On the Power Function of the Likelihood Ratio Test for MANOVA

Size: px
Start display at page:

Download "On the Power Function of the Likelihood Ratio Test for MANOVA"

Transcription

1 Journa of Mutvarate Anayss 8, (00) do: /jmva On the Power Functon of the Lkehood Rato Test for MANOVA Dua Kumar Bhaumk Unversty of South Aabama and Unversty of Inos at Chcago and Sanat K. Sarkar Tempe Unversty Receved October 7, 1999; pubshed onne January 11, 00 We prove that the power functon of the kehood rato test for MANOVA attans ts mnmum when the rank of the ocaton parameter matrx G decreases from s to 1. Ths provdes a theoretca justfcaton of a resut that s known n the terature based ony on numerca studes. 00 Esever Scence AMS 1980 subject cassfcatons: 6H10; 6H15. Key words and phrases: Wshart dstrbuton; maxma nvarant; zona poynoma; product of beta varates X/0 $ Esever Scence A rghts reserved. 1. INTRODUCTION Suppose X: r p s a random matrx whose rows are ndependenty dstrbuted as mutvarate norma wth common covarance matrx S:p p, and et E(X)=G. Let the random matrx S: p p be ndependent of X and have the Wshart dstrbuton W p (S,n), where S s postve defnte and n \ p. Consder the probem of testng the hypothess H 0 : G=O aganst H 1 : G ] O. (1) Ths s the canonca form of the MANOVA probem. Ths testng probem remans nvarant under a transformatons of the type (X, S) Q (CXPŒ, PSPŒ), () where C:r r s an orthogona matrx, and P: p p s a nonsnguar rea matrx. Let a \ a \ \ a t >0 be the ordered t=mn(r, p) argest egenvaues of XS 1 XŒ and d 1 \ d \ d s >0 be those of W=GS 1 GŒ, 416

2 LIKELIHOOD RATIO TEST FOR MANOVA 417 where s=rank G. A maxma nvarant statstc under ths group of transformatons s (a 1,a,...,a t ), wth (d 1, d,...,d s ) beng a maxma nvarant parameter. The four we-known tests for the above testng probem wth ther acceptance regons are gven beow. 1. Roy s maxmum root test:. Lawey Hoteng trace test: t K =3 (X, S): C K 1 ={(X, S): a 1 [ k 1 }, k 1 >0, a [ k 4,k >0, 3. Lkehood rato test (LRT): t K 3 =3 (X, S): D (1+a ) [ k 3 4,k3 >1, 4. Pa s trace test: t K 4 =3 (X, S): C a /(1+a ) [ k 4 4,0<k4 <1. A number of theoretca resuts expanng how the power functons of these four tests behave wth respect to the non-centraty parameters d 1,...,d s have been derved n the terature. Perman and Okn (1980) proved that any test wth ncreasng rejecton regon n the space of a 1,...,a t,.e., a regon of the form {g(a 1,...,a t ) \ k}, where g s nondecreasng n each argument, s unbased, from whch t foows that a of these four tests are unbased. Let the power functon of a test wth the acceptance regon K be defned as foows p K (D,r,n,p)=P G, S {(X, S) K c }, (3) where K c s the compement of K and D=dag(d 1,...,d s ). Das Gupta et a. (1964) proved that f K s convex n each row of X when S and the remanng rows of X are fxed and f t remans nvarant under a the transformatons defned n (), then the correspondng power functon s ncreasng n each component d,,...,s. Eaton and Perman (1974) notced that the acceptance regons K 1 of Roy s maxmum root test and K of the Lawey Hoteng trace test are convex n (X, S), and they reman nvarant under a the transformatons mentoned n (). Usng a resut of Mudhokar (1966), they proved that for an nvarant convex acceptance regon of ths type, the power functon s Schur-convex n ( `d 1,...,`d s ). Thus, for fxed ; s u=1 `d, the power functons of Roy s maxmum root

