Limit Theorems for Jacobi Ensembles

Size: px
Start display at page:

Download "Limit Theorems for Jacobi Ensembles"

Transcription

1 Limit Theorems for Jacobi Esembles Chiheo Kim Departmet of Mathematics MIT May 5th, 203 Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, 203 / 9

2 Jacobi Esembles A set of radom variables is a (complex, β = 2) Jacobi esemble if that ca be described i the followig equivalet ways: for parameters (, m, m 2 ), (λ,..., λ ) [0, ] with joit pdf C,m,m 2 i<j λ i λ j 2 i= λ m i ( λ i ) m 2. Eigevalues of matrix X = AA /(AA + BB ) where A, B are matrices of size m ad m 2 with i.i.d. stadard (complex) Gaussia radom variables. Equivaletly, it is the squared geeralized sigular values of the pair A, B. Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

3 Jacobi Esembles A set of radom variables is a (complex, β = 2) Jacobi esemble if that ca be described i the followig equivalet ways: for parameters (, m, m 2 ), Eigevalues of a radom matrix of the form π ππ where π ad π are idepedet (m + m 2 ) (m + m 2 ) radom orthogoal projectios of raks ad m, whose distributios are ivariat uder uitary cojugatio. Eigevalues of U U where U is the m upper-left corer of a (m + m 2 ) (m + m 2 ) Haar (uitary) matrix. Log-gas model, etc. Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

4 Jacobi Esembles Ca geeralize to ay β > 0. CS values of certai bidiagoal matrix. (Edelma & Sutto 2003) Eigevalues of certai tridiagoal matrix. (Killip & Neciu 2004) Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

5 Jacobi Esembles Let c s s c B β = c s 2 s 2 c 2 c 2 s s c c with ad s i = c 2 i Beta( β 2 (m + i), β 2 (m 2 + i)) c j 2 Beta( β 2 j, β 2 (m + m j)), c 2i =, s j = c j 2. The, the eigevalues of B β B T β has same distributio as Jacobi esemble. Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

6 Limit theorems If we sample values from a certai distributio, the how would the average look like? Idepedet radom variables: Law of large umbers. X X µ Hermite esembles (GOE, GUE,... ) : Wiger s semicircle law δ λi 2 x π 2 dx i= Laguerre esembles : Marcheko-Pastur law (x γ )(γ + x) δ λi [γ,γ +] dx 2πγx i= Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

7 Limit theorems How about Jacobi esembles? Let parameters be (, m, m 2 ).. If m + m 2 2 = o(), the i= δ λi π x( x) dx. 2. If m / p ad m 2 / q ad p + q > 2, the δ λi p + q (λ+ x)(x λ ) 2π x( x) [λ,λ +]dx, where i= ( p λ ± = p + q ( p + q ) ± p + q ( ) 2 p p + q ). Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

8 Limit theorems How about Jacobi esembles? Let parameters be (, m, m 2 ). 3. If m + m 2 2 = ω() ad if m m +m 2 2 λ, the δ λi δ λ. i= (Every covergece here is i probability.) We ca say more about the third case if we scale appropriately. Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

9 Limit theorems Theorem Assume that β 2m γ (0, ], ad m 2 = ω( 2 ). The, i= δ m 2 λ i c f γ (cx)dx weakly, where c = 2γ/β ad f γ is the desity fuctio of Marcheko-Pastur law with parameter γ, i.e., f γ (x) = (x γ )(γ + x) 2πγx [γ+,γ ] ad γ ± = ( γ ± ) 2. Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

10 Fluctuatios Oe may thik about the deviatio. For example, Radom variables: Cetral limit theorem. β-hermite esemble: Arcsi law. For ay polyomial φ, ( ) 2 φ(λ i ) = φ(x)dσ(x) + β φ(x)dµ H (x) + o(/), i= where σ has semicircle distributio, ad dµ H = 4 δ + 4 δ β-laguerre esemble: Similar, we have dµ L = 4 δ γ + 4 δ γ + dx 2π x 2. dx 2π (x γ )(γ + x). Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

11 Fluctuatios I the case of truly Jacobi esemble, we ca calculate the deviatio. Theorem If m / p ad m 2 / q, the for ay polyomial φ, i= λ+ ( ) 2 φ(λ i ) = φ(x)dµ(x) + λ β φ(x)dµ J (x) + o(/), where µ is the limitig distributio, ad dµ J = 4 δ λ + 4 δ dx λ + 2π (x λ )(λ + x) Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

12 Refereces I. Dumitriu ad A. Edelma. Global spectrum uctuatios for the β-hermite ad β-laguerre esembles via matrix models. J. Math. Phys. 47 (2006), o. 6, , 36. MR (2007e:8204) 2 T. Jiag. Approximatio of Haar Distributed Matrices ad Limitig Distributios of Eigevalues of Jacobi Esembles. Probability Theory ad Related Fields, 44() (2009), I. Dumitriu ad E. Paquette. Global Fluctuatios for Liear Statistics of β-jacobi Esemble. Radom Matrices: Theory Appl. 0 (202), Chiheo Kim Limit Theorems for Jacobi Esembles May 5th, / 9

