Sections 2.11 and 5.8

Transcription

1 Sections 211 and 58 Timothy Hanson Department of Statistics, University of South Carolina Stat 704: Data Analysis I 1/25

2 Gesell data Let X be the age in in months a child speaks his/her first word and let Y be the Gesell adaptive score, a measure of a child s aptitude (observed later on) Are X and Y related? How does the child s aptitude change with how long it takes them to speak? Here s the Gesell score y i and age at first word in months x i data, i = 1,,21 x i y i x i y i x i y i x i y i x i y i In R, we compute r = 0640, a moderately strong negative relationship between age at first word spoken and Gesell score > age=c(15,26,10,9,15,20,18,11,8,20,7,9,10,11,11,10,12,42,17,11,10) > Gesell=c(95,71,83,91,102,87,93,100,104,94,113,96,83,84,102,100,105,57,121,86,100) > plot(age,gesell) > cor(age,gesell) [1] /25

3 Scatterplot of (x 1,y 1 ),,(x 21,y 21 ) Gesell age 3/25

4 Random vectors A random vector X = variables X 1 X 2 X k is made up of, say, k random A random vector has a joint distribution, eg a density f(x), that gives probabilities P(X A) = f(x)dx Just as a random variable X has a mean E(X) and variance var(x), a random vector also has a mean vector E(X) and a covariance matrix cov(x) A 4/25

5 Mean vector & covariance matrix Let X = (X 1,,X k ) be a random vector with density f(x 1,,x k ) The mean of X is the vector of marginal means X 1 E(X 1 ) X 2 E(X) = E = E(X 2 ) (538) X k E(X k ) The covariance matrix of X is given by cov(x 1,X 1 ) cov(x 1,X 2 ) cov(x 1,X k ) cov(x 2,X 1 ) cov(x 2,X 2 ) cov(x 2,X k ) cov(x) = cov(x k,x 1 ) cov(x k,x 2 ) cov(x k,x k ) (542) 5/25

6 Multivariate normal distribution The normal distribution generalizes to multiple dimensions We ll first look at two jointly distributed normal random variables, then discuss three or more The bivariate normal density for (X 1,X 2 ) is given by f(x 1,x 2 ) = { [ (x1 ) 1 2πσ 1 σ 2 1 ρ 2 exp 1 µ 2 ( )( ) ( ) ]} 1 x1 µ 1 x2 µ 2 x2 µ 2 2 2(1 ρ 2 2ρ + ) σ 1 σ 1 σ 2 σ 2 There are 5 parameters: (µ 1,µ 2,σ 1,σ 2,ρ) Besides 58, also see 211 pp /25

7 Bivariate normal distribution This density jointly defines X 1 and X 2, which live in R 2 = (, ) (, ) Marginally, X 1 N(µ 1,σ1 2) and X 2 N(µ 2,σ2 2 ) (p 79) The correlation between X 1 and X 2 is given by corr(x 1,X 2 ) = ρ (p 80) For jointly normal random variables, if the correlation is zero then they are independent This is not true in general for jointly defined random variables E(X) = [ µ1 ], cov(x) = [ σ 2 1 σ 1 σ 2 ρ µ 2 σ 1 σ 2 ρ σ2 2 Next slide: µ 1 = 0,1; µ 2 = 0,2; σ1 2 = σ2 2 = 1; ρ = 0,09, 06 ] 7/25

8 Bivariate normal PDF level curves /25

9 Proof that X 1 indpendent X 2 when ρ = 0 When ρ = 0 the joint density for (X 1,X 2 ) simplifies to { [ 1 f(x 1,x 2 ) = exp 1 (x1 ) µ 2 ( ) ]} 1 x2 µ πσ 1 σ 2 2 σ 1 σ 2 [ ( ) 1 = e 05 x1 µ 2 ][ ( ) 1 1 σ 1 e 05 x2 µ ] σ 2 2πσ1 2πσ2 Since these are each respectively functions of x 1 and x 2 only, and the range of (X 1,X 2 ) factors into the produce of two sets, X 1 and X 2 are independent and in fact X 1 N(µ 1,σ 2 1 ) and X 2 N(µ 2,σ 2 2 ) 9/25

