X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

Transcription

1 Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers of radom varables smultaeously. We defe the expectato of a radom vector as elemet-by-elemet expectato: E[ ] E[ ] E[ x]. E[ ] If s a ()m matrx of radom varables, the E[ ] s the ()m where the (,)th j elemet s the mea of the (,)th j elemet of,.e., matrx f m m m, the E[ ] E[ ] E[ ] E[ ] E[ ] E[ ] E[ ]. E[ m] E[ m] E[ m ] These deftos provde a eat way for computg the varaces ad covaraces of the varables all at oce : var[ x] E[( x E[ x])( x E[ x]) ] ( E[ ])( E[ ])( [ E ])( [ E ])( [ ])( E [ ]) E ( E[ ])( E[ ])( [ E ])( [ E ])( [ ])( E [ ]) E E ( E[ ])( E[ ])( [ E ])( [ E ])( E[ ])( E[ ]) var[ ] cov[, ] cov[, ] cov[, ] var[ ] cov[, ] cov[, ] cov[, ] var[ ] We call var[ x] the varace-covarace matrx of x. Athoy Tay 5-

2 The formula var[ x] E[( x E[ x])( x E[ x]) ] ca be vewed as the matrx verso of the varace formula var[ ] E[( E[ ]) ] for a sgle varable. Sometmes we wat to compute a covarace matrx betwee two vectors of radom varables x ad y. We ca compute cov[ x, y] E[( x E[ x])( y E[ y]) ] ( E[ ])( Y E[ Y ])( [ E ])( Y [ E])( Y [ ])( E [ ]) Y E Y ( E[ ])( Y E[ Y ])( [ E ])( Y [ E ])( Y [ ])( E [ Y]) E Y E ( E[ ])( Y E[ Y ])( [ E ])( Y [ E Y])( E[ ])( Y E[ Y ]) cov[, Y ] cov[, Y ] cov[, Y ] cov[, Y ] cov[, Y ] cov[, Y ] cov[, Y ] cov[, Y ] cov[, Y ] Rules for dealg wth the mea vector ad the varace-covarace matrx If x s a ( ) vector of radom varables, s a ()m matrx of radom varables, b s a ( m ) vector of costats, ad A s a ()m matrx of costats, the. E[ Ax b] AE[ x] b. var -cov[ Ax b] A var[ x] A. I partcular, cx c x c j j j var[ c c... c ] var[ ] var[ ] c c cov[, ] 3. A useful result s E[ tr[ ]] E[... ] E[ ] E[ ]... E[ ]. tr[ E[ ]] The frst of these s straghtforward to show by smply wrtg out the expresso Ax b full ad takg expectatos. Ths formula s the matrx verso of the usual sgle varable result E[ a b] ae[ ] b Athoy Tay 5-

3 To show (), plug Ax b to the varace formula: var[ Ax b] E[( Ax b E[ Ax b])( Ax b E[ Ax b]) ] E[( Ax b AE[ x])( b Ax b [ AE]) ] x b E[( Ax AE[ x])( Ax AE[ x]) ] E[( A x [ E])(( x A x[ ])) E x] E[( A x [ E])( x x [ E]) x A] AE[( x E[ x])( x E[ x]) ] A A var[ x] A Ths s the matrx verso of the sgle varable result var[ a b] a var[ ]. Note that a varace-covarace matrx must be postve-defte -- has to be postve for all var[ c c... c ] var[ cx] cvar[ x] c c 0, sce varaces must be postve. Exercse be costats. Wrte out Let x be a vector of radom varables, ad a A a a a b ad b b Ax b full, ad take expectatos to show that E[ Ax b] AE[ x] b The Multvarate Normal Dstrbuto The radom vector x follows the multvarate ormal dstrbuto wth mea E[ x] ad varace-covarace matrx μ f ts dstrbuto has the form f Σ / ()() x Σexp{(/ )()'()} x μ Σ x μ. Athoy Tay 5-3

4 We deote ths by x ~( N,) μ Σ. Ths s aalogous to the uvarate ormal pdf: f () x () x exp Exercse Wrte the jot pdf out wthout matrx otato for the bvarate case x. The show that say? f ( x,)()() x f x f x whe 0. What does ths, Exercse Use computer software (say matlab) to plot the dstrbuto of the bvarate ormal for varous parameter values. The followg are mportat propertes of the multvarate ormal: 4. The codtoal dstrbutos are also ormal. I partcular, f x ~ N μ, Σ Σ x μ Σ Σ where x ad μ are ( ), x ad μ are ( ), Σ s (), Σ (), Σ s (), ad (), the a. the margal dstrbuto for x s N( μ,) Σ b. the codtoal dstrbuto for x gve x s N( μ,) Σ where () μ μ Σ Σ x μ ad Σ Σ Σ Σ Σ s Exercse I the bvarate case, ~ N, (4a) says that ~( N,) ad ~( N,). Wrte out the expressos (4b). Note partcular that the codtoal mea of gve s a lear fucto of. Athoy Tay 5-4