Random Matrices with Blocks of Intermediate Scale Strongly Correlated Band Matrices

Random Matrices with Blocks of Intermediate Scale Strongly Correlated Band Matrices Radom Matrices with Blocks of Itermediate Scale Strogly Correlated Bad Matrices Jiayi Tog Advisor: Dr. Todd Kemp May 30, 07 Departmet of Mathematics Uiversity of Califoria, Sa Diego Cotets Itroductio Notatio

More information

arxiv: v1 [math.pr] 11 Nov 2009

arxiv: v1 [math.pr] 11 Nov 2009 Limit Theorems for Beta-Jacobi Esembles Tiefeg Jiag arxiv:09.6v [math.pr] Nov 009 Abstract For a β-jacobi esemble determied by parameters a,a ad, uder the restrictio that the three parameters go to ifiity

More information

CHAPTER 3. GOE and GUE

CHAPTER 3. GOE and GUE CHAPTER 3 GOE ad GUE We quicly recall that a GUE matrix ca be defied i the followig three equivalet ways. We leave it to the reader to mae the three aalogous statemets for GOE. I the previous chapters,

More information

18.S096: Homework Problem Set 1 (revised)

18.S096: Homework Problem Set 1 (revised) 8.S096: Homework Problem Set (revised) Topics i Mathematics of Data Sciece (Fall 05) Afoso S. Badeira Due o October 6, 05 Exteded to: October 8, 05 This homework problem set is due o October 6, at the

More information

Probability 2 - Notes 10. Lemma. If X is a random variable and g(x) 0 for all x in the support of f X, then P(g(X) 1) E[g(X)].

Probability 2 - Notes 10. Lemma. If X is a random variable and g(x) 0 for all x in the support of f X, then P(g(X) 1) E[g(X)]. Probability 2 - Notes 0 Some Useful Iequalities. Lemma. If X is a radom variable ad g(x 0 for all x i the support of f X, the P(g(X E[g(X]. Proof. (cotiuous case P(g(X Corollaries x:g(x f X (xdx x:g(x

More information

Lecture 7: Properties of Random Samples

Lecture 7: Properties of Random Samples Lecture 7: Properties of Radom Samples 1 Cotiued From Last Class Theorem 1.1. Let X 1, X,...X be a radom sample from a populatio with mea µ ad variace σ

More information

The variance of a sum of independent variables is the sum of their variances, since covariances are zero. Therefore. V (xi )= n n 2 σ2 = σ2.

The variance of a sum of independent variables is the sum of their variances, since covariances are zero. Therefore. V (xi )= n n 2 σ2 = σ2. SAMPLE STATISTICS A radom sample x 1,x,,x from a distributio f(x) is a set of idepedetly ad idetically variables with x i f(x) for all i Their joit pdf is f(x 1,x,,x )=f(x 1 )f(x ) f(x )= f(x i ) The sample

More information

Exercise 4.3 Use the Continuity Theorem to prove the Cramér-Wold Theorem, Theorem. (1) φ a X(1).

Exercise 4.3 Use the Continuity Theorem to prove the Cramér-Wold Theorem, Theorem. (1) φ a X(1). Assigmet 7 Exercise 4.3 Use the Cotiuity Theorem to prove the Cramér-Wold Theorem, Theorem 4.12. Hit: a X d a X implies that φ a X (1) φ a X(1). Sketch of solutio: As we poited out i class, the oly tricky

More information

Asymptotic Results for the Linear Regression Model

Asymptotic Results for the Linear Regression Model Asymptotic Results for the Liear Regressio Model C. Fli November 29, 2000 1. Asymptotic Results uder Classical Assumptios The followig results apply to the liear regressio model y = Xβ + ε, where X is

More information

Ref. Gallager, Stochastic Processes. Notation a vector. All vectors are row vectors. k k. jωx. Φ joint chacteristic function of.

Ref. Gallager, Stochastic Processes. Notation a vector. All vectors are row vectors. k k. jωx. Φ joint chacteristic function of. Gaussia Radom ector Ref. Gallager, Stochastic Processes. Notatio a vector. All vectors are row vectors a a aa ω matrix scalar matrix ( ) ( ) -dim radom vector,, -dim real vector ω,, ω Φ oit chacteristic

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

The Entries of Circular Orthogonal Ensembles. Tiefeng Jiang 1

The Entries of Circular Orthogonal Ensembles. Tiefeng Jiang 1 The Etries of Circular Orthogoal Esembles Tiefeg Jiag 1 Abstract. Let V = v ij be a circular orthogoal esemble. I this paper, for 1 m o / log, we give a boud for the tail probability of max 1 i,j m v ij

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statistic ad Radom Samples A parameter is a umber that describes the populatio. It is a fixed umber, but i practice we do ot kow its value. A statistic is a fuctio of the sample data, i.e.,