3 418 BHAUMIK AND SARKAR test and the Lawey Hoteng trace test ncrease as ( `d 1,...,`d s ) ncreases wth respect to the majorzaton parta orderng. The same monotoncty resut, however, cannot be estabshed for the kehood rato test or Pa s trace test usng the resut of Eaton and Perman (1974) as nether K 3 nor K 4 s a convex set n (X, S). Das Gupta and Perman (1973) proved that the power of the kehood rato test strcty decreases wth p and s. What s conjectured about these tests n the terature based on numerca studes (Fujkosh, 1970) s that for fxed trd=; s d ther power functons decrease as the rank of G decreases from s to 1. Ths artce provdes a parta theoretca support to ths conjecture for the kehood rato test. More specfcay, we prove theoretcay that the power functon of ths test, when ; s d s constant, attans ts mnmum when the rank of D s 1.. THE POWER FUNCTION OF THE LIKELIHOOD RATIO TEST Let us denote the kehood rato test statstc by U and the ower 100a % pont of the nu dstrbuton of U by U a (p,r,n). Let C o (D) denote the zona poynoma of D for the partton o=(k 1,...,k s ), k 1 \ \ k s \ 0, of the nteger k. Let Z s be mutuay ndependent random varabes, where,,...,s, Z = Beta5 n +1, r/+k 6 Beta5 n +1, r/6, =s+1,..., p. We denote the product < p Z by U p, o (r, n) and wrte U p, 0 (r, n) smpy as U p (r, n). Sarkar (1984) has proved that U E K U p, K (r, n), (4) where the expectaton s taken wth respect to K havng the foowng probabty mass functon at o P(o, D)=e C o(d/) trd/. (5) k! Let V s (r, n)=< s Z g, where condtonay gven K=o,Z g s are ndependenty dstrbuted as Beta[ n+r +1,k ],,..., s respectvey. It s assumed that Beta[n, 0]=1 wth probabty one. As defned before o=(k 1,k,...,k s ), where k 1 \ k \ k s \ 0, and k=; s k. If a the k s are not strcty postve then there exsts a postve nteger (1 [ <s),

4 LIKELIHOOD RATIO TEST FOR MANOVA 419 such that k 1 \ k \ \ k >0 and k +1 = =k s =0. From a property of the beta dstrbuton (Rao, 1973, p. 168), t foows that U V s (r, n) U p (r, n). (6) Now we w defne a sequence of random varabes Y g s from Z g s to construct a new random varabe V g wth the property that t s stochastcay arger than V s. To ths end, et us consder Y g Beta1 n+r +k +1, C j=1 k j,,,..., 1, (7) and then defne V g =Y g 1Z g,,...,, wth Y g 0 =1. In the foowng, the nequates between random varabes are referred to as stochastc nequates. Note that V g =Beta1 n+r +1, C k j j=1 k j [ Beta1 n+r +k +1, C =Y g,,..., 1. The second nequaty above hods from the fact that k +1 \ 1, = 1,,..., 1, and that a beta random varabe Beta(p, q) stochastcay ncreases wth p. Thus we have s V s =D Z g =D Z g j=1 [ Y g 1 D Z g =V g D Z g = =3 [ Y g D Z g =V g 3 D Z g =3 =4 [ [ V g. (8) In other words, we have V g n+r +1 Beta(, ; n+r +1 k )=Beta(,k) whch s stochastcay arger than V s (r, n). Aso note that V g s stochastcay smaer than V 1 Beta( n+r,k) as \ 1. Usng ; o C o (D)=(trD) k we

5 40 BHAUMIK AND SARKAR now see that the power functon, say p s (D), of the kehood rato test for MANOVA satsfes the foowng p s (D)=P(U [ U a (p,r,n)) =P(V s U p (r, n) [ U a (p,r,n)) \ P(V g U p (r, n) [ U a (p,r,n)) \ P(V 1 U p (r, n) [ U a (p,r,n)). = C k=0 C o e C o(d/) trd/ P(V k! 1 U p (r, n) [ U a (p,r,n)). trd/ (trd/)k = C e P(V k=0 k! 1 U p (r, n) [ U a (p,r,n)) =p 1 (trd). (9) Thus, the power functon of the kehood rato test for MANOVA, for fxed trd, attans ts mnmum at s=1. Ths proves our man resut. Remark 1. From a computatona pont of vew, the extreme rghthand sde of (9) s very usefu. It provdes a frst-hand approxmaton to the power functon of the kehood rato test for MANOVA whch, beng based on the Posson dstrbuton as opposed to the more compcated dstrbuton nvovng zona poynomas, coud be computed reatvey easy. It s mportant to pont out that n (9) we have ony proved that the power of the kehood rato test for MANOVA at D=(d 1,...,d s ) s more than that at D 0 =(trd,0,...,0). Athough D 0 majorzes D, the current resut cannot be generazed to make a cam that whenever any D s majorzed by D 0, the power of the kehood rato test for MANOVA at D s more than D 0, or n other words, the power functon s Schur-concave. Aso, the current technque does not work to study the smar property of the power functon of Pa s trace test. ACKNOWLEDGMENTS The authors are gratefu to two referees and an edtor for ther usefu comments and suggestons.

