Some Proofs: This section provides proofs of some theoretical results in section 3.
|
|
- Berenice James
- 6 years ago
- Views:
Transcription
1 Testing Jups via False Discovery Rate Control Yu-Min Yen. Institute of Econoics, Acadeia Sinica, Taipei, Taiwan. E-ail: SUPPLEMENTARY MATERIALS Suppleentary Materials contain the following sections: Soe oofs: This section provides proofs of soe theoretical results in section 3. The PRDS condition: This section provides a ore detailed discussion on the PRDS condition. Siulation with the SVFJ odel: This section provides siulation results fro another stochastic volatility plus jup odel SVFJ []. Data descriptions: This section provides descriptions of the real data used in section 5. Soe discussions on the daily realized variance, bipower variation, the jup test statistics and icrostructure issue of the data are also presented. oofs of soe theoretical results oof of Theore oof. Let s start our proof fro how to construct the. Without loss of generality, suppose that the first hypotheses are true, and the rest hypotheses are false. Now consider events such that we reject the first v true null hypotheses and the first s false hypotheses. Let the optial significance level selected by the BH procedure i γ/ q v+s. Then p M, q v+s,..., p M,v q v+s, p M,v+ > q v+s+,..., p M,0 > q 0+s, p M,0+ q v+s,..., p M,0+s q v+s, p M,0+s+ > q 0+s+,..., p M, > q represents probability of one of such events. Note that here i v+s, and q i iγ/ for i v+s+,..., is the crietria corresponding to a hypothesis which is not rejected. Let,, [0, q v+s ] v 0+s iv+s+ q i, ] [0, q v+s ] s i+s+ q i, ], and the above probability can be rewritten as p M,,. Let E i [0, ] be the fold products of interval [0, ]. Note that joint density of p M is integrable over the set E. Apparently,, E, so p M,, exists. By suitably varying perutations of intervals [0, qv+s ] and q i, ] i v + s +,...,, we can obtain different diensional cubes to construct sets for events 0 of rejecting s false and v true null hypotheses, and the total nuber of such perutations is v s v!. s To see this, at first we focus on the events when p M, q v+s,..., p M,v q v+s and p M,0+ q v+s,..., p M,0+s q v+s occur, and the rest p-values are greater than their corresponding significance
2 2 levels. In this case, there are total s v! possible perutations of q i, ] for these non-rejected hypotheses. Let s v!,,j,, j be union of such events, and also obviously, E. Furtherore, if we vary perutations of the 0 interval [0, q v+s ] for the s false the v true null hypotheses, there are such different perutations. Therefore for the s false and the v true null hypotheses, total nuber of possible perutations s v 0 of the interval [0, q v+s ] is. Let 0 v s 0, and s v! h, v s 0 h,j,, for j h,..., denote such union of the diensional cubes. Finally, let h h, h s v! j h,j,. E, since all h,j, E. When there are true null hypotheses, the probability of rejecting v true null and s false hypotheses under the BH procedure is thus given by 0 s v! h { } v,s s v! p M h,j, p M p M. h j h j h,j, The sae approach can be used to construct the probability of rejecting v true null and s false hypotheses when we ipleent the BH procedure with p, and it is given by p. Furtherore, if the consistency for ultivariate distribution holds, p M and p D v,s exist when. Then E V/R and E pm V/R can be expressed as a function of the arginal distributions of p-values. Let us use E V/R as an exaple. As shown in Lea 4. of [2], p can be further expressed as h v,s p i q v+s v p h,, h and therefore V E R v v + s p s0 v v v + s 0 p h, s0 v h v p i q v+s v + s v p s0 v h 0 v + s v,s p i q v+s p s0 v h, h h, h.
3 3 Let Λ v,s i, denote the event that if p i q v+s occurs and then v true null and s false hypotheses are rejected. We can see that Also let {p i q v+s } p h, h {p i q v+s } Λ v,s i,. q {q v+s : v + s } α, and Λ i, { } Λ v,s i, : v + s. Note that Λ v,s i, is utually disjoint for different v and s. Λ i, is the event that except Hi 0, we reject the other hypotheses given true null hypotheses, and it is disjoint for different i. Then Considering the sae way, Thus V E R s0 v s0 v p M,i q Λi,0, an analog of E pm V R v + s p i q v+s p v + s p i q v+s Λ v,s p i q Λ i,0. h, h i, p i q Λ when p i,0 M is used. Following p M,i q Λ i,0. V V E p M R E R p M,i q Λ i,0 p i q Λ i,0. Note that the consistency for ultivariate distribution should hold, then the above joint probability functions exist when. p i q Λ is just the probability that if p i,0 i q, then the other hypotheses are rejected. Therefore p i q Λ can be explicitly expressed as i,0 Then p i q Λ i,0 p i q, p i q, p i p i q, p i p i q Λ i,0 > q +,... p i > q > q +,... p i > q p i q, p i > q, p i > q +,... p i > q. p i q, p i > q +,... p i > q p i q, p i > q, p i > q +,... p i > q.
4 The first ter of the above suation is ter is/ is 2 2 p i q p i q, p i > q p i q, p i > q 2,... p i > q, while the last. Suation of the iddle 2 ters p i q, p i > q +,... p i > q p i q, p i > q, p i > q +,... p i > q p i q, p i > q +,... p i > q 2 + p i q +, p i > q +, p i + > q +2,... p i > q p i q, p i > q +,... p i > q + p i q +, p i p i q, p i > q 2+,... p i > q > q +, p i + > q +2,... p i > q + p i q, p i > q. 4 Therefore p i q Λ i,0 p i q, p i > q +,..., p i > q p i q +, p i + > q +,..., p i > q + p i q By siilar way, p M,i q Λ i,0 p M,i q, p i M, > q +,..., p i M, > q + p M,i q +, p i M, > q +,..., p i M, > q + p M,i q. Note that q γ/, so in general as goes large, p i q, p i > q +,..., p i > q p i q +, p i > q +,..., p i > q.