More information

Chi-squared tests Math 6070, Spring 2014

Chi-squared tests Math 6070, Spring 2014 Chi-squared tests Math 6070, Sprig 204 Davar Khoshevisa Uiversity of Utah March, 204 Cotets MLE for goodess-of fit 2 2 The Multivariate ormal distributio 3 3 Cetral limit theorems 5 4 Applicatio to goodess-of-fit

More information

MA Advanced Econometrics: Properties of Least Squares Estimators

MA Advanced Econometrics: Properties of Least Squares Estimators MA Advaced Ecoometrics: Properties of Least Squares Estimators Karl Whela School of Ecoomics, UCD February 5, 20 Karl Whela UCD Least Squares Estimators February 5, 20 / 5 Part I Least Squares: Some Fiite-Sample

More information

7.1 Convergence of sequences of random variables

7.1 Convergence of sequences of random variables Chapter 7 Limit Theorems Throughout this sectio we will assume a probability space (, F, P), i which is defied a ifiite sequece of radom variables (X ) ad a radom variable X. The fact that for every ifiite

More information

Factor Analysis. Lecture 10: Factor Analysis and Principal Component Analysis. Sam Roweis

Factor Analysis. Lecture 10: Factor Analysis and Principal Component Analysis. Sam Roweis Lecture 10: Factor Aalysis ad Pricipal Compoet Aalysis Sam Roweis February 9, 2004 Whe we assume that the subspace is liear ad that the uderlyig latet variable has a Gaussia distributio we get a model

More information

Chi-Squared Tests Math 6070, Spring 2006

Chi-Squared Tests Math 6070, Spring 2006 Chi-Squared Tests Math 6070, Sprig 2006 Davar Khoshevisa Uiversity of Utah February XXX, 2006 Cotets MLE for Goodess-of Fit 2 2 The Multiomial Distributio 3 3 Applicatio to Goodess-of-Fit 6 3 Testig for

More information

Lecture 01: the Central Limit Theorem. 1 Central Limit Theorem for i.i.d. random variables

Lecture 01: the Central Limit Theorem. 1 Central Limit Theorem for i.i.d. random variables CSCI-B609: A Theorist s Toolkit, Fall 06 Aug 3 Lecture 0: the Cetral Limit Theorem Lecturer: Yua Zhou Scribe: Yua Xie & Yua Zhou Cetral Limit Theorem for iid radom variables Let us say that we wat to aalyze

More information

CMSE 820: Math. Foundations of Data Sci.

CMSE 820: Math. Foundations of Data Sci. Lecture 17 8.4 Weighted path graphs Take from [10, Lecture 3] As alluded to at the ed of the previous sectio, we ow aalyze weighted path graphs. To that ed, we prove the followig: Theorem 6 (Fiedler).

More information

Central Limit Theorem using Characteristic functions

Central Limit Theorem using Characteristic functions Cetral Limit Theorem usig Characteristic fuctios RogXi Guo MAT 477 Jauary 20, 2014 RogXi Guo (2014 Cetral Limit Theorem usig Characteristic fuctios Jauary 20, 2014 1 / 15 Itroductio study a radom variable

More information

Distribution of Random Samples & Limit theorems

Distribution of Random Samples & Limit theorems STAT/MATH 395 A - PROBABILITY II UW Witer Quarter 2017 Néhémy Lim Distributio of Radom Samples & Limit theorems 1 Distributio of i.i.d. Samples Motivatig example. Assume that the goal of a study is to

More information

7.1 Convergence of sequences of random variables

7.1 Convergence of sequences of random variables Chapter 7 Limit theorems Throughout this sectio we will assume a probability space (Ω, F, P), i which is defied a ifiite sequece of radom variables (X ) ad a radom variable X. The fact that for every ifiite

More information

Quick Review of Probability

Quick Review of Probability Quick Review of Probability Berli Che Departmet of Computer Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Refereces: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chapter & Teachig Material.

More information

Quick Review of Probability

Quick Review of Probability Quick Review of Probability Berli Che Departmet of Computer Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Refereces: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chapter 2 & Teachig

More information

APPLIED MULTIVARIATE ANALYSIS

APPLIED MULTIVARIATE ANALYSIS ALIED MULTIVARIATE ANALYSIS FREQUENTLY ASKED QUESTIONS AMIT MITRA & SHARMISHTHA MITRA DEARTMENT OF MATHEMATICS & STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANUR X = X X X [] The variace covariace atrix

More information

4. Partial Sums and the Central Limit Theorem

4. Partial Sums and the Central Limit Theorem 1 of 10 7/16/2009 6:05 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 4. Partial Sums ad the Cetral Limit Theorem The cetral limit theorem ad the law of large umbers are the two fudametal theorems

More information

Eigenvalue variance bounds for covariance matrices

Eigenvalue variance bounds for covariance matrices Eigevalue variace bouds for covariace matrices S. Dallaporta arxiv:1309.6265v1 [math.pr] 24 Sep 2013 Uiversity of Toulouse ad CMLA, ENS Cacha Abstract. This work is cocered with fiite rage bouds o the

More information

Lecture 20: Multivariate convergence and the Central Limit Theorem

Lecture 20: Multivariate convergence and the Central Limit Theorem Lecture 20: Multivariate covergece ad the Cetral Limit Theorem Covergece i distributio for radom vectors Let Z,Z 1,Z 2,... be radom vectors o R k. If the cdf of Z is cotiuous, the we ca defie covergece

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Statistical Theory; Why is the Gaussian Distribution so popular?