6 LIKELIHOOD RATIO TEST FOR MANOVA 41 REFERENCES 1. S. Das Gupta, T. W. Anderson, and G. S. Mudhokar, Monotoncty of the power functons of some tests of the mutvarate near hypothess, Ann. Math. Statst. 35 (1964), S. Das Gupta, and M. D. Perman, On the power of Wks U-test for MANOVA, J. Mutvarate Ana. 3 (1973), M. L. Eaton, and M. D. Perman, A monotoncty property of the power functons of some nvarant tests for MANOVA, Ann. Statst. 5 (1974), Y. Fujkosh, Asymptotc expansons of the dstrbutons of test statstcs n mutvarate statstcs, J. Sc. Hroshma Unv. Ser. A-I. 34 (1970), G. S. Mudhokar, The ntegra of an nvarant unmoda functon over an nvarant convex set- an nequaty an appcatons, Proc. Amer. Math. Soc. 17 (1966), R. J. Murhead, Aspects of Mutvarate Statstca Theory, Wey, New York, M. D. Perman, and I. Okn, Unbasedness of nvarant tests for MANOVA and other mutvarate probems, Ann. Statst. 6 (1980), C. R. Rao, Lnear Statstca Inference and Its Appcatons, second ed., Wey, New York, S. K. Sarkar, A note on the power of the kehood rato test for MANOVA, Sankhya, Ser. A 46 (1984),

Journal of Multivariate Analysis

Journal of Multivariate Analysis Journa of Mutvarate Anayss 3 (04) 74 96 Contents sts avaabe at ScenceDrect Journa of Mutvarate Anayss journa homepage: www.esever.com/ocate/jmva Hgh-dmensona sparse MANOVA T. Tony Ca a, Yn Xa b, a Department

More information

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks Shengyang Sun, Changyou Chen, Lawrence Carn Suppementary Matera: Learnng Structured Weght Uncertanty n Bayesan Neura Networks Shengyang Sun Changyou Chen Lawrence Carn Tsnghua Unversty Duke Unversty Duke

More information

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications Durban Watson for Testng the Lack-of-Ft of Polynomal Regresson Models wthout Replcatons Ruba A. Alyaf, Maha A. Omar, Abdullah A. Al-Shha ralyaf@ksu.edu.sa, maomar@ksu.edu.sa, aalshha@ksu.edu.sa Department

More information

First Year Examination Department of Statistics, University of Florida

First Year Examination Department of Statistics, University of Florida Frst Year Examnaton Department of Statstcs, Unversty of Florda May 7, 010, 8:00 am - 1:00 noon Instructons: 1. You have four hours to answer questons n ths examnaton.. You must show your work to receve

More information

Nested case-control and case-cohort studies

Nested case-control and case-cohort studies Outne: Nested case-contro and case-cohort studes Ørnuf Borgan Department of Mathematcs Unversty of Oso NORBIS course Unversty of Oso 4-8 December 217 1 Radaton and breast cancer data Nested case contro

More information

Strong Markov property: Same assertion holds for stopping times τ.

Strong Markov property: Same assertion holds for stopping times τ. Brownan moton Let X ={X t : t R + } be a real-valued stochastc process: a famlty of real random varables all defned on the same probablty space. Defne F t = nformaton avalable by observng the process up

More information

ON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION

ON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION European Journa of Mathematcs and Computer Scence Vo. No. 1, 2017 ON AUTOMATC CONTNUTY OF DERVATONS FOR BANACH ALGEBRAS WTH NVOLUTON Mohamed BELAM & Youssef T DL MATC Laboratory Hassan Unversty MORO CCO

More information

Integral Formula of Minkowski Type and New Characterization of the Wulff Shape

Integral Formula of Minkowski Type and New Characterization of the Wulff Shape Acta athematca Snca, Engsh Seres Apr., 2008, Vo. 24, No. 4, pp. 697 704 Pubshed onne: Apr 5, 2008 DOI: 0.007/s04-007-76-6 Http://www.Actaath.com Acta athematca Snca, Engsh Seres The Edtora Offce of AS

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

COXREG. Estimation (1)

COXREG. Estimation (1) COXREG Cox (972) frst suggested the modes n whch factors reated to fetme have a mutpcatve effect on the hazard functon. These modes are caed proportona hazards (PH) modes. Under the proportona hazards

More information

Optimum Selection Combining for M-QAM on Fading Channels

Optimum Selection Combining for M-QAM on Fading Channels Optmum Seecton Combnng for M-QAM on Fadng Channes M. Surendra Raju, Ramesh Annavajjaa and A. Chockangam Insca Semconductors Inda Pvt. Ltd, Bangaore-56000, Inda Department of ECE, Unversty of Caforna, San

More information

Interference Alignment and Degrees of Freedom Region of Cellular Sigma Channel

Interference Alignment and Degrees of Freedom Region of Cellular Sigma Channel 2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Interference Agnment and Degrees of Freedom Regon of Ceuar Sgma Channe Huaru Yn 1 Le Ke 2 Zhengdao Wang 2 1 WINLAB Dept of EEIS Unv. of Sc.

