Mathmatical Statisticals

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1 Mathmatical Statisticals Che, L.-A. Chapter 4. Distributio of Fuctio of Radom variables Sample space S : set of possible outcome i a experimet. Probability set fuctio P: ()P (A) 0, A S. ()P (S) =. (3)P ( A i ) = P (A i ), ifa i A j =, i j. Radom variable X: X : S R Give B R, P (X B) = P ({s S : X(s) B}) = P (X (B)) where X (B) S. X is a discrete radom variable if its rage X(s) = {x R : s S, X(s) = x} is coutable. The probability desity/mass fuctio (p.d.f) of X is defied as f(x) = P (X = x), x R. Distributio fuctio F: F (x) = P (X x), x R. A r.v. is called a cotiuous r.v. if there exists f(x) 0 such that F (x) = x where f is the p.d.f of cotiuous r.v. X. f(t)dt, x R.

2 Let X be a r.v. with p.d.f f(x). Let g : R R Q: What is the p.d.f. of g(x)? ad is g(x) a r.v.?(yes) Aswer: (a) distributio method : Suppose that X is a cotiuous r.v.. Let Y = g(x) The d.f(distributio fuctio) of Y is G(y) = P (Y y) = P (g(x) y) If G is differetiable the the p.d.f. of Y = g(x) is g(y) = G (y). (b) mgf method :(momet geeratig fuctio) { e E[e tx tx f(x) (discrete) ] = etx f(x)dx (cotiuous) Thm. m.g.f. M x (t) ad its distributio (p.d.f. or d.f.) forms a fuctios. ex: M Y (t) = e t = M N(0,) (t) Y N(0, ) Let X,..., X be radom variables. If they are discrete, the joit p.d.f. of X,..., X is f(x,..., x ) = P (X = x, X = x,..., X = x ), If X,..., X are cotiuous r.v. s, there exists f such that x x F (x,..., x ) =... f(t,..., t )dt... dt, for We call f the joit p.d.f. of X,..., X. x. x x. x R R If X is cotiuous, the F (x) = x f(t)dt ad P (X = x) = x x f(t)dt = 0, x R.

3 Margial p.d.f s: Discrete: f Xi (x) = P (X i = x) = f(x,..., x i, x, x i+,..., x ) x x i+ x i x Cotiuous: f Xi (x) = f(x,..., x i, x, x i+,..., x )dx... dx i dx i+... dx Evets A ad B are idepedet if P (A B) = P (A)P (B). Q: If A B =, are A ad B idepedet? A: I geeral, they are ot. Let X ad Y be r.v. s with joit p.d.f. f(x, y) ad margial p.d.f. f X (x) ad f Y (y). We say that X ad Y are idepedet if ( ) x f(x, y) = f X (x)f Y (y), R y Radom variables X ad Y are idetically distributed (i.d.) p.d.f. s f ad g satisfy f = g or d.f. s F ad G satisfy F = G. if margial We say that X ad Y are iid radom variables if they are idepedet ad idetically distributed. Trasformatio of r.v. s (discrete case) Uivariate: Y = g(x), p.d.f. of Y is g(y) = P (Y = y) = P (g(x) = y) = P ({x Rage of X : g(x) = y}) = {x:g(x)=y} For radom variables X,..., X with joit p.d.f. f(x,..., x ), defie trasformatios Y = g (X,..., X ),..., Y m = g m (X,..., X ). The joit p.d.f. of Y,..., Y m is f(x) 3

4 g(y,..., y m ) = P (Y = y,..., Y m = y m ) = P ({ =. x x. x : g (x,..., x ) = y,..., g m (x,..., x ) = y m }) x { :g (x,...,x )=y,...,g m(x,...,x )=y m} Example: joit p.d.f. of X, X, X 3 is f(x,..., x ) (x, x, x 3 ) (0, 0, 0) (0, 0, ) (0,, ) (, 0, ) (,, 0) (,, ) f(x, x, x 3 ) Y = X + X + X 3, Y = X 3 X Space of (Y, Y ) is {(0, 0), (, ), (, 0), (, ), (3, 0)}. Joit p.d.f. of Y ad Y is (y, y ) (0, 0) (, ) (, 0) (, ) (3, 0) g(y, y ) Cotiuous oe-to-oe trasformatios: Let X be a cotiuous r.v. with joit p.d.f. f(x) ad rage A = X(s). Cosider Y = g(x), a differetiable fuctio. We wat p.d.f. of Y. Thm. If g is - trasformatio, the the p.d.f. of Y is { f X (g (y)) dg (y) y g(a) f Y (y) = dy 0 otherwise. Proof. The d.f. of Y is 8 F Y (y) = P (Y y) = P (g(x) y) (a) If g is, g is also.( dg dy > 0) F Y (y) = P (X g (y)) = g (y) 8 f X (x)dx 8

5 p.d.f. of Y is g (y) f Y (y) = D y f X (x)dx (b) If g is, g is also. ( dg dy < 0) F Y (y) = P (X g (y)) = p.d.f. of Y is = f X (g (y)) dg (y) dy = f X (g (y)) dg (y) dy g (y) f Y (y) = D y ( f X (x)dx = g (y) f X (x)dx) = f X (g (y)) dg (y) dy = f X (g (y)) dg (y) dy g (y) f X (x)dx Example : X U(0, ), Y = l(x) = g(x) sol: p.d.f. of X is {, if 0 < x < f X (x) = 0, elsewhere. A = (0, ), g(a) = (0, ), p.d.f. of Y is x = e y = g (y), dx dy = y e f Y (y) = f X (g (y)) dy dx = y e, y > 0 (X U(a, b) if f X (x) = { b a if a < x < b 0 elsewhere. ) 5

