v>0,w (0, (4.4 which implies for the weight if a =0 w = + v +(a 4av if a 0. (4.5 The point v>0can e found y maximizing the function h(xg(x forx>0, where g(x =x (a + x 4 (a, (4.6 and the optimality of the design in (3.0 is estalished using the equivalence theorem (.. The resulting cases are listed in Tale 4. which also contains the corresponding quantities α j, used for checking the minimax optimality y (.. Note that the tale does not include the case a =0, <c. In this case the minimax optimal design has equal masses at the points and which also follows from (. using α =. Tale 4.. Locally minimax optimal designs for the logistic regression model with parametrization (3.. The tale shows the dependence of the locally optimal design on the size of the quantity a. Here g(x isdefined y (4.6, the constant c =argmax{x h(x} y (3.6, v = arg max{h(xg(x} and w y (4.5. minimax opt. design a c a <c c a ( c a c a α 0 Φ(ξ c h(c ( v a v a w w 4 (a v g(v 4a h(vg(v ( c a a c a c a a+c a a c h(c
13 MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS 6 Finally, the standardized minimax optimal designs for the logistic regression with parametrization (3. are otained from Lemma 3.3 and given y ( a ξ = ṽ a + ṽ w w (4.7 ṽ =argmax{h(x[x (a + s x 4 (a s ]; x>0} (4.8 if a =0 w = (4.9 + ṽ +(a s 4aṽ if a 0, where s = s(a is defined in Lemma 3.3. Note that the standardized minimax optimal design is scale invariant, ecause ṽ and w defined in (4.8 and (4.9 do not depend on the slope parameter. In Tales 4.3 and 4.4 the minimax and standardized minimax optimal designs for the logistic regression model with parametrization (3. are calculated for some specific values of a and as well as their efficiencies with respect to the optimal designs for the individual parameters a and. We oserve again that for many parameter cominations the minimax optimal designs are inefficient for estimating the slope, especially for small values of the slope parameter. The standardized minimax optimal designs are equally efficient for estimating the individual parameters. This efficiency does not depend on the parameter and is increasing with the intercept a. Tale 4.3. Locally minimax optimal designs ξ for logistic regression with parametrization (3. for various values of a and. The designs are otained from (4.4-(4.6 and put masses w and w at the points a/ v/ and a/+v/. The last two columns give the efficiencies of the minimax optimal design for estimating the individual parameters. The tale illustrates how sensitively the locally minimax optimal design depends on the slope parameter of the inary response model. In particular it shows that for small values of the slope, the minimax optimal design ecomes inefficient for estimating the slope parameter. a v w Φ(ξ eff a (ξ eff (ξ c c c c c c c
14 6 HOLGER DETTE AND MICHAEL SAHM Tale 4.4. Locally standardized minimax optimal designs for the logistic regression model with parametrization (3. for various values of a and aritrary. The last column gives the efficiencies of the minimax optimal design for estimating the individual parameters. The designs are otained from (4.7-(4.9 and put masses w and w at the points a/ ṽ/ and a/ + ṽ/. The locally standardized minimax optimal designs and its efficiencies are invariant with respect to the slope parameter. Thus the tale illustrates the impact of the intercept on the locally standardized minimax optimal designs. Note that these designs never ecome inefficient for estimating the individual parameters. a ṽ w Ψ( ξ eff a ( ξ =eff ( ξ c Conclusions In this paper we considered the prolem of constructing minimax optimal designs for inary response models which minimize the asymptotic maximum variance of the estimates for the individual parameters. Some general guidelines for the determination of these designs are given and illustrated in the doule exponential and logistic regression model. It turns out that minimax optimal designs may ecome inefficient in the sense, that the numer of their dose levels is less than the numer of parameters in the model. For this reason a standardized version (standardized minimax optimality of Elfving s partial minimax criterion is also considered, which is ased on the variances of the estimators for the individual coefficients relative to the est variances otainale y the choice of an experimental design, i.e. efficiencies of the estimates are considered. The standardized minimax optimal designs for inary response models can e otained from the minimax optimal designs and produce equal efficiencies for estimating the individual parameters in the model. In some cases the standardized minimax optimal design has more support points than the minimax optimal design and a goodness-of-fit test of the inary response model can e performed. Because the standardized minimax optimal designs are scale invariant and ecause they cannot degenerate to one point designs, we would recommend their application instead of minimax optimal designs. Acknowledgement The authors are grateful to an unknown Associate Editor and referee for their valuale comments and suggestions which led to a sustantial improvement of the original manuscript.