5 5 Then p i q, p i > q +,..., p i > q + p i q +, p i > q +,..., p i > q p i q, p i > q +,..., p i > q p i q, p i + > q +,..., p i > q + p i q, p i > q +,..., p i > q. Also p M,i q, p i M, > q +,..., p i M, > q + p M,i q +, p i M, > q +,..., p i M, > q + p M,i q, p i M, > q +,..., p i M, > q. Finally V V E p M R E R p M,i q Λ i,0 p i q Λ i,0 i I p M,i q, p i M, > q +,..., p i M, > q 0 + p i q, p i > q +,..., p i > q + p M,i q p i q p M,i q, p i M, > q +,..., p i M, > q + p i q, p i > q +,..., p i > q + p M,i q p i q. If condition 4 holds, the second ter of the last inequality is bounded by O /M δ. If condition 5 hold, the first ter of the last inequality becoes p M,i q, p i M, > q +,..., p i M, > q + p i q, p i > q +,..., p i > q sup sup p M,i q, p i M, > q +,..., p i M, > q p i q, p i > q +,..., p i > q 0 o.
6 6 We then can conclude that V V E p M R E R 0 o + 0 O M δ. Then V V E p M E R R V 0 E p M R 0 0 E V 00 R 0 0 VR V E pm E R 0 0 o + 0 O M δ 0 E o + O M δ o. As shown in the proof of Theore.2 of [2], if condition 2 holds, then Λ i, p i q. By the assuption that p i q γ, {p i q } Λ Λ i,0 i, p i q γ. Thus V E R V E R γ 0 0 E {p i q } Λ i, Λ i, p i q p i q Λ i, p i q γ Λ p i,0 i q γ γ, V R γ E γ γ. Finally we can conclude that V V li M E p M E γ. R R
7 7 oof of Theore 2 oof. To start our proof, at first we have a loo of the inequality, p M,i a a, where a 0, and i I 0. Suppose that a q γ/,,...,, and γ 0,, then the above inequality becoes p M,i q γ. It iplies p M,i q γ for all,..., and and i I 0. Let p M,i q F pi,m q, therefore for i I 0, F pi,m q is bound by γ as. Furtherore, since T,..., T are continuous rando variables, p i q γ/. Let F pi q p i a /, then for i I 0, F pi q is also bounded. Since both F pi q and F pi,m q are bound and continuous functions of p i q and p M,i q respectively, we can conclude that as M, if sup sup p M,i q p i q O M δ, then sup sup sup F pm,i q F pi q sup p M,i q p i q O M δ. Since T,..., T are independent, then p,..., p are also independent. Therefore the event Λ i, and {p i q } are independent, and Λ i,0 p i q Λ i,0. Furtherore, by Λ i, are utually exclusive for and is the whole space, therefore Λ i, Λ p i,0 i q Λ i,0 Λ i,. Since T M,,..., T M, are also utually independent, by siilar arguent as above, Λ i,. Fro proof of Theore, we now that V V E p M R E R p M,i q Λ i,0 p i q Λ i,0. Λ i,
8 8 It can be shown that p M,i q Λ i,0 p i q Λ i,0 Λ p i,0 M,i q p M,i q Λ p i,0 M,i q p i q + Λ p i,0 M,i q p i q Λ p i,0 i q p i q Λ p i,0 M,i q p M,i q p i q + Λ p i,0 M,i q Λ p i,0 i q p i q. Therefore O since Λ V V E p M R E R p M,i q Λ i,0 p i q Λ i,0 Λ i,0 p M,i q p M,i q p i q + Λ p i,0 M,i q Λ p i,0 i q p i q Λ i,0 p M,i q p M,i q p i q + Λ p i,0 M,i q Λ i, p i q Λ i,0 sup sup i I 0 p M,i q p i q + Λ i,0 Λ i, γ Λ i,0 O M δ + γ M δ, i, Λ i,0 0. So V V E p M E R R VR E pm E 0 E O M δ o. V R Λ i,0 Λ i,
9 9 Finally, if T,..., T are utually independent, their joint distribution is PRDS on the subset of p-values corresponding to true null hypotheses. Thus the conclusion follows. oof of oposition Siilar as in [3], we apply the Orlicz nor to prove the proposition. The Orlicz nor U ψ is defined as { } U U ψ inf c 3 > 0 : E ψ, where ψ is a non-decreasing and convex function with ψ 0 0. As suggested by [4], we set ψ as c 3 ψ ρ u exp u ρ, in the following proof. The corresponding Orlicz nor of ψ ρ u is called an exponential Orlicz nor. For all nonnegative u, u ρ ψ ρ u, which iplies that U ρ U ψρ for each ρ. oof. Let M δ TM,i T i U i,m. With ψ ρ u exp u ρ and ψρ log + ρ, the proof directly follows fro lea 2.2. and in [4]. Given true null hypotheses, as M M 0 ax U i,m ax U i,m ρ ψρ c 5 log + ρ ax U i,m i I ψρ 0 c 5 log + ρ + c ρ 2c 5 log ρ + c c 2 c 2 ρ, by log + 2 log. Thus ax Ti T M,i ax U i log ρ i I ρ 0 M δ c 6 ρ M δ, [ where c 6 2c 5 c 2 + c ] ρ <. Therefore if M δ log ρ o as M,, we can conclude that T P. M,i T i for all i I 0, and sup sup i I0 p M,i q p i q o since convergence in probability iplies convergence in law. More discussions on the PRDS condition PRDS is a special case of positive regression dependence. Lehann [5] defined a rando variable Y positive regression dependent on a rando variable X as Y y X x is non-increasing in x, while Y is negative regression dependent on X if Y y X x is non-decreasing in x. Y positive negative regression dependent on X is also called stochastic onotonicity of Y y X x.