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights ACTA ET COMMENTATIONES UNIVERSITATIS TARTUENSIS DE MATHEMATICA Volume 7, Number 2, December 203 Avalable onlne at http://acutm.math.ut.ee A note on almost sure behavor of randomly weghted sums of φ-mxng

More information

Randić Energy and Randić Estrada Index of a Graph

Randić Energy and Randić Estrada Index of a Graph EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 5, No., 202, 88-96 ISSN 307-5543 www.ejpam.com SPECIAL ISSUE FOR THE INTERNATIONAL CONFERENCE ON APPLIED ANALYSIS AND ALGEBRA 29 JUNE -02JULY 20, ISTANBUL

More information

G : Statistical Mechanics

G : Statistical Mechanics G25.2651: Statstca Mechancs Notes for Lecture 11 I. PRINCIPLES OF QUANTUM STATISTICAL MECHANICS The probem of quantum statstca mechancs s the quantum mechanca treatment of an N-partce system. Suppose the

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed

More information

D hh ν. Four-body charm semileptonic decay. Jim Wiss University of Illinois

D hh ν. Four-body charm semileptonic decay. Jim Wiss University of Illinois Four-body charm semeptonc decay Jm Wss Unversty of Inos D hh ν 1 1. ector domnance. Expected decay ntensty 3. SU(3) apped to D s φν 4. Anaytc forms for form factors 5. Non-parametrc form factors 6. Future

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede We now reformulate the lnear Least Squares ethod n more general terms, sutable for (eventually extendng to the non-lnear case, and also

More information

QUARTERLY OF APPLIED MATHEMATICS

QUARTERLY OF APPLIED MATHEMATICS QUARTERLY OF APPLIED MATHEMATICS Voume XLI October 983 Number 3 DIAKOPTICS OR TEARING-A MATHEMATICAL APPROACH* By P. W. AITCHISON Unversty of Mantoba Abstract. The method of dakoptcs or tearng was ntroduced

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

A Unified Elementary Approach to the Dyson, Morris, Aomoto, and Forrester Constant Term Identities

A Unified Elementary Approach to the Dyson, Morris, Aomoto, and Forrester Constant Term Identities A Unfed Eementary Approach to the Dyson, Morrs, Aomoto, and Forrester Constant Term Identtes Ira M Gesse 1, Lun Lv, Guoce Xn 3, Yue Zhou 4 1 Department of Mathematcs Brandes Unversty, Watham, MA 0454-9110,

More information

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA RESEARCH ARTICLE MOELING FIXE OS BETTING FOR FUTURE EVENT PREICTION Weyun Chen eartment of Educatona Informaton Technoogy, Facuty of Educaton, East Chna Norma Unversty, Shangha, CHINA {weyun.chen@qq.com}

More information

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2 Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................

More information

Goodness of fit and Wilks theorem

Goodness of fit and Wilks theorem DRAFT 0.0 Glen Cowan 3 June, 2013 Goodness of ft and Wlks theorem Suppose we model data y wth a lkelhood L(µ) that depends on a set of N parameters µ = (µ 1,...,µ N ). Defne the statstc t µ ln L(µ) L(ˆµ),

More information

LOW-DENSITY Parity-Check (LDPC) codes have received

LOW-DENSITY Parity-Check (LDPC) codes have received IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 59, NO. 7, JULY 2011 1807 Successve Maxmzaton for Systematc Desgn of Unversay Capacty Approachng Rate-Compatbe Sequences of LDPC Code Ensembes over Bnary-Input

More information

The Jacobsthal and Jacobsthal-Lucas Numbers via Square Roots of Matrices

The Jacobsthal and Jacobsthal-Lucas Numbers via Square Roots of Matrices Internatonal Mathematcal Forum, Vol 11, 2016, no 11, 513-520 HIKARI Ltd, wwwm-hkarcom http://dxdoorg/1012988/mf20166442 The Jacobsthal and Jacobsthal-Lucas Numbers va Square Roots of Matrces Saadet Arslan

More information

Ph 219a/CS 219a. Exercises Due: Wednesday 23 October 2013

Ph 219a/CS 219a. Exercises Due: Wednesday 23 October 2013 1 Ph 219a/CS 219a Exercses Due: Wednesday 23 October 2013 1.1 How far apart are two quantum states? Consder two quantum states descrbed by densty operators ρ and ρ n an N-dmensonal Hlbert space, and consder

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

Computing MLE Bias Empirically

Computing MLE Bias Empirically Computng MLE Bas Emprcally Kar Wa Lm Australan atonal Unversty January 3, 27 Abstract Ths note studes the bas arses from the MLE estmate of the rate parameter and the mean parameter of an exponental dstrbuton.