15 MINIMAX OPTIMAL DESIGNS IN NONLINEAR REGRESSION MODELS 63 Appendix Proof of Lemma 3.. Let z,...,z k e aritrary nonnegative numers. We show that there is a symmetric minimax optimal design with at most four support points in the class of all symmetric designs with support in the set {µ ± z i / i =,...,k}. Therefore, let ξ e a symmetric measure on {µ ± z i / i =,...,k} with weights w i /atthepointsµ ± z i /. Due to the symmetry of ξ the Fisher information matrix is diagonal, i.e. k k I(θ, ξ =diag ( w i h(z i, w i zi h(z i. i= Optimizing with respect to the criterion (. is therefore equivalent to maximizing k k } min { w i h(z i, w i zi h(z i i= over all possile weights, that is maximizing on the k-simplex { k } w,...,w k w i 0, w i =. i= Since oth functions are linear in the weights the Lemma follows from a general result from linear programming. More precisely, the maximum value of the minimum of two linear functions on the k-simplex is always attained at a convex comination of two extreme points. The extreme points of the k-simplex represent the symmetric two point designs with weight / atthepointsµ ± z i0 /, i 0 k. That means there is always a minimax optimal design in the considered class with at most 4 support points. References Adelasit, K. M. and Plackett, R. L. (983. Experimental design for inary data. J. Amer. Statist. Assoc. 78, Atkinson, A. C. and Donev, A. N. (99. Optimum Experimental Designs. Oxford: Oxford University Press. Chaloner, K. and Larntz, K. (989. Optimal Bayesian design applied to logistic regression experiments. J. Statist. Plann. Inference, Dette, H. (997. Designing experiments with respect to standardized optimality criteria. J. Roy. Statist. Soc. Ser. B 59, Dette, H. and Studden, W. J. (994. Optimal designs with respect to Elfving s partial minimax criterion in polynomial regression. Ann. Inst. Statist. Math. 46, Elfving, G. (95. Optimum allocation in linear regression theory. Ann. Math. Statist. 3, Elfving, G. (959. Design of linear experiments. In Proaility and Statistics. The Harald Cramér Volume (Edited y U. Grenander, Stockholm: Almagrist and Wiksell. i= i=
16 64 HOLGER DETTE AND MICHAEL SAHM Ford, I., Torsney, B. and Wu, C. F. J. (99. The use of a canonical form in the construction of locally optimal designs for non-linear prolems. J. Roy. Statist. Soc. Ser. B 54, Gaudard, M. A., Karson, M. J., Linder, E. and Tse, S. K. (993. Efficient designs for estimation in the power logistic quantal response model. Statist. Sinica 3, Krafft, O. and Schaefer, M. (995. Exact Elfving-minimax designs for quadratic regression. Statist. Sinica 5, Müller, C. (995. Maximin efficient designs for estimating nonlinear aspects in linear models. J. Statist. Plann. Inference 44, 7-3. Murty, V. N. (97. Minimax designs. J. Amer. Statist. Assoc. 66, Pukelsheim, F. (993. Optimal Experimental Design. New York: Wiley. Silvey, S. D. (980. Optimal Design: An Introduction to the Theory for Parameter Estimation. Chapman and Hall, London. Sitter, R. R. (99. Roust design for inary data. Biometrics 48, Sitter, R. R. and Wu, C. F. J. (993. Optimal designs for inary response experiments: Fieller-, D- and A-criteria. Scand. J. Statist. 0, Torsney, B. and López-Fidalgo, J. (995. MV-optimization in simple linar regression. In MODA4-Advances in Model-Oriented Data Analysis (Edited y C. P. Kitsos, W.G. Müller, Wu, C. F. J. (985. Efficient sequential designs with inary data. J. Amer. Statist. Assoc. 80, Wu, C. F. J. (988. Optimal design for percentile estimation of a quantal response curve. In Optimal Design and Analysis of Experiments (EditedyJ.Dodge,V.V.Fedorov,H.P. Wynn, North Holland, Amsterdam. Ruhr-Universität Bochum, Fakultät für Mathematik, Universitätsstr. 50, D Bochum, Germany. holger.dette@rz.ruhr-uni-ochum.de michael.sahm@ruhr-uni-ochum.de (Received May 997; accepted Octoer 997
460 HOLGER DETTE AND WILLIAM J STUDDEN order to examine how a given design behaves in the model g` with respect to the D-optimality criterion one uses
Statistica Sinica 5(1995), 459-473 OPTIMAL DESIGNS FOR POLYNOMIAL REGRESSION WHEN THE DEGREE IS NOT KNOWN Holger Dette and William J Studden Technische Universitat Dresden and Purdue University Abstract:
More informationOptimal designs for estimating the slope of a regression
Optimal designs for estimating the slope of a regression Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 4480 Bochum, Germany e-mail: holger.dette@rub.de Viatcheslav B. Melas St. Petersburg
More informationA-optimal designs for generalized linear model with two parameters
A-optimal designs for generalized linear model with two parameters Min Yang * University of Missouri - Columbia Abstract An algebraic method for constructing A-optimal designs for two parameter generalized
More informationOPTIMAL DESIGNS FOR GENERALIZED LINEAR MODELS WITH MULTIPLE DESIGN VARIABLES