10 0 Y positive regression dependent on X also iplies that for all x x and Y y X x Y y X x, 2 Y y, X x Y y X x. 3 3 is called X and Y are positively quadrant dependent. It says that the ore possibility of X being sall large, the ore possibility of Y also being sall large. If we let x, then 2 becoes 3. With siple algebra, it can be shown that iplies 2, and 2 iplies 3. All of the three conditions can be extended to ultiple variables. Positive regression dependent of an l diensional rando vector Y on a diensional rando vector X is that Y y,..., Y l y l X x,..., X x 4 is non-increasing in x,... x. Obviously Y is PRDS on a subset I 0 of X is less stringent than 4. Another frequently used but ore restricted criteria for dependency of ultivariate rando variables is the ultivariate totally positive of order 2 MTP 2. Karlin and Rinott [6] defined a diensional rando vector X to have an MTP 2 distribution if the corresponding joint density f X satisfies where f X y z f X y z f X y f X z, y y,..., y, z z,..., z, y z ax y, z,..., ax y, z, y z in y, z,..., in y, z. The nuber of diension can be extended to infinity or even continuous. MTP 2 iplies positive regression dependent, and therefore iplies PRDS [7]. It can be shown that joint density of rando variables X i satisfying MTP 2 iplies Cov X i, X j 0 for i, j,...,. Nevertheless, except the case of ultivariate noral, PRDS and Cov X i, X j 0 ay not iply each other [2]. In a ore general situation, epirically verifying whether data structure satisfies the above conditions ay be difficult. But soe solutions have been suggested, for exaple, a nonparaetric test for stochastic onotonicity proposed by [8]. A Siulation study with SVFJ For an additional siulation study, we use the following stochastic volatility with one jup coponent odel SVFJ, which also was considered in [], d log P t µdt + exp β 0 + β σ t dw t + dj t, dσ t aσ t dt + dw 2 t, J t Nt j D t, j, D t, j iid N 0,, N t iid Poisson λdt, where dw t and dw 2 t follow the standard Brownian otion, and σ 2 t follows a siple stochastic process. J t follows a Copound Poisson ocess CPP with a constant intensity λdt, and N t is the nuber of jups occurring within the sall interval t t, t].
11 For the siulation, we set the paraeter to the following values. µ 0.03, β 0 0, β 0.25, and a 0.. In addition, we also add the leverage effect into the odel, and the correlation between dw t and dw 2 t is set to All of the other settings for the siulation are the sae as in the SVJ case. Relevant results are shown in Figure S to Figure S5. It can be seen that all the results are qualitatively siilar to those of the SVJ case. Data descriptions The raw data used for the epirical applications are one inute recorded prices of S&P500 SPC500 index in cash and Dow Jones Industrial Average DJIA index. The saple period spans fro Jan to Dec The data sets are provided by Olsen Financial Technologies in Zūrich, Switzerland. During the saple period, aret closed at p on a few days. Such days were inactive trading days, and we exclude the fro our saples. After eliinating these inactive trading days, we have 247 active trading days for both DJIA and S&P 500 indices. In our epirical analysis in section 5, all estiated realized price variations and test statistics are based on the data fro the 247 active trading days. To estiate the intradaily price variations, we use five inute log returns but exclude overnight returns. Soe issues of icrostructure noise are also concerned here. When observed prices contain icrostructure noise, realized variations estiated with different sapling frequencies will have different degrees of biasness. Since the two indices are not really traded, their price series would be less liely to suffer distortions fro the icrostructure noise than those of traded futures. The property of iunizing the icrostructure noise can be seen in Figure S6, which shows volatility signature plots. The horizontal dashed line in each plot is the average daily realized variance when the 5-in log returns are used. It can be seen that the average values of the realized variances are downward biased when their sapling intervals are sall. As the sapling interval becoes oderately large, the average values becoe stable, and the biasness is itigated. However, the downward biasness reappears when the sapling interval increases beyond one hour. Fro the figure, we can see that the realized variances estiated fro the 5-in log return data see to suffer little icrostructural effect. This is the reason why the 5-in log return data is used to construct the realized variance estiations. We then calculate the three different jup test statistics Z.5,i, Z log,i and Z ratio,i and their corresponding p-values. To avoid effects of abnoral trades, we oit data of the first five inutes 09:3-09:35 and the last ten inutes 6:0-6:0, so the nuber of saples for each day equals to 77. This additional step of screening the data aes our estiates reflect intradaily dynaics of the two indices ore hoogeneously and efficiently. Note that the additional screening step only applies to JV i and the daily jup test statistics. For RV i and BV i, we still eep the 80 saples each day. Figure S7 shows tie series plots of RV, BV and JV i,0.05 for the two indices. It can be seen that the log type statistic have ost identified jup days.
12 2 References. Huang X, Tauchen GE 2005 The relative contribution of jups to total price variance. Journal of Financial Econoetrics 3: Benjaini Y, Yeutieli D 200 The control of the false discovery rate in ultiple testing under dependency. The Annals of Statistics 29: Kosoro MR, Ma S 2007 Marginal asyptotics for the large p, sall n paradig : With applications to icroarray data. The Annals of Statistics 35: van der Vaart A, Wellner J 996 Wea Convergence and Epirical ocesses: With Applications to Statistics. New Yor: Springer-Verlag. 5. Lehann EL 966 Soe concepts of dependence. The Annals of Matheatical Statistics 37: Karlin S, Rinott Y 98 Total positivity properties of absolute value ultinoral variables with applications to confidence interval estiates and related probabilistic inequalities. The Annals of Statistics 9: Sarar SK 2002 Soe results on false discovery rate in stepwise ultiple testing procedures. The Annals of Statistics 30: Lee S, Linton O, Whang YJ 2009 Testing for stochastic onotonicity. Econoetrica 77:
Testing Jumps via False Discovery Rate Control
Testing Jumps via False Discovery Rate Control Yu-Min Yen August 12, 2011 Abstract Many recently developed nonparametric jump tests can be viewed as multiple hypothesis testing problems. For such multiple
More informationare equal to zero, where, q = p 1. For each gene j, the pairwise null and alternative hypotheses are,
Page of 8 Suppleentary Materials: A ultiple testing procedure for ulti-diensional pairwise coparisons with application to gene expression studies Anjana Grandhi, Wenge Guo, Shyaal D. Peddada S Notations
More informationThe proofs of Theorem 1-3 are along the lines of Wied and Galeano (2013).