10 Conditional distributions [X 1 X 2 = x 2 ] and [X 2 X 1 = x 1 ] (pp 80 81) The conditional distribution of X 1 given X 2 = x 2 is ( [X 1 X 2 = x 2 ] N µ 1 + σ ) 1 ρ(x 2 µ 2 ),σ 2 σ 1(1 ρ 2 ) 2 Similarly, [X 2 X 1 = x 1 ] N This ties directly to linear regression: ( µ 2 + σ ) 2 ρ(x 1 µ 1 ),σ 2 σ 2(1 ρ 2 ) 1 To predict X 2 X 1 = x 1, we have [ E(X 2 X 1 = x 1 ) = µ 2 σ ] [ ] 2 σ2 ρµ 1 + ρ x 1 = β 0 +β 1 x 1 σ 1 σ 1 10/25

11 Bivariate normal distribution as data model Here we assume or succinctly, [ Xi1 X i2 ] iid N2 ([ µ1 µ 2 ], X i iid N2 (µ,σ) [ ]) σ11 σ 12, σ 21 σ 22 If the bivariate normal model is appropriate for paired outcomes, it provides a convenient probability model with some nice properties The sample mean X = 1 n n i=1 X i is the MLE of µ and the sample covariance matrix S = 1 n n 1 i=1 (X i X)(X i X) is unbiased for Σ It can be shown that X N 2 ( µ, 1 n Σ ) The matrix (n 1)S has a Wishart distribution (generalizes χ 2 ) 11/25

12 Sample mean vector & covariance matrix Say n outcome pairs are to be recorded: {(X 11,X 12 ),(X 21,X 22 ),,(X n1,x n2 )} The i th pair is (X i1,x i2 ) The sample mean vector is given elementwise by X = [ X1 X 2 ] = [ 1 n n i=1 X i1 1 n n i=1 X i2 and the sample covariance matrix is given elementwise by [ 1 n S = n 1 i=1 (X i1 X 1 ) 2 1 n n 1 i=1 (X ] i1 X 1 )(X i2 X 2 ) 1 n n 1 i=1 (X i1 X 1 )(X i2 X 1 n 2 ) n 1 i=1 (X i2 X 2 ) 2 ], 12/25

13 Estimation The sample mean vector X estimates µ = [ µ1 µ 2 ] and the sample covariance [ matrix S estimates ] [ ] σ1 Σ = 2 ρσ 1 σ 2 σ11 σ ρσ 1 σ 2 σ2 2 = 12 σ 21 σ 22 We will place hats on parameter estimators based on the data So ˆµ 1 = X 1, ˆµ 2 = X 2, ˆσ 2 1 = s 2 1 = 1 n 1 n (X i1 X 1 ) 2, i=1 Also, ˆσ 2 2 = s 2 2 = 1 n 1 ĉov(x 1,X 2 ) = 1 n 1 n (X i2 X 2 ) 2 i=1 n (X i1 X)(X i2 X 2 ) i=1 13/25

14 Correlation coefficient r So a natural estimate of ρ is then ˆρ = ĉov(x 1,X 2 ) ˆσ 1ˆσ 2 = 1 n 1 1 n n 1 i=1 (X i1 X 1 )(X i2 X 2 ) n i=1 (X i1 X 1 ) 2 n i=1 (X i1 X 1 ) 2 1 n 1 This is in fact the MLE estimate based on the bivariate normal model It is also a plug-in estimator based on the method-of-moments too It is commonly referred to as the Pearson correlation coefficient You can get it as, eg, cor(age,gesell) in R This estimate of correlation can be unduly influenced by outliers in the sample An alternative measure of linear association is the Spearman correlation based on ranks, discussed a few lectures ago > cor(age,gesell) [1] > cor(age,gesell,method="spearman") [1] /25

15 Gesell data Recall: X is age in months a child speaks his/her first word and let Y is Gesell adaptive score, a measure of a child s aptitude Question: how does the child s aptitude change with how long it takes them to speak? [ Here, ] n = 21 [ ] In R we find X = Also, S = Assuming a bivariate model, we plug in the estimates and obtain the estimated PDF for (X,Y): f(x,y) = exp( x 00134x y 00098xy 00043y 2 ) We can further find from Y N(9367,18632), f Y (y) = exp( (y 9367) 2 ) 15/25