Statistica Sinica 21 (2011, 1415-1430 OPTIMAL DESIGNS FOR GENERALIZED LINEAR MODELS WITH MULTIPLE DESIGN VARIABLES Min Yang, Bin Zhang and Shuguang Huang University of Missouri, University of Alabama-Birmingham
More informationCompound Optimal Designs for Percentile Estimation in Dose-Response Models with Restricted Design Intervals
Compound Optimal Designs for Percentile Estimation in Dose-Response Models with Restricted Design Intervals Stefanie Biedermann 1, Holger Dette 1, Wei Zhu 2 Abstract In dose-response studies, the dose
More informationEXACT OPTIMAL DESIGNS FOR WEIGHTED LEAST SQUARES ANALYSIS WITH CORRELATED ERRORS
Statistica Sinica 18(2008), 135-154 EXACT OPTIMAL DESIGNS FOR WEIGHTED LEAST SQUARES ANALYSIS WITH CORRELATED ERRORS Holger Dette, Joachim Kunert and Andrey Pepelyshev Ruhr-Universität Bochum, Universität
More informationMAXIMIN AND BAYESIAN OPTIMAL DESIGNS FOR REGRESSION MODELS
Statistica Sinica 17(2007), 463-480 MAXIMIN AND BAYESIAN OPTIMAL DESIGNS FOR REGRESSION MODELS Holger Dette, Linda M. Haines and Lorens A. Imhof Ruhr-Universität, University of Cape Town and Bonn University
More informationOptimal discrimination designs
Optimal discrimination designs Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@ruhr-uni-bochum.de Stefanie Titoff Ruhr-Universität Bochum Fakultät
More informationA geometric characterization of c-optimal designs for heteroscedastic regression
A geometric characterization of c-optimal designs for heteroscedastic regression Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@rub.de Tim Holland-Letz
More informationOptimal designs for estimating the slope in nonlinear regression
Optimal designs for estimating the slope in nonlinear regression Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@rub.de Viatcheslav B. Melas St.
More informationOPTIMAL DESIGN OF EXPERIMENTS WITH POSSIBLY FAILING TRIALS
Statistica Sinica 12(2002), 1145-1155 OPTIMAL DESIGN OF EXPERIMENTS WITH POSSIBLY FAILING TRIALS Lorens A. Imhof, Dale Song and Weng Kee Wong RWTH Aachen, Scirex Corporation and University of California
More informationOptimal designs for rational regression models
Optimal designs for rational regression models Holger Dette, Christine Kiss Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany holger.dette@ruhr-uni-bochum.de tina.kiss12@googlemail.com
More informationExact optimal designs for weighted least squares analysis with correlated errors
Exact optimal designs for weighted least squares analysis with correlated errors Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@rub.de Joachim Kunert
More informationON THE COMPARISON OF BOUNDARY AND INTERIOR SUPPORT POINTS OF A RESPONSE SURFACE UNDER OPTIMALITY CRITERIA. Cross River State, Nigeria
ON THE COMPARISON OF BOUNDARY AND INTERIOR SUPPORT POINTS OF A RESPONSE SURFACE UNDER OPTIMALITY CRITERIA Thomas Adidaume Uge and Stephen Seastian Akpan, Department Of Mathematics/Statistics And Computer
More informationEfficient computation of Bayesian optimal discriminating designs
Efficient computation of Bayesian optimal discriminating designs Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@rub.de Roman Guchenko, Viatcheslav
More informationOptimum designs for model. discrimination and estimation. in Binary Response Models
Optimum designs for model discrimination and estimation in Binary Response Models by Wei-Shan Hsieh Advisor Mong-Na Lo Huang Department of Applied Mathematics National Sun Yat-sen University Kaohsiung,
More informationD-optimal designs for logistic regression in two variables
D-optimal designs for logistic regression in two variables Linda M. Haines 1, M. Gaëtan Kabera 2, P. Ndlovu 2 and T. E. O Brien 3 1 Department of Statistical Sciences, University of Cape Town, Rondebosch
More informationMULTIPLE-OBJECTIVE DESIGNS IN A DOSE-RESPONSE EXPERIMENT
New Developments and Applications in Experimental Design IMS Lecture Notes - Monograph Series (1998) Volume 34 MULTIPLE-OBJECTIVE DESIGNS IN A DOSE-RESPONSE EXPERIMENT BY WEI ZHU AND WENG KEE WONG 1 State
More informationOptimal and efficient designs for Gompertz regression models
Ann Inst Stat Math (2012) 64:945 957 DOI 10.1007/s10463-011-0340-y Optimal and efficient designs for Gompertz regression models Gang Li Received: 13 July 2010 / Revised: 11 August 2011 / Published online:
More informationEfficient algorithms for calculating optimal designs in pharmacokinetics and dose finding studies
Efficient algorithms for calculating optimal designs in pharmacokinetics and dose finding studies Tim Holland-Letz Ruhr-Universität Bochum Medizinische Fakultät 44780 Bochum, Germany email: tim.holland-letz@rub.de
More informationBy Min Yang 1 and John Stufken 2 University of Missouri Columbia and University of Georgia
The Annals of Statistics 2009, Vol. 37, No. 1, 518 541 DOI: 10.1214/07-AOS560 c Institute of Mathematical Statistics, 2009 SUPPORT POINTS OF LOCALLY OPTIMAL DESIGNS FOR NONLINEAR MODELS WITH TWO PARAMETERS
More informationOPTIMAL TWO-STAGE DESIGNS FOR BINARY RESPONSE EXPERIMENTS