5 Exercse Usg the expressos for the codtoal mea ad codtoal varace of gve, show that f ( x,)( x )() f x x f x., (You ca make a smlar argumet for the geeral x ad x case.) 5. If x ~( N,) μ Σ, the Ax b ~( N Aμ,) b AΣA ; The expresso Ax b s ormal because lear combatos of ormal radom varables rema ormal. The formulae for the mea ad varace-covarace matrx are the usual oes. 6. If x ~( N,) μ Σ ad depedet. Σ dag(,,...,), the the radom varables x are The followg make use of the fact that The square of a stadard ormal varable has a dstrbuto; The sum of depedet s a ; If ~(0,) N, Y ~ ad ad Y are depedet, the ~ t Y / If ~ Y, ~ Y / Y m, ad Y ad Y are depedet, the ~ F Y / m We have 7. If x ~( N,) 0 I ad A s symmetrc, ad dempotet wth rak J, the the scalar radom varable xax ~ () J. I partcular, xx ~ (). Proof Because A s symmetrc, we ca wrte A CΛC, wth CC I. Note that Cx ~( N,) 0 I because var() Cx CIC CC I. That s, Cx s a vector of depedetly dstrbuted stadard ormal varables. Wrte xax xcλcx yλy y (,) m where y Cx. Each y s a depedet ch-sq degree oe, sce the y s are depedet stadard ormal varables. Because A s dempotet, there are J s equal to oe, ad () J s that are zero. Relabelg the y s so that the frst s are equal to oe, we have whch s a sum of J depedet xax J J y ~ () J. Therefore xax. Athoy Tay 5-5

6 8. If x ~( N,) μ Σ, the () ()() x ~ μ Σ x μ. Proof Σ s postve defte, symmetrc, ad full rak. Therefore we ca wrte / / / Σ Σ Σ. Note that z Σ () x~( μ,) N 0 I, therefore / / () ()()(())() x μ Σ x μ~ Σ x μ Σ x μ zz. 9. If x ~( N,) 0 I, ad A ad B are symmetrc ad dempotet, the xax ad xbx are depedet f AB 0. Proof Because A ad B are symmetrc ad dempotet, we have AA A ad BB B. Therefore we ca wrte the quadratc forms as xax xaax ()() Ax Ax. Because x s ormal wth mea 0, Ax also ormal wthmea 0. For vectors of zero mea radom varables, cov[ x, y] E[ xy] (why?). We have E cov Ax, Bx AxxB AE[ xx] B AB AB. Therefore, AB 0 mples that Ax ad Bx are ormally dstrbuted, wth covarace 0. Ths mples that Ax ad Bx are depedet (why?), ad therefore the quadratc forms xax ad xbx are also depedet. It follows from (7) that [ xax /()] rak A [ xbx /()] rak B s dstrbuted F rak() A,() rak B. 0. If x ~( N,) 0 I, ad A s symmetrc ad dempotet, the Lx ad xax are depedet f LA 0. Proof Same dea as (9). We use these results to prove some stadard results statstcs. Suppose,,..., are depedet draws from a N(,) dstrbuto,.e. x ~( N,) μ I. where μ s a ( ) vector μ. Athoy Tay 5-6

7 We kow that the sample mea s ormally dstrbuted (because a lear combato of ormal varables s ormal) wth mea ad varace E[ ] E E var[ ] var var Furthermore, ~ N, mples that / ~(0,) N Ufortuately, ths result s ot very helpful f, for stace, you wat to test a hypothess o, sce s geeral ukow, ad must be estmated. A ubased estmator s ˆ () To show ths, ote that (). Each has varace so Also, var[ ] E[ ], E E. E var E. Puttg all ths together, we have: Athoy Tay 5-7

8 E ˆ E () E ( ). Oe dea, the, s to substtute wth ˆ / to get the t-statstc t. ˆ / Ufortuately, the t -statstc does ot have a stadard ormal dstrbuto. We use the results dscussed earler to derve the dstrbuto of the t - statstc. We beg by dervg a matrx expresso for ˆ. Observe frst that (()) I x 0 0 (/) Athoy Tay 5-8

9 A terestg property of the matrx dempotet, wth rak. Symmetrc: Idempotet: M I () s that t s symmetrc ad ()()() M I I I M ()() MM I I II ()()()() I I I ()()() cacels out M Because M symmetrc ad dempotet, rak()() M trace M, ad trace I ()()(())(()) tracei trace trace Therefore ˆ () xmmx xmx Furthermore, ote that x μ ~( N,) 0 I Mx M() x μ sce Mμ 0 (why?) Together wth the fact that M s symmetrc ad dempotet wth rak, result (7) mples that xmx ()() x μ ~ M x μ. Athoy Tay 5-9

10 Ths result s cosstet wth the fact that.e. Sce ˆ E[ ˆ ] E[ xmx ]. xmx, the result follows. The fact that ( ) ˆ xmx ~,. The mea of a s, whch we have just show, s of course a much stroger oe. The result that E[ ˆ ] does ot deped o the ormalty of x. If we have ormalty of x, the we have the Fally, ote that ad that result. ()() ~(0,) x μ N, ()()()()()() M I 0 whch says that cacels out ()() ~(0,) x μ N ad are depedet. Therefore, ( ) ˆ xmx ~ t () N(0,) ~ ˆ / ( ) ˆ ( ) t. Of course, f s are ot radom draws from N(,), the all of these results do ot hold (except E[ ], var[ ] / ad E[ ˆ ], whch does ot requre the ormalty assumpto). Uder reasoable codtos, the t -statstc wll coverge to the ormal as the sample sze grows. Athoy Tay 5-0