Statistical Theory; Why is the Gaussian Distribution so popular? Statistical Theory; Why is the Gaussia Distributio so popular? Rob Nicholls MRC LMB Statistics Course 2014 Cotets Cotiuous Radom Variables Expectatio ad Variace Momets The Law of Large Numbers (LLN) The

More information

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002 ECE 330:541, Stochastic Sigals ad Systems Lecture Notes o Limit Theorems from robability Fall 00 I practice, there are two ways we ca costruct a ew sequece of radom variables from a old sequece of radom

More information

STURM SEQUENCES AND RANDOM EIGENVALUE DISTRIBUTIONS

STURM SEQUENCES AND RANDOM EIGENVALUE DISTRIBUTIONS STURM SEQUENCES AND RANDOM EIGENVALUE DISTRIBUTIONS JAMES T. ALBRECHT, CY P. CHAN, AND ALAN EDELMAN Abstract. This paper proposes that the study of Sturm sequeces is ivaluable i the umerical computatio

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 Fuctioal Law of Large Numbers. Costructio of the Wieer Measure Cotet. 1. Additioal techical results o weak covergece

More information

Large Sample Theory. Convergence. Central Limit Theorems Asymptotic Distribution Delta Method. Convergence in Probability Convergence in Distribution

Large Sample Theory. Convergence. Central Limit Theorems Asymptotic Distribution Delta Method. Convergence in Probability Convergence in Distribution Large Sample Theory Covergece Covergece i Probability Covergece i Distributio Cetral Limit Theorems Asymptotic Distributio Delta Method Covergece i Probability A sequece of radom scalars {z } = (z 1,z,

More information

arxiv: v2 [math.pr] 24 Jul 2017

arxiv: v2 [math.pr] 24 Jul 2017 Mod-Gaussia covergece for radom determiats ad radom characteristic polyomials M. Dal Borgo, E. Hovhaisya, A. Rouault July 5, 7 arxiv:77.449v [math.pr] 4 Jul 7 Abstract The aim of this paper is to give

More information

Last time: Moments of the Poisson distribution from its generating function. Example: Using telescope to measure intensity of an object

Last time: Moments of the Poisson distribution from its generating function. Example: Using telescope to measure intensity of an object 6.3 Stochastic Estimatio ad Cotrol, Fall 004 Lecture 7 Last time: Momets of the Poisso distributio from its geeratig fuctio. Gs () e dg µ e ds dg µ ( s) µ ( s) µ ( s) µ e ds dg X µ ds X s dg dg + ds ds

More information

Asymptotics of random unitaries

Asymptotics of random unitaries BUTE Istitute of Mathematics Departmet for Aalysis Júlia Réffy Asymptotics of radom uitaries PhD thesis Supervisor: Dées Petz Professor, Doctor of the Mathematical Scieces 005 Cotets Itroductio 4 Radom

More information

Random matrix theory. Manjunath Krishnapur. Indian Institute of Science, Bangalore

Random matrix theory. Manjunath Krishnapur. Indian Institute of Science, Bangalore Radom matrix theory Majuath Krishapur Idia Istitute of Sciece, Bagalore October 27, 207 Cotets The simplest o-trivial matrices 8 Jacobi matrices.................................... 8 The -dimesioal discrete

More information

Stat 421-SP2012 Interval Estimation Section

Stat 421-SP2012 Interval Estimation Section Stat 41-SP01 Iterval Estimatio Sectio 11.1-11. We ow uderstad (Chapter 10) how to fid poit estimators of a ukow parameter. o However, a poit estimate does ot provide ay iformatio about the ucertaity (possible

More information

Gaussian Approximation of the Distribution of Strongly Repelling Particles on the Unit Circle

Gaussian Approximation of the Distribution of Strongly Repelling Particles on the Unit Circle Gaussia Approximatio of the Distributio of Strogly Repellig Particles o the Uit Circle Alexader Soshiov, Yuayua Xu November 0, 07 Abstract I this paper, we cosider a strogly-repellig model of ordered particles

More information

Expectation and Variance of a random variable

Expectation and Variance of a random variable Chapter 11 Expectatio ad Variace of a radom variable The aim of this lecture is to defie ad itroduce mathematical Expectatio ad variace of a fuctio of discrete & cotiuous radom variables ad the distributio

More information

STA Object Data Analysis - A List of Projects. January 18, 2018

STA Object Data Analysis - A List of Projects. January 18, 2018 STA 6557 Jauary 8, 208 Object Data Aalysis - A List of Projects. Schoeberg Mea glaucomatous shape chages of the Optic Nerve Head regio i aimal models 2. Aalysis of VW- Kedall ati-mea shapes with a applicatio