Statistica Sinica 7(1997), 941-955 OPTIMAL TWO-STAGE DESIGNS FOR BINARY RESPONSE EXPERIMENTS R. R. Sitter and B. E. Forbes Simon Fraser University and Carleton University Abstract: In typical binary response
More informationOptimum Designs for the Equality of Parameters in Enzyme Inhibition Kinetic Models
Optimum Designs for the Equality of Parameters in Enzyme Inhibition Kinetic Models Anthony C. Atkinson, Department of Statistics, London School of Economics, London WC2A 2AE, UK and Barbara Bogacka, School
More informationStochastic Design Criteria in Linear Models
AUSTRIAN JOURNAL OF STATISTICS Volume 34 (2005), Number 2, 211 223 Stochastic Design Criteria in Linear Models Alexander Zaigraev N. Copernicus University, Toruń, Poland Abstract: Within the framework
More informationAP-Optimum Designs for Minimizing the Average Variance and Probability-Based Optimality
AP-Optimum Designs for Minimizing the Average Variance and Probability-Based Optimality Authors: N. M. Kilany Faculty of Science, Menoufia University Menoufia, Egypt. (neveenkilany@hotmail.com) W. A. Hassanein
More informationOptimal designs for multi-response generalized linear models with applications in thermal spraying
Optimal designs for multi-response generalized linear models with applications in thermal spraying Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum Germany email: holger.dette@ruhr-uni-bochum.de
More informationLocal&Bayesianoptimaldesigns in binary bioassay
Local&Bayesianoptimaldesigns in binary bioassay D.M.Smith Office of Biostatistics & Bioinformatics Medicial College of Georgia. New Directions in Experimental Design (DAE 2003 Chicago) 1 Review of binary
More informationEVALUATIONS OF EXPECTED GENERALIZED ORDER STATISTICS IN VARIOUS SCALE UNITS
APPLICATIONES MATHEMATICAE 9,3 (), pp. 85 95 Erhard Cramer (Oldenurg) Udo Kamps (Oldenurg) Tomasz Rychlik (Toruń) EVALUATIONS OF EXPECTED GENERALIZED ORDER STATISTICS IN VARIOUS SCALE UNITS Astract. We
More informationDiscussion Paper SFB 823. Nearly universally optimal designs for models with correlated observations
SFB 823 Nearly universally optimal designs for models with correlated observations Discussion Paper Holger Dette, Andrey Pepelyshev, Anatoly Zhigljavsky Nr. 3/212 Nearly universally optimal designs for
More informationON THE NUMBER OF SUPPORT POINTS OF MAXIMIN AND BAYESIAN OPTIMAL DESIGNS 1. BY DIETRICH BRAESS AND HOLGER DETTE Ruhr-Universität Bochum
The Annals of Statistics 2007, Vol. 35, No. 2, 772 792 DOI: 10.1214/009053606000001307 Institute of Mathematical Statistics, 2007 ON THE NUMBER OF SUPPORT POINTS OF MAXIMIN AND BAYESIAN OPTIMAL DESIGNS
More informationLuis Manuel Santana Gallego 100 Investigation and simulation of the clock skew in modern integrated circuits. Clock Skew Model
Luis Manuel Santana Gallego 100 Appendix 3 Clock Skew Model Xiaohong Jiang and Susumu Horiguchi [JIA-01] 1. Introduction The evolution of VLSI chips toward larger die sizes and faster clock speeds makes
More informationUpper Bounds for Stern s Diatomic Sequence and Related Sequences
Upper Bounds for Stern s Diatomic Sequence and Related Sequences Colin Defant Department of Mathematics University of Florida, U.S.A. cdefant@ufl.edu Sumitted: Jun 18, 01; Accepted: Oct, 016; Pulished:
More informationMulti- and Hyperspectral Remote Sensing Change Detection with Generalized Difference Images by the IR-MAD Method
Multi- and Hyperspectral Remote Sensing Change Detection with Generalized Difference Images y the IR-MAD Method Allan A. Nielsen Technical University of Denmark Informatics and Mathematical Modelling DK-2800
More informationOn construction of constrained optimum designs
On construction of constrained optimum designs Institute of Control and Computation Engineering University of Zielona Góra, Poland DEMA2008, Cambridge, 15 August 2008 Numerical algorithms to construct
More informationInformation in a Two-Stage Adaptive Optimal Design
Information in a Two-Stage Adaptive Optimal Design Department of Statistics, University of Missouri Designed Experiments: Recent Advances in Methods and Applications DEMA 2011 Isaac Newton Institute for
More informationLocally optimal designs for errors-in-variables models
Locally optimal designs for errors-in-variables models Maria Konstantinou, Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany maria.konstantinou@ruhr-uni-bochum.de holger.dette@ruhr-uni-bochum.de
More informationON D-OPTIMAL DESIGNS FOR ESTIMATING SLOPE
Sankhyā : The Indian Journal of Statistics 999, Volume 6, Series B, Pt. 3, pp. 488 495 ON D-OPTIMAL DESIGNS FOR ESTIMATING SLOPE By S. HUDA and A.A. AL-SHIHA King Saud University, Riyadh, Saudi Arabia
More informationIN this paper, we consider the estimation of the frequency
Iterative Frequency Estimation y Interpolation on Fourier Coefficients Elias Aoutanios, MIEEE, Bernard Mulgrew, MIEEE Astract The estimation of the frequency of a complex exponential is a prolem that is