A Appendix: Proofs The proofs of Theore 1-3 are along the lines of Wied and Galeano (2013) Proof of Theore 1 Let D[d 1, d 2 ] be the space of càdlàg functions on the interval [d 1, d 2 ] equipped with
More informationA Simple Regression Problem
A Siple Regression Proble R. M. Castro March 23, 2 In this brief note a siple regression proble will be introduced, illustrating clearly the bias-variance tradeoff. Let Y i f(x i ) + W i, i,..., n, where
More informationBlock designs and statistics
Bloc designs and statistics Notes for Math 447 May 3, 2011 The ain paraeters of a bloc design are nuber of varieties v, bloc size, nuber of blocs b. A design is built on a set of v eleents. Each eleent
More informationGeneralized Augmentation for Control of the k-familywise Error Rate
International Journal of Statistics in Medical Research, 2012, 1, 113-119 113 Generalized Augentation for Control of the k-failywise Error Rate Alessio Farcoeni* Departent of Public Health and Infectious
More informationThe degree of a typical vertex in generalized random intersection graph models
Discrete Matheatics 306 006 15 165 www.elsevier.co/locate/disc The degree of a typical vertex in generalized rando intersection graph odels Jerzy Jaworski a, Michał Karoński a, Dudley Stark b a Departent
More informationFDR- and FWE-controlling methods using data-driven weights
FDR- and FWE-controlling ethods using data-driven weights LIVIO FINOS Center for Modelling Coputing and Statistics, University of Ferrara via N.Machiavelli 35, 44 FERRARA - Italy livio.finos@unife.it LUIGI
More informationESTIMATING AND FORMING CONFIDENCE INTERVALS FOR EXTREMA OF RANDOM POLYNOMIALS. A Thesis. Presented to. The Faculty of the Department of Mathematics
ESTIMATING AND FORMING CONFIDENCE INTERVALS FOR EXTREMA OF RANDOM POLYNOMIALS A Thesis Presented to The Faculty of the Departent of Matheatics San Jose State University In Partial Fulfillent of the Requireents
More informationAN OPTIMAL SHRINKAGE FACTOR IN PREDICTION OF ORDERED RANDOM EFFECTS
Statistica Sinica 6 016, 1709-178 doi:http://dx.doi.org/10.5705/ss.0014.0034 AN OPTIMAL SHRINKAGE FACTOR IN PREDICTION OF ORDERED RANDOM EFFECTS Nilabja Guha 1, Anindya Roy, Yaakov Malinovsky and Gauri
More informationDERIVING TESTS OF THE REGRESSION MODEL USING THE DENSITY FUNCTION OF A MAXIMAL INVARIANT
DERIVING TESTS OF THE REGRESSION MODEL USING THE DENSITY FUNCTION OF A MAXIMAL INVARIANT Jahar L. Bhowik and Maxwell L. King Departent of Econoetrics and Business Statistics Monash University Clayton,
More informationTesting the lag length of vector autoregressive models: A power comparison between portmanteau and Lagrange multiplier tests
Working Papers 2017-03 Testing the lag length of vector autoregressive odels: A power coparison between portanteau and Lagrange ultiplier tests Raja Ben Hajria National Engineering School, University of
More informationCourse Notes for EE227C (Spring 2018): Convex Optimization and Approximation
Course Notes for EE7C (Spring 018: Convex Optiization and Approxiation Instructor: Moritz Hardt Eail: hardt+ee7c@berkeley.edu Graduate Instructor: Max Sichowitz Eail: sichow+ee7c@berkeley.edu October 15,
More information1 Proof of learning bounds
COS 511: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #4 Scribe: Akshay Mittal February 13, 2013 1 Proof of learning bounds For intuition of the following theore, suppose there exists a
More informationTEST OF HOMOGENEITY OF PARALLEL SAMPLES FROM LOGNORMAL POPULATIONS WITH UNEQUAL VARIANCES
TEST OF HOMOGENEITY OF PARALLEL SAMPLES FROM LOGNORMAL POPULATIONS WITH UNEQUAL VARIANCES S. E. Ahed, R. J. Tokins and A. I. Volodin Departent of Matheatics and Statistics University of Regina Regina,
More informationUnderstanding Machine Learning Solution Manual
Understanding Machine Learning Solution Manual Written by Alon Gonen Edited by Dana Rubinstein Noveber 17, 2014 2 Gentle Start 1. Given S = ((x i, y i )), define the ultivariate polynoial p S (x) = i []:y
More informationConstructing Locally Best Invariant Tests of the Linear Regression Model Using the Density Function of a Maximal Invariant
Aerican Journal of Matheatics and Statistics 03, 3(): 45-5 DOI: 0.593/j.ajs.03030.07 Constructing Locally Best Invariant Tests of the Linear Regression Model Using the Density Function of a Maxial Invariant
More informationLost-Sales Problems with Stochastic Lead Times: Convexity Results for Base-Stock Policies
OPERATIONS RESEARCH Vol. 52, No. 5, Septeber October 2004, pp. 795 803 issn 0030-364X eissn 1526-5463 04 5205 0795 infors doi 10.1287/opre.1040.0130 2004 INFORMS TECHNICAL NOTE Lost-Sales Probles with
More informationarxiv: v1 [stat.ot] 7 Jul 2010
Hotelling s test for highly correlated data P. Bubeliny e-ail: bubeliny@karlin.ff.cuni.cz Charles University, Faculty of Matheatics and Physics, KPMS, Sokolovska 83, Prague, Czech Republic, 8675. arxiv:007.094v
More informationCourse Notes for EE227C (Spring 2018): Convex Optimization and Approximation
Course Notes for EE227C (Spring 2018): Convex Optiization and Approxiation Instructor: Moritz Hardt Eail: hardt+ee227c@berkeley.edu Graduate Instructor: Max Sichowitz Eail: sichow+ee227c@berkeley.edu October
More informationE0 370 Statistical Learning Theory Lecture 6 (Aug 30, 2011) Margin Analysis
E0 370 tatistical Learning Theory Lecture 6 (Aug 30, 20) Margin Analysis Lecturer: hivani Agarwal cribe: Narasihan R Introduction In the last few lectures we have seen how to obtain high confidence bounds
More informationLocal asymptotic powers of nonparametric and semiparametric tests for fractional integration