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

The line method combined with spectral chebyshev for space-time fractional diffusion equation

The line method combined with spectral chebyshev for space-time fractional diffusion equation Apped and Computatona Mathematcs 014; 3(6): 330-336 Pubshed onne December 31, 014 (http://www.scencepubshnggroup.com/j/acm) do: 10.1164/j.acm.0140306.17 ISS: 3-5605 (Prnt); ISS: 3-5613 (Onne) The ne method

More information

The lower and upper bounds on Perron root of nonnegative irreducible matrices

The lower and upper bounds on Perron root of nonnegative irreducible matrices Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College

More information

Analysis of Bipartite Graph Codes on the Binary Erasure Channel

Analysis of Bipartite Graph Codes on the Binary Erasure Channel Anayss of Bpartte Graph Codes on the Bnary Erasure Channe Arya Mazumdar Department of ECE Unversty of Maryand, Coege Par ema: arya@umdedu Abstract We derve densty evouton equatons for codes on bpartte

More information

A finite difference method for heat equation in the unbounded domain

A finite difference method for heat equation in the unbounded domain Internatona Conerence on Advanced ectronc Scence and Technoogy (AST 6) A nte derence method or heat equaton n the unbounded doman a Quan Zheng and Xn Zhao Coege o Scence North Chna nversty o Technoogy

More information

KMT theory applied to approximations of SDE

KMT theory applied to approximations of SDE KMT theory apped to approxmatons of SD A. M. Dave September 18, 014 Abstract The dyadc method of Komós, Major and Tusnády s a powerfu way of constructng smutaneous norma approxmatons to a sequence of parta

More information

Randomness and Computation

Randomness and Computation Randomness and Computaton or, Randomzed Algorthms Mary Cryan School of Informatcs Unversty of Ednburgh RC 208/9) Lecture 0 slde Balls n Bns m balls, n bns, and balls thrown unformly at random nto bns usually

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS HCMC Unversty of Pedagogy Thong Nguyen Huu et al. A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS Thong Nguyen Huu and Hao Tran Van Department of mathematcs-nformaton,

More information

A Note on Test of Homogeneity Against Umbrella Scale Alternative Based on U-Statistics

A Note on Test of Homogeneity Against Umbrella Scale Alternative Based on U-Statistics J Stat Appl Pro No 3 93- () 93 NSP Journal of Statstcs Applcatons & Probablty --- An Internatonal Journal @ NSP Natural Scences Publshng Cor A Note on Test of Homogenety Aganst Umbrella Scale Alternatve

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Monica Purcaru and Nicoleta Aldea. Abstract

Monica Purcaru and Nicoleta Aldea. Abstract FILOMAT (Nš) 16 (22), 7 17 GENERAL CONFORMAL ALMOST SYMPLECTIC N-LINEAR CONNECTIONS IN THE BUNDLE OF ACCELERATIONS Monca Purcaru and Ncoeta Adea Abstract The am of ths paper 1 s to fnd the transformaton

More information

A DIMENSION-REDUCTION METHOD FOR STOCHASTIC ANALYSIS SECOND-MOMENT ANALYSIS

A DIMENSION-REDUCTION METHOD FOR STOCHASTIC ANALYSIS SECOND-MOMENT ANALYSIS A DIMESIO-REDUCTIO METHOD FOR STOCHASTIC AALYSIS SECOD-MOMET AALYSIS S. Rahman Department of Mechanca Engneerng and Center for Computer-Aded Desgn The Unversty of Iowa Iowa Cty, IA 52245 June 2003 OUTLIE

More information

Test for Intraclass Correlation Coefficient under Unequal Family Sizes

Test for Intraclass Correlation Coefficient under Unequal Family Sizes Journal of Modern Appled Statstcal Methods Volume Issue Artcle 9 --03 Test for Intraclass Correlaton Coeffcent under Unequal Famly Szes Madhusudan Bhandary Columbus State Unversty, Columbus, GA, bhandary_madhusudan@colstate.edu