More informationIsolated Toughness and Existence of [a, b]-factors in Graphs
Isolated Toughness and Existence of [a, ]-factors in Graphs Yinghong Ma 1 and Qinglin Yu 23 1 Department of Computing Science Shandong Normal University, Jinan, Shandong, China 2 Center for Cominatorics,
More informationA new approach to optimal designs for models with correlated observations
A new approach to optimal designs for models with correlated observations Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@rub.de Andrey Pepelyshev
More informationD-Optimal Designs for Second-Order Response Surface Models with Qualitative Factors
Journal of Data Science 920), 39-53 D-Optimal Designs for Second-Order Response Surface Models with Qualitative Factors Chuan-Pin Lee and Mong-Na Lo Huang National Sun Yat-sen University Abstract: Central
More informationESTIMATION OF NONLINEAR BERKSON-TYPE MEASUREMENT ERROR MODELS
Statistica Sinica 13(2003), 1201-1210 ESTIMATION OF NONLINEAR BERKSON-TYPE MEASUREMENT ERROR MODELS Liqun Wang University of Manitoba Abstract: This paper studies a minimum distance moment estimator for
More informationEfficient Robbins-Monro Procedure for Binary Data
Efficient Robbins-Monro Procedure for Binary Data V. Roshan Joseph School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332-0205, USA roshan@isye.gatech.edu SUMMARY
More information1 Systems of Differential Equations
March, 20 7- Systems of Differential Equations Let U e an open suset of R n, I e an open interval in R and : I R n R n e a function from I R n to R n The equation ẋ = ft, x is called a first order ordinary
More informationDesigns for Generalized Linear Models
Designs for Generalized Linear Models Anthony C. Atkinson David C. Woods London School of Economics and Political Science, UK University of Southampton, UK December 9, 2013 Email: a.c.atkinson@lse.ac.uk
More information1 Hoeffding s Inequality
Proailistic Method: Hoeffding s Inequality and Differential Privacy Lecturer: Huert Chan Date: 27 May 22 Hoeffding s Inequality. Approximate Counting y Random Sampling Suppose there is a ag containing
More informationA converse Gaussian Poincare-type inequality for convex functions
Statistics & Proaility Letters 44 999 28 290 www.elsevier.nl/locate/stapro A converse Gaussian Poincare-type inequality for convex functions S.G. Bokov a;, C. Houdre ; ;2 a Department of Mathematics, Syktyvkar
More informationNonparametric Estimation of Smooth Conditional Distributions
Nonparametric Estimation of Smooth Conditional Distriutions Bruce E. Hansen University of Wisconsin www.ssc.wisc.edu/~hansen May 24 Astract This paper considers nonparametric estimation of smooth conditional
More information#A50 INTEGERS 14 (2014) ON RATS SEQUENCES IN GENERAL BASES
#A50 INTEGERS 14 (014) ON RATS SEQUENCES IN GENERAL BASES Johann Thiel Dept. of Mathematics, New York City College of Technology, Brooklyn, New York jthiel@citytech.cuny.edu Received: 6/11/13, Revised:
More informationON STATISTICAL INFERENCE UNDER ASYMMETRIC LOSS. Abstract. We introduce a wide class of asymmetric loss functions and show how to obtain
ON STATISTICAL INFERENCE UNDER ASYMMETRIC LOSS FUNCTIONS Michael Baron Received: Abstract We introduce a wide class of asymmetric loss functions and show how to obtain asymmetric-type optimal decision
More informationSTRONG NORMALITY AND GENERALIZED COPELAND ERDŐS NUMBERS
#A INTEGERS 6 (206) STRONG NORMALITY AND GENERALIZED COPELAND ERDŐS NUMBERS Elliot Catt School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, New South Wales, Australia
More informationD-optimal Designs for Factorial Experiments under Generalized Linear Models
D-optimal Designs for Factorial Experiments under Generalized Linear Models Jie Yang Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago Joint research with Abhyuday
More informationMinimax design criterion for fractional factorial designs
Ann Inst Stat Math 205 67:673 685 DOI 0.007/s0463-04-0470-0 Minimax design criterion for fractional factorial designs Yue Yin Julie Zhou Received: 2 November 203 / Revised: 5 March 204 / Published online:
More informationFinQuiz Notes
Reading 9 A time series is any series of data that varies over time e.g. the quarterly sales for a company during the past five years or daily returns of a security. When assumptions of the regression
More informationMinimizing a convex separable exponential function subject to linear equality constraint and bounded variables
Minimizing a convex separale exponential function suect to linear equality constraint and ounded variales Stefan M. Stefanov Department of Mathematics Neofit Rilski South-Western University 2700 Blagoevgrad
More informationFixed-b Inference for Testing Structural Change in a Time Series Regression
Fixed- Inference for esting Structural Change in a ime Series Regression Cheol-Keun Cho Michigan State University imothy J. Vogelsang Michigan State University August 29, 24 Astract his paper addresses