More information

17. Joint distributions of extreme order statistics Lehmann 5.1; Ferguson 15

17. Joint distributions of extreme order statistics Lehmann 5.1; Ferguson 15 17. Joit distributios of extreme order statistics Lehma 5.1; Ferguso 15 I Example 10., we derived the asymptotic distributio of the maximum from a radom sample from a uiform distributio. We did this usig

More information

Lecture 19. sup y 1,..., yn B d n

Lecture 19. sup y 1,..., yn B d n STAT 06A: Polyomials of adom Variables Lecture date: Nov Lecture 19 Grothedieck s Iequality Scribe: Be Hough The scribes are based o a guest lecture by ya O Doell. I this lecture we prove Grothedieck s

More information

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d Liear regressio Daiel Hsu (COMS 477) Maximum likelihood estimatio Oe of the simplest liear regressio models is the followig: (X, Y ),..., (X, Y ), (X, Y ) are iid radom pairs takig values i R d R, ad Y

More information

Basics of Probability Theory (for Theory of Computation courses)

Basics of Probability Theory (for Theory of Computation courses) Basics of Probability Theory (for Theory of Computatio courses) Oded Goldreich Departmet of Computer Sciece Weizma Istitute of Sciece Rehovot, Israel. oded.goldreich@weizma.ac.il November 24, 2008 Preface.

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Introducing a Novel Bivariate Generalized Skew-Symmetric Normal Distribution

Introducing a Novel Bivariate Generalized Skew-Symmetric Normal Distribution Joural of mathematics ad computer Sciece 7 (03) 66-7 Article history: Received April 03 Accepted May 03 Available olie Jue 03 Itroducig a Novel Bivariate Geeralized Skew-Symmetric Normal Distributio Behrouz

More information

Global spectrum fluctuations for the β-hermite and β-laguerre ensembles via matrix models

Global spectrum fluctuations for the β-hermite and β-laguerre ensembles via matrix models Global spectrum fluctuatios for the β-hermite ad β-laguerre esembles via matrix models Ioaa Dumitriu ad Ala Edelma October 0, 005 Abstract We study the global spectrum fluctuatios for β-hermite ad β-laguerre

More information

Joint Probability Distributions and Random Samples. Jointly Distributed Random Variables. Chapter { }

Joint Probability Distributions and Random Samples. Jointly Distributed Random Variables. Chapter { } UCLA STAT A Applied Probability & Statistics for Egieers Istructor: Ivo Diov, Asst. Prof. I Statistics ad Neurology Teachig Assistat: Neda Farziia, UCLA Statistics Uiversity of Califoria, Los Ageles, Sprig

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall Midterm Solutions

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall Midterm Solutions MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.65/5.070J Fall 0 Midterm Solutios Problem Suppose a radom variable X is such that P(X > ) = 0 ad P(X > E) > 0 for every E > 0. Recall that the large deviatios rate

More information

Iterative Techniques for Solving Ax b -(3.8). Assume that the system has a unique solution. Let x be the solution. Then x A 1 b.

Iterative Techniques for Solving Ax b -(3.8). Assume that the system has a unique solution. Let x be the solution. Then x A 1 b. Iterative Techiques for Solvig Ax b -(8) Cosider solvig liear systems of them form: Ax b where A a ij, x x i, b b i Assume that the system has a uique solutio Let x be the solutio The x A b Jacobi ad Gauss-Seidel

More information

Random Matrices with Complex Gaussian Entries

Random Matrices with Complex Gaussian Entries adom Matrices with Complex Gaussia Etries evised versio, April 3 U. Haagerup ad S. Thorbjørse Itroductio adom matrices has bee a importat tool i statistics sice 198 ad i physics sice 1955 startig with

More information

Lecture 6: Coupon Collector s problem

Lecture 6: Coupon Collector s problem Radomized Algorithms Lecture 6: Coupo Collector s problem Sotiris Nikoletseas Professor CEID - ETY Course 2017-2018 Sotiris Nikoletseas, Professor Radomized Algorithms - Lecture 6 1 / 16 Variace: key features

More information

Mathematics 170B Selected HW Solutions.

Mathematics 170B Selected HW Solutions. Mathematics 17B Selected HW Solutios. F 4. Suppose X is B(,p). (a)fidthemometgeeratigfuctiom (s)of(x p)/ p(1 p). Write q = 1 p. The MGF of X is (pe s + q), sice X ca be writte as the sum of idepedet Beroulli

More information

Matrix Representation of Data in Experiment

Matrix Representation of Data in Experiment Matrix Represetatio of Data i Experimet Cosider a very simple model for resposes y ij : y ij i ij, i 1,; j 1,,..., (ote that for simplicity we are assumig the two () groups are of equal sample size ) Y

More information

Hauptman and Karle Joint and Conditional Probability Distributions. Robert H. Blessing, HWI/UB Structural Biology Department, January 2003 ( )