More information

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b Int J Contemp Math Scences, Vol 3, 28, no 17, 819-827 A New Refnement of Jacob Method for Soluton of Lnear System Equatons AX=b F Naem Dafchah Department of Mathematcs, Faculty of Scences Unversty of Gulan,

More information

arxiv: v1 [cs.gt] 28 Mar 2017

arxiv: v1 [cs.gt] 28 Mar 2017 A Dstrbuted Nash qubrum Seekng n Networked Graphca Games Farzad Saehsadaghan, and Lacra Pave arxv:7009765v csgt 8 Mar 07 Abstract Ths paper consders a dstrbuted gossp approach for fndng a Nash equbrum

More information

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition) Count Data Models See Book Chapter 11 2 nd Edton (Chapter 10 1 st Edton) Count data consst of non-negatve nteger values Examples: number of drver route changes per week, the number of trp departure changes

More information

Chapter 7 Generalized and Weighted Least Squares Estimation. In this method, the deviation between the observed and expected values of

Chapter 7 Generalized and Weighted Least Squares Estimation. In this method, the deviation between the observed and expected values of Chapter 7 Generalzed and Weghted Least Squares Estmaton The usual lnear regresson model assumes that all the random error components are dentcally and ndependently dstrbuted wth constant varance. When

More information

Research Article H Estimates for Discrete-Time Markovian Jump Linear Systems

Research Article H Estimates for Discrete-Time Markovian Jump Linear Systems Mathematca Probems n Engneerng Voume 213 Artce ID 945342 7 pages http://dxdoorg/11155/213/945342 Research Artce H Estmates for Dscrete-Tme Markovan Jump Lnear Systems Marco H Terra 1 Gdson Jesus 2 and

More information

A Note on Bound for Jensen-Shannon Divergence by Jeffreys

A Note on Bound for Jensen-Shannon Divergence by Jeffreys OPEN ACCESS Conference Proceedngs Paper Entropy www.scforum.net/conference/ecea- A Note on Bound for Jensen-Shannon Dvergence by Jeffreys Takuya Yamano, * Department of Mathematcs and Physcs, Faculty of

More information

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics ECOOMICS 35*-A Md-Term Exam -- Fall Term 000 Page of 3 pages QUEE'S UIVERSITY AT KIGSTO Department of Economcs ECOOMICS 35* - Secton A Introductory Econometrcs Fall Term 000 MID-TERM EAM ASWERS MG Abbott

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

3. Stress-strain relationships of a composite layer

3. Stress-strain relationships of a composite layer OM PO I O U P U N I V I Y O F W N ompostes ourse 8-9 Unversty of wente ng. &ech... tress-stran reatonshps of a composte ayer - Laurent Warnet & emo Aerman.. tress-stran reatonshps of a composte ayer Introducton

More information

MATH 281A: Homework #6

MATH 281A: Homework #6 MATH 28A: Homework #6 Jongha Ryu Due date: November 8, 206 Problem. (Problem 2..2. Soluton. If X,..., X n Bern(p, then T = X s a complete suffcent statstc. Our target s g(p = p, and the nave guess suggested

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Y. Guo. A. Liu, T. Liu, Q. Ma UDC

Y. Guo. A. Liu, T. Liu, Q. Ma UDC UDC 517. 9 OSCILLATION OF A CLASS OF NONLINEAR PARTIAL DIFFERENCE EQUATIONS WITH CONTINUOUS VARIABLES* ОСЦИЛЯЦIЯ КЛАСУ НЕЛIНIЙНИХ ЧАСТКОВО РIЗНИЦЕВИХ РIВНЯНЬ З НЕПЕРЕРВНИМИ ЗМIННИМИ Y. Guo Graduate School

More information

DIOPHANTINE EQUATIONS WITH BINOMIAL COEFFICIENTS AND PERTURBATIONS OF SYMMETRIC BOOLEAN FUNCTIONS

DIOPHANTINE EQUATIONS WITH BINOMIAL COEFFICIENTS AND PERTURBATIONS OF SYMMETRIC BOOLEAN FUNCTIONS DIOPHANTINE EQUATIONS WITH BINOMIAL COEFFICIENTS AND PERTURBATIONS OF SYMMETRIC BOOLEAN FUNCTIONS FRANCIS N CASTRO, OSCAR E GONZÁLEZ, AND LUIS A MEDINA Abstract Ths work presents a study of perturbatons

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He

More information

Perron Vectors of an Irreducible Nonnegative Interval Matrix

Perron Vectors of an Irreducible Nonnegative Interval Matrix Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of