More informationOne-step ahead adaptive D-optimal design on a finite design. space is asymptotically optimal
Author manuscript, published in "Metrika (2009) 20" DOI : 10.1007/s00184-008-0227-y One-step ahead adaptive D-optimal design on a finite design space is asymptotically optimal Luc Pronzato Laboratoire
More informationRepresentation theory of SU(2), density operators, purification Michael Walter, University of Amsterdam
Symmetry and Quantum Information Feruary 6, 018 Representation theory of S(), density operators, purification Lecture 7 Michael Walter, niversity of Amsterdam Last week, we learned the asic concepts of
More informationE-optimal approximate block designs for treatment-control comparisons
E-optimal approximate block designs for treatment-control comparisons Samuel Rosa 1 1 Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia We study E-optimal block
More informationOptimal designs for the Michaelis Menten model with correlated observations
Optimal designs for the Michaelis Menten model with correlated observations Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@rub.de Joachim Kunert
More informationOptimal Experimental Designs for the Poisson Regression Model in Toxicity Studies
Optimal Experimental Designs for the Poisson Regression Model in Toxicity Studies Yanping Wang Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial
More informationCharacterization of c-, L- and φ k -optimal designs for a class of non-linear multiple-regression models
J. R. Statist. Soc. B (2019) 81, Part 1, pp. 101 120 Characterization of c-, L- and φ k -optimal designs for a class of non-linear multiple-regression models Dennis Schmidt Otto-von-Guericke-Universität
More informationTIGHT BOUNDS FOR THE FIRST ORDER MARCUM Q-FUNCTION
TIGHT BOUNDS FOR THE FIRST ORDER MARCUM Q-FUNCTION Jiangping Wang and Dapeng Wu Department of Electrical and Computer Engineering University of Florida, Gainesville, FL 3611 Correspondence author: Prof.
More informationCOHERENT DISPERSION CRITERIA FOR OPTIMAL EXPERIMENTAL DESIGN
The Annals of Statistics 1999, Vol. 27, No. 1, 65 81 COHERENT DISPERSION CRITERIA FOR OPTIMAL EXPERIMENTAL DESIGN By A. Philip Dawid and Paola Sebastiani 1 University College London and The Open University
More informationThe Mean Version One way to write the One True Regression Line is: Equation 1 - The One True Line
Chapter 27: Inferences for Regression And so, there is one more thing which might vary one more thing aout which we might want to make some inference: the slope of the least squares regression line. The
More informationTwo-Stage Improved Group Plans for Burr Type XII Distributions
American Journal of Mathematics and Statistics 212, 2(3): 33-39 DOI: 1.5923/j.ajms.21223.4 Two-Stage Improved Group Plans for Burr Type XII Distriutions Muhammad Aslam 1,*, Y. L. Lio 2, Muhammad Azam 1,
More informationChaos and Dynamical Systems
Chaos and Dynamical Systems y Megan Richards Astract: In this paper, we will discuss the notion of chaos. We will start y introducing certain mathematical concepts needed in the understanding of chaos,
More informationAN ASYMPTOTIC THEORY OF SEQUENTIAL DESIGNS BASED ON MAXIMUM LIKELIHOOD RECURSIONS
Statistica Sinica 7(1997), 75-91 AN ASYMPTOTIC THEORY OF SEQUENTIAL DESIGNS BASED ON MAXIMUM LIKELIHOOD RECURSIONS Zhiliang Ying and C. F. Jeff Wu Rutgers University and University of Michigan Abstract:
More informationA New Robust Design Strategy for Sigmoidal Models Based on Model Nesting
A New Robust Design Strategy for Sigmoidal Models Based on Model Nesting Timothy E. O'Brien Department of Statistics, Washington State University 1 Introduction For a given process, researchers often have
More informationNon-Linear Regression Samuel L. Baker
NON-LINEAR REGRESSION 1 Non-Linear Regression 2006-2008 Samuel L. Baker The linear least squares method that you have een using fits a straight line or a flat plane to a unch of data points. Sometimes
More informationOn the efficiency of two-stage adaptive designs
On the efficiency of two-stage adaptive designs Björn Bornkamp (Novartis Pharma AG) Based on: Dette, H., Bornkamp, B. and Bretz F. (2010): On the efficiency of adaptive designs www.statistik.tu-dortmund.de/sfb823-dp2010.html
More informationOptimal Design for Nonlinear and Spatial Models: Introduction and Historical Overview
12 Optimal Design for Nonlinear and Spatial Models: Introduction and Historical Overview Douglas P. Wiens CONTENTS 12.1 Introduction...457 12.2 Generalized Linear Models...457 12.3 Selected Nonlinear Models...460
More informationOn Consistency of Estimators in Simple Linear Regression 1
On Consistency of Estimators in Simple Linear Regression 1 Anindya ROY and Thomas I. SEIDMAN Department of Mathematics and Statistics University of Maryland Baltimore, MD 21250 (anindya@math.umbc.edu)
More informationEssential Maths 1. Macquarie University MAFC_Essential_Maths Page 1 of These notes were prepared by Anne Cooper and Catriona March.