Stochastic Processes and their Applications 117 (2007) 251 261 www.elsevier.co/locate/spa Local asyptotic powers of nonparaetric and seiparaetric tests for fractional integration Xiaofeng Shao, Wei Biao
More informationKeywords: Estimator, Bias, Mean-squared error, normality, generalized Pareto distribution
Testing approxiate norality of an estiator using the estiated MSE and bias with an application to the shape paraeter of the generalized Pareto distribution J. Martin van Zyl Abstract In this work the norality
More informationTracking using CONDENSATION: Conditional Density Propagation
Tracking using CONDENSATION: Conditional Density Propagation Goal Model-based visual tracking in dense clutter at near video frae rates M. Isard and A. Blake, CONDENSATION Conditional density propagation
More informatione-companion ONLY AVAILABLE IN ELECTRONIC FORM
OPERATIONS RESEARCH doi 10.1287/opre.1070.0427ec pp. ec1 ec5 e-copanion ONLY AVAILABLE IN ELECTRONIC FORM infors 07 INFORMS Electronic Copanion A Learning Approach for Interactive Marketing to a Custoer
More informationStatistics and Probability Letters
Statistics and Probability Letters 79 2009 223 233 Contents lists available at ScienceDirect Statistics and Probability Letters journal hoepage: www.elsevier.co/locate/stapro A CLT for a one-diensional
More information1 Generalization bounds based on Rademacher complexity
COS 5: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #0 Scribe: Suqi Liu March 07, 08 Last tie we started proving this very general result about how quickly the epirical average converges
More information1 Bounding the Margin
COS 511: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #12 Scribe: Jian Min Si March 14, 2013 1 Bounding the Margin We are continuing the proof of a bound on the generalization error of AdaBoost
More informationTesting equality of variances for multiple univariate normal populations
University of Wollongong Research Online Centre for Statistical & Survey Methodology Working Paper Series Faculty of Engineering and Inforation Sciences 0 esting equality of variances for ultiple univariate
More informationCS Lecture 13. More Maximum Likelihood
CS 6347 Lecture 13 More Maxiu Likelihood Recap Last tie: Introduction to axiu likelihood estiation MLE for Bayesian networks Optial CPTs correspond to epirical counts Today: MLE for CRFs 2 Maxiu Likelihood
More informationNonlinear Log-Periodogram Regression for Perturbed Fractional Processes
Nonlinear Log-Periodogra Regression for Perturbed Fractional Processes Yixiao Sun Departent of Econoics Yale University Peter C. B. Phillips Cowles Foundation for Research in Econoics Yale University First
More informationBootstrapping Dependent Data
Bootstrapping Dependent Data One of the key issues confronting bootstrap resapling approxiations is how to deal with dependent data. Consider a sequence fx t g n t= of dependent rando variables. Clearly
More informationSolutions of some selected problems of Homework 4
Solutions of soe selected probles of Hoework 4 Sangchul Lee May 7, 2018 Proble 1 Let there be light A professor has two light bulbs in his garage. When both are burned out, they are replaced, and the next
More informationMulti-Dimensional Hegselmann-Krause Dynamics
Multi-Diensional Hegselann-Krause Dynaics A. Nedić Industrial and Enterprise Systes Engineering Dept. University of Illinois Urbana, IL 680 angelia@illinois.edu B. Touri Coordinated Science Laboratory
More informationCENTRE FOR STOCHASTIC GEOMETRY AND ADVANCED BIOIMAGING. RESEARCH REPORT. Christophe A.N. Biscio and Jesper Møller
CENTRE FOR STOCHASTIC GEOMETRY AND ADVANCED BIOIMAGING www.csgb.dk RESEARCH REPORT 2016 Christophe A.N. Biscio and Jesper Møller The accuulated persistence function, a new useful functional suary statistic
More informationSupplement to: Subsampling Methods for Persistent Homology
Suppleent to: Subsapling Methods for Persistent Hoology A. Technical results In this section, we present soe technical results that will be used to prove the ain theores. First, we expand the notation
More informationSupport Vector Machines. Maximizing the Margin
Support Vector Machines Support vector achines (SVMs) learn a hypothesis: h(x) = b + Σ i= y i α i k(x, x i ) (x, y ),..., (x, y ) are the training exs., y i {, } b is the bias weight. α,..., α are the
More informationOptimal Jackknife for Discrete Time and Continuous Time Unit Root Models
Optial Jackknife for Discrete Tie and Continuous Tie Unit Root Models Ye Chen and Jun Yu Singapore Manageent University January 6, Abstract Maxiu likelihood estiation of the persistence paraeter in the
More informationDERIVING PROPER UNIFORM PRIORS FOR REGRESSION COEFFICIENTS
DERIVING PROPER UNIFORM PRIORS FOR REGRESSION COEFFICIENTS N. van Erp and P. van Gelder Structural Hydraulic and Probabilistic Design, TU Delft Delft, The Netherlands Abstract. In probles of odel coparison
More informationThis article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and
This article appeared in a ournal published by Elsevier. The attached copy is furnished to the author for internal non-coercial research and education use, including for instruction at the authors institution
More informationSimultaneous critical values for t-tests in very high dimensions
Bernoulli 17(1, 2011, 347 394 DOI: 10.3150/10-BEJ272 Siultaneous critical values for t-tests in very high diensions HONGYUAN CAO 1 and MICHAEL R. KOSOROK 2 1 Departent of Health Studies, 5841 South Maryland
More informationProbability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers Roy D. Yates and David J.