Hauptman and Karle Joint and Conditional Probability Distributions. Robert H. Blessing, HWI/UB Structural Biology Department, January 2003 ( ) Hauptma ad Karle Joit ad Coditioal Probability Distributios Robert H Blessig HWI/UB Structural Biology Departmet Jauary 00 ormalized crystal structure factors are defied by E h = F h F h = f a hexp ihi

More information

AMS570 Lecture Notes #2

AMS570 Lecture Notes #2 AMS570 Lecture Notes # Review of Probability (cotiued) Probability distributios. () Biomial distributio Biomial Experimet: ) It cosists of trials ) Each trial results i of possible outcomes, S or F 3)

More information

Entropy Rates and Asymptotic Equipartition

Entropy Rates and Asymptotic Equipartition Chapter 29 Etropy Rates ad Asymptotic Equipartitio Sectio 29. itroduces the etropy rate the asymptotic etropy per time-step of a stochastic process ad shows that it is well-defied; ad similarly for iformatio,

More information

LECTURE 8: ASYMPTOTICS I

LECTURE 8: ASYMPTOTICS I LECTURE 8: ASYMPTOTICS I We are iterested i the properties of estimators as. Cosider a sequece of radom variables {, X 1}. N. M. Kiefer, Corell Uiversity, Ecoomics 60 1 Defiitio: (Weak covergece) A sequece

More information

Lectures 6 7 : Marchenko-Pastur Law

Lectures 6 7 : Marchenko-Pastur Law Fall 2009 MATH 833 Random Matrices B. Valkó Lectures 6 7 : Marchenko-Pastur Law Notes prepared by: A. Ganguly We will now turn our attention to rectangular matrices. Let X = (X 1, X 2,..., X n ) R p n

More information

Lecture 19: Convergence

Lecture 19: Convergence Lecture 19: Covergece Asymptotic approach I statistical aalysis or iferece, a key to the success of fidig a good procedure is beig able to fid some momets ad/or distributios of various statistics. I may

More information

Statistical Inference Based on Extremum Estimators

Statistical Inference Based on Extremum Estimators T. Rotheberg Fall, 2007 Statistical Iferece Based o Extremum Estimators Itroductio Suppose 0, the true value of a p-dimesioal parameter, is kow to lie i some subset S R p : Ofte we choose to estimate 0

More information

Mi-Hwa Ko and Tae-Sung Kim

Mi-Hwa Ko and Tae-Sung Kim J. Korea Math. Soc. 42 2005), No. 5, pp. 949 957 ALMOST SURE CONVERGENCE FOR WEIGHTED SUMS OF NEGATIVELY ORTHANT DEPENDENT RANDOM VARIABLES Mi-Hwa Ko ad Tae-Sug Kim Abstract. For weighted sum of a sequece

More information

arxiv:math.pr/ v1 27 Apr 2005

arxiv:math.pr/ v1 27 Apr 2005 Poisso Statistics for the Largest Eigevalues i Radom Matrix Esembles arxiv:math.pr/0504562 v 27 Apr 2005 Alexader Soshikov Uiversity of Califoria at Davis Departmet of Mathematics Davis, CA 9566, USA soshiko@math.ucdavis.edu

More information

arxiv: v1 [math.pr] 13 Oct 2011

arxiv: v1 [math.pr] 13 Oct 2011 A tail iequality for quadratic forms of subgaussia radom vectors Daiel Hsu, Sham M. Kakade,, ad Tog Zhag 3 arxiv:0.84v math.pr] 3 Oct 0 Microsoft Research New Eglad Departmet of Statistics, Wharto School,

More information

Statistical and Mathematical Methods DS-GA 1002 December 8, Sample Final Problems Solutions

Statistical and Mathematical Methods DS-GA 1002 December 8, Sample Final Problems Solutions Statistical ad Mathematical Methods DS-GA 00 December 8, 05. Short questios Sample Fial Problems Solutios a. Ax b has a solutio if b is i the rage of A. The dimesio of the rage of A is because A has liearly-idepedet

More information

This section is optional.

This section is optional. 4 Momet Geeratig Fuctios* This sectio is optioal. The momet geeratig fuctio g : R R of a radom variable X is defied as g(t) = E[e tx ]. Propositio 1. We have g () (0) = E[X ] for = 1, 2,... Proof. Therefore

More information

Week 10. f2 j=2 2 j k ; j; k 2 Zg is an orthonormal basis for L 2 (R). This function is called mother wavelet, which can be often constructed

Week 10. f2 j=2 2 j k ; j; k 2 Zg is an orthonormal basis for L 2 (R). This function is called mother wavelet, which can be often constructed Wee 0 A Itroductio to Wavelet regressio. De itio: Wavelet is a fuctio such that f j= j ; j; Zg is a orthoormal basis for L (R). This fuctio is called mother wavelet, which ca be ofte costructed from father