Essential Maths 1 The information in this document is the minimum assumed knowledge for students undertaking the Macquarie University Masters of Applied Finance, Graduate Diploma of Applied Finance, and
More informationLog-mean linear regression models for binary responses with an application to multimorbidity
Log-mean linear regression models for inary responses with an application to multimoridity arxiv:1410.0580v3 [stat.me] 16 May 2016 Monia Lupparelli and Alerto Roverato March 30, 2016 Astract In regression
More informationA good permutation for one-dimensional diaphony
Monte Carlo Methods Appl. 16 (2010), 307 322 DOI 10.1515/MCMA.2010.015 de Gruyter 2010 A good permutation for one-dimensional diaphony Florian Pausinger and Wolfgang Ch. Schmid Astract. In this article
More informationarxiv: v1 [math.st] 14 Aug 2007
The Annals of Statistics 2007, Vol. 35, No. 2, 772 792 DOI: 10.1214/009053606000001307 c Institute of Mathematical Statistics, 2007 arxiv:0708.1901v1 [math.st] 14 Aug 2007 ON THE NUMBER OF SUPPORT POINTS
More informationHIGH-DIMENSIONAL GRAPHS AND VARIABLE SELECTION WITH THE LASSO
The Annals of Statistics 2006, Vol. 34, No. 3, 1436 1462 DOI: 10.1214/009053606000000281 Institute of Mathematical Statistics, 2006 HIGH-DIMENSIONAL GRAPHS AND VARIABLE SELECTION WITH THE LASSO BY NICOLAI
More informationCPM: A Covariance-preserving Projection Method
CPM: A Covariance-preserving Projection Method Jieping Ye Tao Xiong Ravi Janardan Astract Dimension reduction is critical in many areas of data mining and machine learning. In this paper, a Covariance-preserving
More informationSupplement to: Guidelines for constructing a confidence interval for the intra-class correlation coefficient (ICC)
Supplement to: Guidelines for constructing a confidence interval for the intra-class correlation coefficient (ICC) Authors: Alexei C. Ionan, Mei-Yin C. Polley, Lisa M. McShane, Kevin K. Doin Section Page
More informationAbstract. For the Briot-Bouquet differential equations of the form given in [1] zu (z) u(z) = h(z),
J. Korean Math. Soc. 43 (2006), No. 2, pp. 311 322 STRONG DIFFERENTIAL SUBORDINATION AND APPLICATIONS TO UNIVALENCY CONDITIONS José A. Antonino Astract. For the Briot-Bouquet differential equations of
More informationA new algorithm for deriving optimal designs
A new algorithm for deriving optimal designs Stefanie Biedermann, University of Southampton, UK Joint work with Min Yang, University of Illinois at Chicago 18 October 2012, DAE, University of Georgia,
More informationSharp estimates of bounded solutions to some semilinear second order dissipative equations
Sharp estimates of ounded solutions to some semilinear second order dissipative equations Cyrine Fitouri & Alain Haraux Astract. Let H, V e two real Hilert spaces such that V H with continuous and dense
More informationDepth versus Breadth in Convolutional Polar Codes
Depth versus Breadth in Convolutional Polar Codes Maxime Tremlay, Benjamin Bourassa and David Poulin,2 Département de physique & Institut quantique, Université de Sherrooke, Sherrooke, Quéec, Canada JK
More informationMath 216 Second Midterm 28 March, 2013
Math 26 Second Midterm 28 March, 23 This sample exam is provided to serve as one component of your studying for this exam in this course. Please note that it is not guaranteed to cover the material that
More informationOn the computation of Hermite-Humbert constants for real quadratic number fields
Journal de Théorie des Nombres de Bordeaux 00 XXXX 000 000 On the computation of Hermite-Humbert constants for real quadratic number fields par Marcus WAGNER et Michael E POHST Abstract We present algorithms
More informationCONSTRUCTION OF SLICED ORTHOGONAL LATIN HYPERCUBE DESIGNS
Statistica Sinica 23 (2013), 1117-1130 doi:http://dx.doi.org/10.5705/ss.2012.037 CONSTRUCTION OF SLICED ORTHOGONAL LATIN HYPERCUBE DESIGNS Jian-Feng Yang, C. Devon Lin, Peter Z. G. Qian and Dennis K. J.