Probability and Stochastic Processes: A Friendly Introduction for Electrical and oputer Engineers Roy D. Yates and David J. Goodan Proble Solutions : Yates and Goodan,1..3 1.3.1 1.4.6 1.4.7 1.4.8 1..6
More informationConsistent Multiclass Algorithms for Complex Performance Measures. Supplementary Material
Consistent Multiclass Algoriths for Coplex Perforance Measures Suppleentary Material Notations. Let λ be the base easure over n given by the unifor rando variable (say U over n. Hence, for all easurable
More informationAsymptotics of weighted random sums
Asyptotics of weighted rando sus José Manuel Corcuera, David Nualart, Mark Podolskij arxiv:402.44v [ath.pr] 6 Feb 204 February 7, 204 Abstract In this paper we study the asyptotic behaviour of weighted
More informationIn this chapter, we consider several graph-theoretic and probabilistic models
THREE ONE GRAPH-THEORETIC AND STATISTICAL MODELS 3.1 INTRODUCTION In this chapter, we consider several graph-theoretic and probabilistic odels for a social network, which we do under different assuptions
More informationarxiv: v1 [math.pr] 17 May 2009
A strong law of large nubers for artingale arrays Yves F. Atchadé arxiv:0905.2761v1 [ath.pr] 17 May 2009 March 2009 Abstract: We prove a artingale triangular array generalization of the Chow-Birnbau- Marshall
More informationThe path integral approach in the frame work of causal interpretation
Annales de la Fondation Louis de Broglie, Volue 28 no 1, 2003 1 The path integral approach in the frae work of causal interpretation M. Abolhasani 1,2 and M. Golshani 1,2 1 Institute for Studies in Theoretical
More informationPoisson processes and their properties
Poisson processes and their properties Poisson processes. collection {N(t) : t [, )} of rando variable indexed by tie t is called a continuous-tie stochastic process, Furtherore, we call N(t) a Poisson
More informationarxiv: v2 [math.st] 11 Dec 2018
esting for high-diensional network paraeters in auto-regressive odels arxiv:803659v [aths] Dec 08 Lili Zheng and Garvesh Raskutti Abstract High-diensional auto-regressive odels provide a natural way to
More informationComputable Shell Decomposition Bounds
Coputable Shell Decoposition Bounds John Langford TTI-Chicago jcl@cs.cu.edu David McAllester TTI-Chicago dac@autoreason.co Editor: Leslie Pack Kaelbling and David Cohn Abstract Haussler, Kearns, Seung
More informationPseudo-marginal Metropolis-Hastings: a simple explanation and (partial) review of theory
Pseudo-arginal Metropolis-Hastings: a siple explanation and (partial) review of theory Chris Sherlock Motivation Iagine a stochastic process V which arises fro soe distribution with density p(v θ ). Iagine
More informationEstimation of the Mean of the Exponential Distribution Using Maximum Ranked Set Sampling with Unequal Samples
Open Journal of Statistics, 4, 4, 64-649 Published Online Septeber 4 in SciRes http//wwwscirporg/ournal/os http//ddoiorg/436/os4486 Estiation of the Mean of the Eponential Distribution Using Maiu Ranked
More informationMoments of the product and ratio of two correlated chi-square variables
Stat Papers 009 50:581 59 DOI 10.1007/s0036-007-0105-0 REGULAR ARTICLE Moents of the product and ratio of two correlated chi-square variables Anwar H. Joarder Received: June 006 / Revised: 8 October 007
More informationThe Distribution of the Covariance Matrix for a Subset of Elliptical Distributions with Extension to Two Kurtosis Parameters
journal of ultivariate analysis 58, 96106 (1996) article no. 0041 The Distribution of the Covariance Matrix for a Subset of Elliptical Distributions with Extension to Two Kurtosis Paraeters H. S. Steyn
More informationNon-Parametric Non-Line-of-Sight Identification 1
Non-Paraetric Non-Line-of-Sight Identification Sinan Gezici, Hisashi Kobayashi and H. Vincent Poor Departent of Electrical Engineering School of Engineering and Applied Science Princeton University, Princeton,
More informationMetric Entropy of Convex Hulls
Metric Entropy of Convex Hulls Fuchang Gao University of Idaho Abstract Let T be a precopact subset of a Hilbert space. The etric entropy of the convex hull of T is estiated in ters of the etric entropy
More informationSharp Time Data Tradeoffs for Linear Inverse Problems
Sharp Tie Data Tradeoffs for Linear Inverse Probles Saet Oyak Benjain Recht Mahdi Soltanolkotabi January 016 Abstract In this paper we characterize sharp tie-data tradeoffs for optiization probles used
More informationA Jackknife Correction to a Test for Cointegration Rank
Econoetrics 205, 3, 355-375; doi:0.3390/econoetrics3020355 OPEN ACCESS econoetrics ISSN 2225-46 www.dpi.co/journal/econoetrics Article A Jackknife Correction to a Test for Cointegration Rank Marcus J.