16 3D plot of f(x,y) for (X,Y) estimated from data /25

17 Gesell conditional distribution Solid is f Y (y); left dashed is f Y X (y 25) the right dashed is f Y X (y 10) As the age in months of first words X = x increases, the distribution of Gesell Adaptive Scores Y decreases 17/25

18 Density estimate with actual data Gesell age 18/25

19 Multivariate normal distribution In general, a k-variate normal is defined through the mean and covariance matrix: X 1 µ 1 σ 11 σ 12 σ 1k X 2 N µ 2 k, σ 21 σ 22 σ 2k X k µ k σ k1 σ k2 σ kk Succinctly, X N k (µ,σ) Recall that if Z N(0,1), then X = µ+σz N(µ,σ 2 ) The definition of the multivariate normal distribution just extends this idea 19/25

20 Multivariate normal made from independent normals Instead of one standard normal, we have a list of k independent standard normals Z = (Z 1,,Z k ), and consider the same sort of transformation in the multivariate case using matrices and vectors Let Z 1,,Z k iid N(0,1) The joint pdf of (Z1,,Z k ) is given by Let µ = f(z 1,,z k ) = µ 1 µ 2 µ k k exp( 05zi 2 )/ 2π i=1 σ 11 σ 12 σ 1k and Σ = σ 21 σ 22 σ 2k, σ k1 σ k2 σ kk where Σ is symmetric (ie Σ = Σ, which implies σ ij = σ ji for all 1 i,j k) 20/25

21 Multivariate normal made from independent normals Let Σ 1/2 be any matrix such that Σ 1/2 Σ 1/2 = Σ Then X = µ+σ 1/2 Z is said to have a multivariate normal distribution with mean vector µ and covariance matrix Σ, written Written in terms of matrices X N k (µ,σ) X = X 1 X 2 X k = µ 1 µ 2 µ k + σ 11 σ 12 σ 1k σ 21 σ 22 σ 2k σ k1 σ k2 σ kk 1/2 Z 1 Z 2 Z k 21/25

22 Joint PDF Using some math, it can be shown that the pdf of the new vector X = (X 1,,X k ) is given by f(x 1,,x k µ,σ) = 2πΣ 1/2 exp{ 05(x µ) Σ 1 (x µ)} In the one-dimensional case, this simplifies to our old friend f(x 1 µ,σ 2 ) = (2πσ 2 ) 1/2 exp{ 05(x µ)(σ 2 ) 1 (x µ)}, the pdf of a N(µ,σ 2 ) random variable X A is the determinant of the matrix A, and is a function of the elements of A, but beyond this course 22/25

23 Properties of multivariate normal vectors Let Then X N k (µ,σ) 1 For each X i in X = (X 1,,X k ), E(X i ) = µ i and var(x i ) = σ ii That is, marginally, X i N(µ i,σ ii ) 2 For any r k matrix M, MX N r (Mµ,MΣM ) 3 For any two (X i,x j ) where 1 i < j k, cov(x i,x j ) = σ ij The off-diagonal elements of Σ give the covariance between two elements of (X 1,,X k ) Note then ρ(x i,x j ) = σ ij / σ ii σ jj 4 For any k 1 vector m = (m 1,,m k ) and Y N k (µ,σ), m+y N k (m+µ,σ) 23/25

24 Example Let Define X 1 X 2 N 3 2 5, X [ ] [ ] Y1 M = and Y = = MX = Y 2 [ ] X 1 X 2 X 3 Then X 2 N(5,3), cov(x 2,X 3 ) = 1 and [ ] X 1 X X 3 [ ] N [ ] , or simplifying, [ Y1 Y 2 ] = [ ] X 1 X 2 X 3 N 2 ([ 2 1 ] [ 4 0, Note that for the transformed vector Y = (Y 1,Y 2 ), cov(y 1,Y 2 ) = 0 and therefore Y 1 and Y 2 are uncorrelated, ie ρ(y 1,Y 2 ) = 0 ]), 24/25

25 Simple linear regression For the linear model (eg simple linear regression or the two-sample model) Y = Xβ +ǫ, the error vector is assumed (pp ) ǫ N n (0,I n n σ 2 ) Then the least squares estimators have a multivariate normal distribution β N p (β,(x X) 1 σ 2 ) p = 2 is the number of mean parameters (The MSE has a gamma distribution) We ll discuss this shortly! 25/25