More information

Continuous Time Markov Chain

Continuous Time Markov Chain Contnuous Tme Markov Chan Hu Jn Department of Electroncs and Communcaton Engneerng Hanyang Unversty ERICA Campus Contents Contnuous tme Markov Chan (CTMC) Propertes of sojourn tme Relatons Transton probablty

More information

EXPANSIVE MAPPINGS. by W. R. Utz

EXPANSIVE MAPPINGS. by W. R. Utz Volume 3, 978 Pages 6 http://topology.auburn.edu/tp/ EXPANSIVE MAPPINGS by W. R. Utz Topology Proceedngs Web: http://topology.auburn.edu/tp/ Mal: Topology Proceedngs Department of Mathematcs & Statstcs

More information

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle Lower bounds for the Crossng Number of the Cartesan Product of a Vertex-transtve Graph wth a Cyce Junho Won MIT-PRIMES December 4, 013 Abstract. The mnmum number of crossngs for a drawngs of a gven graph

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory Proceedngs of the 2009 IEEE Internatona Conference on Systems Man and Cybernetcs San Antono TX USA - October 2009 Mutspectra Remote Sensng Image Cassfcaton Agorthm Based on Rough Set Theory Yng Wang Xaoyun

More information

However, since P is a symmetric idempotent matrix, of P are either 0 or 1 [Eigen-values

However, since P is a symmetric idempotent matrix, of P are either 0 or 1 [Eigen-values Fall 007 Soluton to Mdterm Examnaton STAT 7 Dr. Goel. [0 ponts] For the general lnear model = X + ε, wth uncorrelated errors havng mean zero and varance σ, suppose that the desgn matrx X s not necessarly

More information

Note On Some Identities of New Combinatorial Integers

Note On Some Identities of New Combinatorial Integers Apped Mathematcs & Informaton Scences 5(3 (20, 500-53 An Internatona Journa c 20 NSP Note On Some Identtes of New Combnatora Integers Adem Kııçman, Cenap Öze 2 and Ero Yımaz 3 Department of Mathematcs

More information

Estimation: Part 2. Chapter GREG estimation

Estimation: Part 2. Chapter GREG estimation Chapter 9 Estmaton: Part 2 9. GREG estmaton In Chapter 8, we have seen that the regresson estmator s an effcent estmator when there s a lnear relatonshp between y and x. In ths chapter, we generalzed the

More information

A nonparametric two-sample wald test of equality of variances

A nonparametric two-sample wald test of equality of variances Unversty of Wollongong Research Onlne Centre for Statstcal & Survey Methodology Workng Paper Seres Faculty of Engneerng and Informaton Scences 0 A nonparametrc two-sample wald test of equalty of varances

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

More information

Lecture 6/7 (February 10/12, 2014) DIRAC EQUATION. The non-relativistic Schrödinger equation was obtained by noting that the Hamiltonian 2

Lecture 6/7 (February 10/12, 2014) DIRAC EQUATION. The non-relativistic Schrödinger equation was obtained by noting that the Hamiltonian 2 P470 Lecture 6/7 (February 10/1, 014) DIRAC EQUATION The non-relatvstc Schrödnger equaton was obtaned by notng that the Hamltonan H = P (1) m can be transformed nto an operator form wth the substtutons

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Chapter 12 Analysis of Covariance

Chapter 12 Analysis of Covariance Chapter Analyss of Covarance Any scentfc experment s performed to know somethng that s unknown about a group of treatments and to test certan hypothess about the correspondng treatment effect When varablty

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

Chapter 6. Rotations and Tensors

Chapter 6. Rotations and Tensors Vector Spaces n Physcs 8/6/5 Chapter 6. Rotatons and ensors here s a speca knd of near transformaton whch s used to transforms coordnates from one set of axes to another set of axes (wth the same orgn).

More information

Appendix for An Efficient Ascending-Bid Auction for Multiple Objects: Comment For Online Publication

Appendix for An Efficient Ascending-Bid Auction for Multiple Objects: Comment For Online Publication Appendx for An Effcent Ascendng-Bd Aucton for Mutpe Objects: Comment For Onne Pubcaton Norak Okamoto The foowng counterexampe shows that sncere bddng by a bdders s not aways an ex post perfect equbrum

More information

An (almost) unbiased estimator for the S-Gini index

An (almost) unbiased estimator for the S-Gini index An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for

More information

Norm Bounds for a Transformed Activity Level. Vector in Sraffian Systems: A Dual Exercise