More informationInference about Realized Volatility using In ll Subsampling
Inference about Realized Volatility using In ll Subsapling Ilze Kalnina y and Oliver Linton z The London School of Econoics (PRELIMINARY AND INCOMPLETE) April 16, 7 Abstract We investigate the use of subsapling,
More informationComputable Shell Decomposition Bounds
Journal of Machine Learning Research 5 (2004) 529-547 Subitted 1/03; Revised 8/03; Published 5/04 Coputable Shell Decoposition Bounds John Langford David McAllester Toyota Technology Institute at Chicago
More informationComputational and Statistical Learning Theory
Coputational and Statistical Learning Theory Proble sets 5 and 6 Due: Noveber th Please send your solutions to learning-subissions@ttic.edu Notations/Definitions Recall the definition of saple based Radeacher
More informationThe Hilbert Schmidt version of the commutator theorem for zero trace matrices
The Hilbert Schidt version of the coutator theore for zero trace atrices Oer Angel Gideon Schechtan March 205 Abstract Let A be a coplex atrix with zero trace. Then there are atrices B and C such that
More informationRAFIA(MBA) TUTOR S UPLOADED FILE Course STA301: Statistics and Probability Lecture No 1 to 5
Course STA0: Statistics and Probability Lecture No to 5 Multiple Choice Questions:. Statistics deals with: a) Observations b) Aggregates of facts*** c) Individuals d) Isolated ites. A nuber of students
More informationInformation Loss in Volatility Measurement with Flat Price Trading 1
Inforation Loss in Volatility Measureent with Flat Price Trading Peter C. B. Phillips Yale University, University of Auckland, University of York & Singapore Manageent University Jun Yu Singapore Manageent
More informationExtension of CSRSM for the Parametric Study of the Face Stability of Pressurized Tunnels
Extension of CSRSM for the Paraetric Study of the Face Stability of Pressurized Tunnels Guilhe Mollon 1, Daniel Dias 2, and Abdul-Haid Soubra 3, M.ASCE 1 LGCIE, INSA Lyon, Université de Lyon, Doaine scientifique
More informationESE 523 Information Theory
ESE 53 Inforation Theory Joseph A. O Sullivan Sauel C. Sachs Professor Electrical and Systes Engineering Washington University 11 Urbauer Hall 10E Green Hall 314-935-4173 (Lynda Marha Answers) jao@wustl.edu
More informationNecessity of low effective dimension
Necessity of low effective diension Art B. Owen Stanford University October 2002, Orig: July 2002 Abstract Practitioners have long noticed that quasi-monte Carlo ethods work very well on functions that
More informationAdaptive Stabilization of a Class of Nonlinear Systems With Nonparametric Uncertainty
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 46, NO. 11, NOVEMBER 2001 1821 Adaptive Stabilization of a Class of Nonlinear Systes With Nonparaetric Uncertainty Aleander V. Roup and Dennis S. Bernstein
More informationFeature Extraction Techniques
Feature Extraction Techniques Unsupervised Learning II Feature Extraction Unsupervised ethods can also be used to find features which can be useful for categorization. There are unsupervised ethods that
More informationInformation Overload in a Network of Targeted Communication: Supplementary Notes
Inforation Overload in a Network of Targeted Counication: Suppleentary Notes Tiothy Van Zandt INSEAD 10 June 2003 Abstract These are suppleentary notes for Van Zandt 2003). They include certain extensions.
More informationAnalyzing Simulation Results
Analyzing Siulation Results Dr. John Mellor-Cruey Departent of Coputer Science Rice University johnc@cs.rice.edu COMP 528 Lecture 20 31 March 2005 Topics for Today Model verification Model validation Transient
More informationBiostatistics Department Technical Report
Biostatistics Departent Technical Report BST006-00 Estiation of Prevalence by Pool Screening With Equal Sized Pools and a egative Binoial Sapling Model Charles R. Katholi, Ph.D. Eeritus Professor Departent
More informationNew upper bound for the B-spline basis condition number II. K. Scherer. Institut fur Angewandte Mathematik, Universitat Bonn, Bonn, Germany.
New upper bound for the B-spline basis condition nuber II. A proof of de Boor's 2 -conjecture K. Scherer Institut fur Angewandte Matheati, Universitat Bonn, 535 Bonn, Gerany and A. Yu. Shadrin Coputing
More informationMultiple Testing Issues & K-Means Clustering. Definitions related to the significance level (or type I error) of multiple tests
StatsM254 Statistical Methods in Coputational Biology Lecture 3-04/08/204 Multiple Testing Issues & K-Means Clustering Lecturer: Jingyi Jessica Li Scribe: Arturo Rairez Multiple Testing Issues When trying
More informationComputational and Statistical Learning Theory
Coputational and Statistical Learning Theory TTIC 31120 Prof. Nati Srebro Lecture 2: PAC Learning and VC Theory I Fro Adversarial Online to Statistical Three reasons to ove fro worst-case deterinistic
More information1 Brownian motion and the Langevin equation
Figure 1: The robust appearance of Robert Brown (1773 1858) 1 Brownian otion and the Langevin equation In 1827, while exaining pollen grains and the spores of osses suspended in water under a icroscope,
More informationE0 370 Statistical Learning Theory Lecture 5 (Aug 25, 2011)
E0 370 Statistical Learning Theory Lecture 5 Aug 5, 0 Covering Nubers, Pseudo-Diension, and Fat-Shattering Diension Lecturer: Shivani Agarwal Scribe: Shivani Agarwal Introduction So far we have seen how
More informationFAST DYNAMO ON THE REAL LINE
FAST DYAMO O THE REAL LIE O. KOZLOVSKI & P. VYTOVA Abstract. In this paper we show that a piecewise expanding ap on the interval, extended to the real line by a non-expanding ap satisfying soe ild hypthesis
More informationA Bernstein-Markov Theorem for Normed Spaces
A Bernstein-Markov Theore for Nored Spaces Lawrence A. Harris Departent of Matheatics, University of Kentucky Lexington, Kentucky 40506-0027 Abstract Let X and Y be real nored linear spaces and let φ :
More informationFixed-to-Variable Length Distribution Matching
Fixed-to-Variable Length Distribution Matching Rana Ali Ajad and Georg Böcherer Institute for Counications Engineering Technische Universität München, Gerany Eail: raa2463@gail.co,georg.boecherer@tu.de
More information13.2 Fully Polynomial Randomized Approximation Scheme for Permanent of Random 0-1 Matrices
CS71 Randoness & Coputation Spring 018 Instructor: Alistair Sinclair Lecture 13: February 7 Disclaier: These notes have not been subjected to the usual scrutiny accorded to foral publications. They ay
More informationarxiv: v1 [cs.ds] 3 Feb 2014
arxiv:40.043v [cs.ds] 3 Feb 04 A Bound on the Expected Optiality of Rando Feasible Solutions to Cobinatorial Optiization Probles Evan A. Sultani The Johns Hopins University APL evan@sultani.co http://www.sultani.co/
More informationLeast Squares Fitting of Data
Least Squares Fitting of Data David Eberly, Geoetric Tools, Redond WA 98052 https://www.geoetrictools.co/ This work is licensed under the Creative Coons Attribution 4.0 International License. To view a
More informationInflation Forecasts: An Empirical Re-examination. Swarna B. Dutt University of West Georgia. Dipak Ghosh Emporia State University
Southwest Business and Econoics Journal/2006-2007 Inflation Forecasts: An Epirical Re-exaination Swarna B. Dutt University of West Georgia Dipak Ghosh Eporia State University Abstract Inflation forecasts
More informationProbability Distributions
Probability Distributions In Chapter, we ephasized the central role played by probability theory in the solution of pattern recognition probles. We turn now to an exploration of soe particular exaples
More informationCelal S. Konor Release 1.1 (identical to 1.0) 3/21/08. 1-Hybrid isentropic-sigma vertical coordinate and governing equations in the free atmosphere
Celal S. Konor Release. (identical to.0) 3/2/08 -Hybrid isentropic-siga vertical coordinate governing equations in the free atosphere This section describes the equations in the free atosphere of the odel.