More information

Lecture 24: Variable selection in linear models

Lecture 24: Variable selection in linear models Lecture 24: Variable selectio i liear models Cosider liear model X = Z β + ε, β R p ad Varε = σ 2 I. Like the LSE, the ridge regressio estimator does ot give 0 estimate to a compoet of β eve if that compoet

More information

HOMEWORK I: PREREQUISITES FROM MATH 727

HOMEWORK I: PREREQUISITES FROM MATH 727 HOMEWORK I: PREREQUISITES FROM MATH 727 Questio. Let X, X 2,... be idepedet expoetial radom variables with mea µ. (a) Show that for Z +, we have EX µ!. (b) Show that almost surely, X + + X (c) Fid the

More information

Chapter 6 Principles of Data Reduction

Chapter 6 Principles of Data Reduction Chapter 6 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 0 Chapter 6 Priciples of Data Reductio Sectio 6. Itroductio Goal: To summarize or reduce the data X, X,, X to get iformatio about a

More information

Eigenvalues of Large Sample Covariance Matrices of Spiked Population Models arxiv:math/ v1 [math.st] 12 Aug 2004

Eigenvalues of Large Sample Covariance Matrices of Spiked Population Models arxiv:math/ v1 [math.st] 12 Aug 2004 Eigevalues of Large Sample Covariace Matrices of Spiked Populatio Models arxiv:math/040865v [math.st] 2 Aug 2004 Jiho Baik ad Jack W. Silverstei July 27, 2004 Abstract We cosider a spiked populatio model,

More information

Partial match queries: a limit process

Partial match queries: a limit process Partial match queries: a limit process Nicolas Brouti Ralph Neiiger Heig Sulzbach Partial match queries: a limit process 1 / 17 Searchig geometric data ad quadtrees 1 Partial match queries: a limit process

More information

Asymptotic distribution of products of sums of independent random variables

Asymptotic distribution of products of sums of independent random variables Proc. Idia Acad. Sci. Math. Sci. Vol. 3, No., May 03, pp. 83 9. c Idia Academy of Scieces Asymptotic distributio of products of sums of idepedet radom variables YANLING WANG, SUXIA YAO ad HONGXIA DU ollege

More information

Lecture 2: Poisson Sta*s*cs Probability Density Func*ons Expecta*on and Variance Es*mators

Lecture 2: Poisson Sta*s*cs Probability Density Func*ons Expecta*on and Variance Es*mators Lecture 2: Poisso Sta*s*cs Probability Desity Fuc*os Expecta*o ad Variace Es*mators Biomial Distribu*o: P (k successes i attempts) =! k!( k)! p k s( p s ) k prob of each success Poisso Distributio Note

More information

arxiv: v2 [math.pr] 20 Jul 2015

arxiv: v2 [math.pr] 20 Jul 2015 LIMITING SPECTRAL DISTRIBUTIONS OF SUMS OF PRODUCTS OF NON-HERMITIAN RANDOM MATRICES H. KÖSTERS,3 AND A. TIKHOMIROV 2,3 arxiv:506.04436v2 [math.pr] 20 Jul 205 Abstract. For fixed l, m, let X (0), X (),...,

More information

A NOTE ON SPECTRAL CONTINUITY. In Ho Jeon and In Hyoun Kim

A NOTE ON SPECTRAL CONTINUITY. In Ho Jeon and In Hyoun Kim Korea J. Math. 23 (2015), No. 4, pp. 601 605 http://dx.doi.org/10.11568/kjm.2015.23.4.601 A NOTE ON SPECTRAL CONTINUITY I Ho Jeo ad I Hyou Kim Abstract. I the preset ote, provided T L (H ) is biquasitriagular

More information

Binomial Distribution

Binomial Distribution 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 1 2 3 4 5 6 7 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Overview Example: coi tossed three times Defiitio Formula Recall that a r.v. is discrete if there are either a fiite umber of possible

More information

for all x ; ;x R. A ifiite sequece fx ; g is said to be ND if every fiite subset X ; ;X is ND. The coditios (.) ad (.3) are equivalet for =, but these

for all x ; ;x R. A ifiite sequece fx ; g is said to be ND if every fiite subset X ; ;X is ND. The coditios (.) ad (.3) are equivalet for =, but these sub-gaussia techiques i provig some strog it theorems Λ M. Amii A. Bozorgia Departmet of Mathematics, Faculty of Scieces Sista ad Baluchesta Uiversity, Zaheda, Ira Amii@hamoo.usb.ac.ir, Fax:054446565 Departmet

More information

Lecture Chapter 6: Convergence of Random Sequences

Lecture Chapter 6: Convergence of Random Sequences ECE5: Aalysis of Radom Sigals Fall 6 Lecture Chapter 6: Covergece of Radom Sequeces Dr Salim El Rouayheb Scribe: Abhay Ashutosh Doel, Qibo Zhag, Peiwe Tia, Pegzhe Wag, Lu Liu Radom sequece Defiitio A ifiite

More information

Regression with an Evaporating Logarithmic Trend

Regression with an Evaporating Logarithmic Trend Regressio with a Evaporatig Logarithmic Tred Peter C. B. Phillips Cowles Foudatio, Yale Uiversity, Uiversity of Aucklad & Uiversity of York ad Yixiao Su Departmet of Ecoomics Yale Uiversity October 5,

More information

Central limit theorem and almost sure central limit theorem for the product of some partial sums

Central limit theorem and almost sure central limit theorem for the product of some partial sums Proc. Idia Acad. Sci. Math. Sci. Vol. 8, No. 2, May 2008, pp. 289 294. Prited i Idia Cetral it theorem ad almost sure cetral it theorem for the product of some partial sums YU MIAO College of Mathematics

More information

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1.