Norm Bounds for a Transformed Activity Level. Vector in Sraffian Systems: A Dual Exercise ppled Mathematcal Scences, Vol. 4, 200, no. 60, 2955-296 Norm Bounds for a ransformed ctvty Level Vector n Sraffan Systems: Dual Exercse Nkolaos Rodousaks Department of Publc dmnstraton, Panteon Unversty

More information

corresponding to those of Heegaard diagrams by the band moves

corresponding to those of Heegaard diagrams by the band moves Agebra transformatons of the fundamenta groups correspondng to those of Heegaard dagrams by the band moves By Shun HORIGUCHI Abstract. Ths paper gves the basc resut of [1](1997),.e., a hande sdng and a

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

On Finite Rank Perturbation of Diagonalizable Operators

On Finite Rank Perturbation of Diagonalizable Operators Functonal Analyss, Approxmaton and Computaton 6 (1) (2014), 49 53 Publshed by Faculty of Scences and Mathematcs, Unversty of Nš, Serba Avalable at: http://wwwpmfnacrs/faac On Fnte Rank Perturbaton of Dagonalzable

More information

Tail Dependence Comparison of Survival Marshall-Olkin Copulas

Tail Dependence Comparison of Survival Marshall-Olkin Copulas Tal Dependence Comparson of Survval Marshall-Olkn Copulas Hajun L Department of Mathematcs and Department of Statstcs Washngton State Unversty Pullman, WA 99164, U.S.A. lh@math.wsu.edu January 2006 Abstract

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

A generalization of Picard-Lindelof theorem/ the method of characteristics to systems of PDE

A generalization of Picard-Lindelof theorem/ the method of characteristics to systems of PDE A generazaton of Pcard-Lndeof theorem/ the method of characterstcs to systems of PDE Erfan Shachan b,a,1 a Department of Physcs, Unversty of Toronto, 60 St. George St., Toronto ON M5S 1A7, Canada b Department

More information

Discrete Mathematics

Discrete Mathematics Dscrete Mathematcs 30 (00) 48 488 Contents lsts avalable at ScenceDrect Dscrete Mathematcs journal homepage: www.elsever.com/locate/dsc The number of C 3 -free vertces on 3-partte tournaments Ana Paulna

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

The Geometry of Logit and Probit

The Geometry of Logit and Probit The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.

More information

Lecture 6 More on Complete Randomized Block Design (RBD)

Lecture 6 More on Complete Randomized Block Design (RBD) Lecture 6 More on Complete Randomzed Block Desgn (RBD) Multple test Multple test The multple comparsons or multple testng problem occurs when one consders a set of statstcal nferences smultaneously. For

More information

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force.

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force. The fundamental prncples of classcal mechancs were lad down by Galleo and Newton n the 16th and 17th centures. In 1686, Newton wrote the Prncpa where he gave us three laws of moton, one law of gravty,

More information

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore Sesson Outlne Introducton to classfcaton problems and dscrete choce models. Introducton to Logstcs Regresson. Logstc functon and Logt functon. Maxmum Lkelhood Estmator (MLE) for estmaton of LR parameters.

More information

Signal Processing 142 (2018) Contents lists available at ScienceDirect. Signal Processing. journal homepage:

Signal Processing 142 (2018) Contents lists available at ScienceDirect. Signal Processing. journal homepage: Sgna Processng 142 (218) 32 329 Contents sts avaabe at ScenceDrect Sgna Processng ourna homepage: www.esever.com/ocate/sgpro Sepan-Bangs-type formuas the reated Msspecfed Cramér-Rao Bounds for Compex Eptcay

More information

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013 ISSN: 2277-375 Constructon of Trend Free Run Orders for Orthogonal rrays Usng Codes bstract: Sometmes when the expermental runs are carred out n a tme order sequence, the response can depend on the run

More information

Supplementary material: Margin based PU Learning. Matrix Concentration Inequalities

Supplementary material: Margin based PU Learning. Matrix Concentration Inequalities Supplementary materal: Margn based PU Learnng We gve the complete proofs of Theorem and n Secton We frst ntroduce the well-known concentraton nequalty, so the covarance estmator can be bounded Then we

More information

MLE and Bayesian Estimation. Jie Tang Department of Computer Science & Technology Tsinghua University 2012

MLE and Bayesian Estimation. Jie Tang Department of Computer Science & Technology Tsinghua University 2012 MLE and Bayesan Estmaton Je Tang Department of Computer Scence & Technology Tsnghua Unversty 01 1 Lnear Regresson? As the frst step, we need to decde how we re gong to represent the functon f. One example:

More information

Neural network-based athletics performance prediction optimization model applied research

Neural network-based athletics performance prediction optimization model applied research Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped

More information