More informationSupplementary Materials: Proofs and Technical Details for Parsimonious Tensor Response Regression Lexin Li and Xin Zhang
Suppleentary Materials: Proofs and Tecnical Details for Parsionious Tensor Response Regression Lexin Li and Xin Zang A Soe preliinary results We will apply te following two results repeatedly. For a positive
More informationQuantum algorithms (CO 781, Winter 2008) Prof. Andrew Childs, University of Waterloo LECTURE 15: Unstructured search and spatial search
Quantu algoriths (CO 781, Winter 2008) Prof Andrew Childs, University of Waterloo LECTURE 15: Unstructured search and spatial search ow we begin to discuss applications of quantu walks to search algoriths
More informationA Note on Online Scheduling for Jobs with Arbitrary Release Times
A Note on Online Scheduling for Jobs with Arbitrary Release Ties Jihuan Ding, and Guochuan Zhang College of Operations Research and Manageent Science, Qufu Noral University, Rizhao 7686, China dingjihuan@hotail.co
More informationComparing Probabilistic Forecasting Systems with the Brier Score
1076 W E A T H E R A N D F O R E C A S T I N G VOLUME 22 Coparing Probabilistic Forecasting Systes with the Brier Score CHRISTOPHER A. T. FERRO School of Engineering, Coputing and Matheatics, University
More informationMeta-Analytic Interval Estimation for Bivariate Correlations
Psychological Methods 2008, Vol. 13, No. 3, 173 181 Copyright 2008 by the Aerican Psychological Association 1082-989X/08/$12.00 DOI: 10.1037/a0012868 Meta-Analytic Interval Estiation for Bivariate Correlations
More informationLORENTZ SPACES AND REAL INTERPOLATION THE KEEL-TAO APPROACH
LORENTZ SPACES AND REAL INTERPOLATION THE KEEL-TAO APPROACH GUILLERMO REY. Introduction If an operator T is bounded on two Lebesgue spaces, the theory of coplex interpolation allows us to deduce the boundedness
More informationLecture 21 Principle of Inclusion and Exclusion
Lecture 21 Principle of Inclusion and Exclusion Holden Lee and Yoni Miller 5/6/11 1 Introduction and first exaples We start off with an exaple Exaple 11: At Sunnydale High School there are 28 students
More information2 Q 10. Likewise, in case of multiple particles, the corresponding density in 2 must be averaged over all
Lecture 6 Introduction to kinetic theory of plasa waves Introduction to kinetic theory So far we have been odeling plasa dynaics using fluid equations. The assuption has been that the pressure can be either
More informationSupplementary Information for Design of Bending Multi-Layer Electroactive Polymer Actuators
Suppleentary Inforation for Design of Bending Multi-Layer Electroactive Polyer Actuators Bavani Balakrisnan, Alek Nacev, and Elisabeth Sela University of Maryland, College Park, Maryland 074 1 Analytical
More informationBest Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence
Best Ar Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon Mohaad Ghavazadeh Alessandro Lazaric INRIA Lille - Nord Europe, Tea SequeL {victor.gabillon,ohaad.ghavazadeh,alessandro.lazaric}@inria.fr
More informationEstimating Parameters for a Gaussian pdf
Pattern Recognition and achine Learning Jaes L. Crowley ENSIAG 3 IS First Seester 00/0 Lesson 5 7 Noveber 00 Contents Estiating Paraeters for a Gaussian pdf Notation... The Pattern Recognition Proble...3
More informationA Theoretical Analysis of a Warm Start Technique
A Theoretical Analysis of a War Start Technique Martin A. Zinkevich Yahoo! Labs 701 First Avenue Sunnyvale, CA Abstract Batch gradient descent looks at every data point for every step, which is wasteful
More informationIN modern society that various systems have become more
Developent of Reliability Function in -Coponent Standby Redundant Syste with Priority Based on Maxiu Entropy Principle Ryosuke Hirata, Ikuo Arizono, Ryosuke Toohiro, Satoshi Oigawa, and Yasuhiko Takeoto
More informationA Low-Complexity Congestion Control and Scheduling Algorithm for Multihop Wireless Networks with Order-Optimal Per-Flow Delay
A Low-Coplexity Congestion Control and Scheduling Algorith for Multihop Wireless Networks with Order-Optial Per-Flow Delay Po-Kai Huang, Xiaojun Lin, and Chih-Chun Wang School of Electrical and Coputer
More information