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School
University of Toronto St. George
Department
Statistical Sciences
Course
STA437H1
Professor
Semester
Fall

Description
Notes for STA 437/1005 — Methods for Multivariate Data Radford M. Neal, 26 November 2010 Random Vectors Notation: ′ ′ Let X be a random vector with p elements, so that X = [X ,...,X ] , 1here p denotes transpose. (By convention, our vectors are column vectors unless otherwise indicated.) We denote a particular realized value of X by x. Expection: ▯ The expectation (expected value, mean) of a random vector X is E(X) = xf(x)dx, where f(x) is the joint probability density function for the distribution of X. We often denote E(X) by µ, with µ = E(X j being thj expectation of the j’th element of X. Variance: 2 The variance of the random variable X is Vaj(X ) = E[(j −E(X )) ], jhich wejsome- times write as σ 2. j ▯ The standard deviation of X is j Var(X )j= σ . j Covariance and correlation: The covariance of X and X is Cov(X ,X ) = E[(X −E(X ))(X −E(X ))], which we j k j k j j k k 2 sometimes write as σ . jkte that Cov(X ,X ) isjthejvariance of X , so σ j jj= σ j The correlation of X anj X is Cok(X ,X )/(σ σj), khichjweksometimes write as ρ . jk Note that correlations are always between −1 and +1, and ρ jj is always one. Covariance and correlation matrices: The covariances for all pairs of elements of X = [X ,..1,X ] p′ can be put in a matrix called the covariance matrix:   σ11 σ 12 ··· σ 1p    σ21 σ 22 ··· σ 2p  Σ =  . . . .   . . . .  σp1 σ p2 ··· σ pp Note that the covariance matrix is symmetrical, with the variances of the elements on the diagonal. ′ The covariance matrix can also be written as Σ = E [(X − E(X))(X − E(X)) ]. Similarly, the correlations can be put into a a symmetrical correlation matrix, which will have ones on the diagonal. 1 www.notesolution.com Multivariate Sample Statistics Notation: Suppose we have n observations, each with values for p variables. We denote the value of variable j in observation i by x ijand the vector of all values for observation i by x .i We often view the observed x as i random sample of realizations of a random vector X with some (unknown) distribution. The is potential ambiguity between the notation x for ibservation i, and the notation x j for a realization of the random variable X .j(The textbook uses bold face for x .) i I will (try to) reserve i for indexing observations, and use j and k for indexing variables, but the textbook somtimes uses i to index a variable. Sample means: ▯n The sample mean of variable j is x ¯ = 1 x . j n i=1 ij The sample mean vector is x ¯ = [x ¯ ,...,x¯ ] . 1 p If the observations all have the same distribution, the sample mean vector, x ¯, is an unbiased estimate of the mean vector, µ, of the distribution from which these observations came. Sample variances: ▯n The sample variance of variable j is s j = n−1 (xij x ¯j) . i=1 If the observations all have the same distribution, the sample variance, s , js an estimate 2 of the variance, σ j of the distribution for X , jnd will be an unbiased estimate if the observations are independent. Sample covariance and correlation: ▯n The sample covariance of variable j with variable k is 1 (x −x ¯ )(x −x ¯ ). n−1i=1 ij j ik k The sample covariance is denoted by s . Note that s equals s , the sample variance of jk jj j variable j. The sample correlation of variable j with variable k is s /jk s j koften denoted by r . jk Sample covariance and correlation matrices: The sample covariances may be arranged as the sample covariance matrix:   s11 s12 ··· s 1p  s21 s22 ··· s 2p  S =  . . . .   . . . .  sp1 sp2 ··· s pp 1 ▯n ′ The sample covariance matrix can also be computed as S = n−1 (xi− x¯)(x i x¯) . i=1 Similarly, the sample correlations may be arranged as the sample correlation matrix, some- times denoted R (though the textbook also uses R for the population correlation matrix). 2 www.notesolution.com Linear Combinations of Random Variables ′ Deﬁne the random variable Y = a X +a X +1··1a X 2 w2ich can be pripten as Y = a X, where a = [a ,1 ,.2.,a ] .p ′ ′ ′ Then one can show that E(Y ) = a µ and Var(Y ) = a Σa, where µ = E(X) and Σ is the covariance matrix for X. For a random vector of dimension q deﬁned as Y = AX, with A being a q × p matrix, one can show that E(Y ) = Aµ and Var(Y ) = AΣA , where Var(Y ) is the covariance matrix of Y . Similarly, if x is the i’th observed vector, and we de
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