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1. Eco 325/327 Notes o Sample Mea, Sample Proportio, Cetral Limit Theorem, Chi-square Distributio, Studet s t distributio 1 Sample Mea By Hiro Kasahara We cosider a radom sample from a populatio. Defiitio

More information

Dimensionality Reduction vs. Clustering

Dimensionality Reduction vs. Clustering Dimesioality Reductio vs. Clusterig Lecture 9: Cotiuous Latet Variable Models Sam Roweis Traiig such factor models (e.g. FA, PCA, ICA) is called dimesioality reductio. You ca thik of this as (o)liear regressio

More information

The random version of Dvoretzky s theorem in l n

The random version of Dvoretzky s theorem in l n The radom versio of Dvoretzky s theorem i l Gideo Schechtma Abstract We show that with high probability a sectio of the l ball of dimesio k cε log c > 0 a uiversal costat) is ε close to a multiple of the

More information

Closed book and notes. No calculators. 60 minutes, but essentially unlimited time.

Closed book and notes. No calculators. 60 minutes, but essentially unlimited time. IE 230 Seat # Closed book ad otes. No calculators. 60 miutes, but essetially ulimited time. Cover page, four pages of exam, ad Pages 8 ad 12 of the Cocise Notes. This test covers through Sectio 4.7 of

More information

Math 778S Spectral Graph Theory Handout #3: Eigenvalues of Adjacency Matrix

Math 778S Spectral Graph Theory Handout #3: Eigenvalues of Adjacency Matrix Math 778S Spectral Graph Theory Hadout #3: Eigevalues of Adjacecy Matrix The Cartesia product (deoted by G H) of two simple graphs G ad H has the vertex-set V (G) V (H). For ay u, v V (G) ad x, y V (H),

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak

More information

Spectra of nearly Hermitian random matrices

Spectra of nearly Hermitian random matrices Aales de l Istitut Heri Poicaré - Probabilités et Statistiques 207, Vol. 53, No. 3, 24 279 DOI: 0.24/6-AIHP754 Associatio des Publicatios de l Istitut Heri Poicaré, 207 www.imstat.org/aihp Spectra of early

More information

1 Principal Component Analysis in High Dimensions and the Spike Model

1 Principal Component Analysis in High Dimensions and the Spike Model Pricipal Compoet Aalysis i High Dimesios ad the Spike Model. Dimesio Reductio ad PCA Whe faced with a high dimesioal dataset, a atural approach is to try to reduce its dimesio, either by projectig it to

More information

Probability and Statistics

Probability and Statistics Probability ad Statistics Cotets. Multi-dimesioal Gaussia radom variable. Gaussia radom process 3. Wieer process Why we eed to discuss Gaussia Process The most commo Accordig to the cetral limit theorem,

More information

Limit distributions for products of sums

Limit distributions for products of sums Statistics & Probability Letters 62 (23) 93 Limit distributios for products of sums Yogcheg Qi Departmet of Mathematics ad Statistics, Uiversity of Miesota-Duluth, Campus Ceter 4, 7 Uiversity Drive, Duluth,

More information

TAMS24: Notations and Formulas

TAMS24: Notations and Formulas TAMS4: Notatios ad Formulas Basic otatios ad defiitios X: radom variable stokastiska variabel Mea Vätevärde: µ = X = by Xiagfeg Yag kpx k, if X is discrete, xf Xxdx, if X is cotiuous Variace Varias: =

More information

OPERATOR PROBABILITY THEORY

OPERATOR PROBABILITY THEORY OPERATOR PROBABILITY THEORY Sta Gudder Departmet of Mathematics Uiversity of Dever Dever, Colorado 80208 sta.gudder@sm.du.edu Abstract This article presets a overview of some topics i operator probability

More information

Chapter 2 The Monte Carlo Method

Chapter 2 The Monte Carlo Method Chapter 2 The Mote Carlo Method The Mote Carlo Method stads for a broad class of computatioal algorithms that rely o radom sampligs. It is ofte used i physical ad mathematical problems ad is most useful

More information

Convergence of random variables. (telegram style notes) P.J.C. Spreij

Convergence of random variables. (telegram style notes) P.J.C. Spreij Covergece of radom variables (telegram style otes).j.c. Spreij this versio: September 6, 2005 Itroductio As we kow, radom variables are by defiitio measurable fuctios o some uderlyig measurable space

More information