More informationChi-square lower bounds
IMS Collections Borrowing Strength: Theory Powering Applications A Festschrift for Lawrence D. Brown Vol. 6 (2010) 22 31 c Institute of Mathematical Statistics, 2010 DOI: 10.1214/10-IMSCOLL602 Chi-square
More informationOptimal designs for the EMAX, log-linear and exponential model
Optimal designs for the EMAX, log-linear and exponential model Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum, Germany e-mail: holger.dette@ruhr-uni-bochum.de Mirjana Bevanda
More informationDiscussion Paper SFB 823. Optimal designs for the EMAX, log-linear linear and exponential model
SFB 823 Optimal designs for the EMAX, log-linear linear and exponential model Discussion Paper Holger Dette, Christine Kiss, Mirjana Bevanda, Frank Bretz Nr. 5/2009 Optimal designs for the EMAX, log-linear
More informationD-optimal Designs for Multinomial Logistic Models
D-optimal Designs for Multinomial Logistic Models Jie Yang University of Illinois at Chicago Joint with Xianwei Bu and Dibyen Majumdar October 12, 2017 1 Multinomial Logistic Models Cumulative logit model:
More informationBayesian Nonparametric Point Estimation Under a Conjugate Prior
University of Pennsylvania ScholarlyCommons Statistics Papers Wharton Faculty Research 5-15-2002 Bayesian Nonparametric Point Estimation Under a Conjugate Prior Xuefeng Li University of Pennsylvania Linda
More informationFIDUCIAL INFERENCE: AN APPROACH BASED ON BOOTSTRAP TECHNIQUES
U.P.B. Sci. Bull., Series A, Vol. 69, No. 1, 2007 ISSN 1223-7027 FIDUCIAL INFERENCE: AN APPROACH BASED ON BOOTSTRAP TECHNIQUES H.-D. HEIE 1, C-tin TÂRCOLEA 2, Adina I. TARCOLEA 3, M. DEMETRESCU 4 În prima
More informationOn Worley s theorem in Diophantine approximations
Annales Mathematicae et Informaticae 35 (008) pp. 61 73 http://www.etf.hu/ami On Worley s theorem in Diophantine approximations Andrej Dujella a, Bernadin Irahimpašić a Department of Mathematics, University
More informationPARAMETER CONVERGENCE FOR EM AND MM ALGORITHMS
Statistica Sinica 15(2005), 831-840 PARAMETER CONVERGENCE FOR EM AND MM ALGORITHMS Florin Vaida University of California at San Diego Abstract: It is well known that the likelihood sequence of the EM algorithm
More informationProbabilistic Modelling and Reasoning: Assignment Modelling the skills of Go players. Instructor: Dr. Amos Storkey Teaching Assistant: Matt Graham
Mechanics Proailistic Modelling and Reasoning: Assignment Modelling the skills of Go players Instructor: Dr. Amos Storkey Teaching Assistant: Matt Graham Due in 16:00 Monday 7 th March 2016. Marks: This
More informationCOMPUTATIONAL COMPLEXITY OF PARAMETRIC LINEAR PROGRAMMING +
Mathematical Programming 19 (1980) 213-219. North-Holland Publishing Company COMPUTATIONAL COMPLEXITY OF PARAMETRIC LINEAR PROGRAMMING + Katta G. MURTY The University of Michigan, Ann Arbor, MI, U.S.A.
More informationDesign of experiments for microbiological models
Design of experiments for microbiological models Holger Dette Ruhr-Universität Bochum Fakultät für Mathematik 44780 Bochum Germany email: holger.dette@ruhr-uni-bochum.de FAX: +49 2 34 70 94 559 Viatcheslav
More informationReal option valuation for reserve capacity
Real option valuation for reserve capacity MORIARTY, JM; Palczewski, J doi:10.1016/j.ejor.2016.07.003 For additional information aout this pulication click this link. http://qmro.qmul.ac.uk/xmlui/handle/123456789/13838
More informationOptimal designs for enzyme inhibition kinetic models
Optimal designs for enzyme inhibition kinetic models Kirsten Schorning, Holger Dette, Katrin Kettelhake, Tilman Möller Fakultät für Mathematik Ruhr-Universität Bochum 44780 Bochum Germany Abstract In this
More information