6 Y χ () (X χ (r) if f X (x) = Cotiuous -r.v.-to-m-r.v., > m, case : x. x Γ( r ) x r r e x, x > 0) Y = g (X,..., X ). Y m = g m (X,..., X ) g. g R m R m Q : What are the margial p.d.f. of Y,, Y m A : We eed to defie Y m+ = g m+ (X,..., X ),, Y = g (X,..., X ) such that g. g is - from R to R. Theory for chage variables : x P (. A) = x f X,...,X (x,..., x )dx dx Let y = g (x,..., x ),, y = g (x,..., x ) be a fuctio with iverse x = w (y,..., y ),, x = w (y,..., y ) ad Jacobia x x y y J =.. x x y y The f X,...,X (x,..., x )dx dx = f X,...,X (w (y,..., y ),..., w (y,..., y )) J dy dy Hece, joit p.d.f. of Y,, Y is f Y,...,Y (y,..., y ) = f X,...,X (w,..., w ) J 6

7 Thm. Suppose that X ad X are two r.v. s with cotiuous joit p.d.f. f X,X ad sample space A. If Y = g (X, X ), Y = g (X, X ) forms a trasformatio iverse fuctio ( X X ) = the joit p.d.f. of Y, Y is ( ) w (Y, Y ) ad Jacobia J = w (Y, Y ) f Y,Y (y, y ) = f X,X (w (y, y ), w (y, y )) J, Steps : (a) joit p.d.f. of X, X, space A. (b) check if it is trasformatio. Iverse fuctio X = w (Y, Y ), X = w (Y, Y ) (c) Rage of ( Y ) ( Y = g ) g (A) ( y x x y y x x y y y ) ( g iid Example : For X, X U(0, ), let Y = X + X, Y = X X. Wat margial p.d.f. of Y, Y Sol : joit p.d.f. of X, X is { if 0 < x f X,X (x, x ) = <, 0 < x < 0 elsewhere. ( ) X A = { : 0 < x <, 0 < x < } X Give y, y, solve y = x + x, y = x x. g ) (A). x = y + y = w (y, y ), x = y y ( trasformatio) = w (y, y ) Jacobia is J = x x y y x x y y The joit p.d.f. of Y, Y is = f Y,Y (y, y ) = f X,X (w, w ) J, 7 = 4 4 = ( y y ) B

8 Margial p.d.f. of Y, Y are y dy y = y, 0 < y < f Y (y ) = y y dy = y, < y < 0, elsewhere. f Y (y ) = +y y dy = y +, < y < 0 y dy y = y, 0 < y < 0, elsewhere. Def. If a sequece of r.v. s X,..., X are idepedet ad idetically distributed (i.i.d.),the they are called a radom sample. If X,..., X is a radom sample from a distributio with p.d.f. f 0, the the joit p.d.f. of X,..., X is x f(x,..., x ) = f 0 (x i ),. R Def. Ay fuctio g(x,..., X ) of a radom sample X,..., X which is ot depedet o a parameter θ is called a statistic. Note : If X is a radom sample with p.d.f. f(x, θ), where θ is a ukow costat, the θ is called parameter. For example, N(µ, σ ) : µ, σ are parameters. Poisso(λ) : λ is a parameter. x Example of statistics : X,..., X are iid r.v. s X ad S are statistics. Note : If X,..., X are r.v. s, the m.g.f of X,..., X is M X,...,X (t,..., t ) = E(e t X + +t X ) m.g.f M x (t) = E(e tx ) = e tx f(x)dx D t M x (t) = D t E(e tx ) = D t e tx f(x)dx = D t e tx f(x)dx 8

9 Lemma. X ad X are idepedet if ad oly if M X,X (t, t ) = M X (t )M X (t ), t, t. Proof. ) If X, X are idepedet, M X,X (t, t ) = E(e t X +t X ) = e t x +t x f(x, x )dx dx = e t x f X (x )dx e t x f X (x )dx = E(e t X )E(e t X ) = M X (t )M X (t ) ) M X,X (t, t ) = E(e t X +t X ) = e t x +t x f(x, x )dx dx M X (t )M X (t ) = E(e t X )E(e t X ) = = e t x f X (x )dx e t x f X (x )dx e t x +t x f(x, x )dx dx With correspodece betwee m.g.f ad p.d.f, the f(x, x ) = f (x )f (x ), x, x X, X are idepedet. X ad Y are idepedet, deote by X Y. X N(µ, σ σ µt+ ), M x (t) = e t, t R X Gamma(α, β), M x (t) = ( βt) α, t < β X b(, p), M x (t) = ( p + pe t ), t R X Poisso(λ), M x (t) = e λ(et ), t R Note : 9

10 (a) If (X,..., X ) ad (Y,..., Y m ) are idepedet, the g(x,..., X ) ad h(y,..., Y m ) are also idepedet. (b) If X, Y are idepedet, the E[g(X)h(Y )] = E[g(X)]E[h(Y )]. Thm. If (X,..., X ) is a radom sample from N(µ, σ ), the Proof. (a) m.g.f. of X is (a)x N(µ, σ ) (b)x ad S are idepedet. ( )S (c) χ ( ) σ M X (t) = E(e tx ) = E(e t X i ) = E(e t X e t X e t X ) = E(e t X )E(e t X )E(e t X ) = M X ( t )M X ( t ) M X ( t ) = (e µ t + σ ( t ) ) = e µt+ σ / t X (µ, σ ) (b) First we wat to show that X ad (X X, X X,..., X X) are 0

11 idepedet. Joit m.g.f. of X ad (X X, X X,..., X X) is M X,X X,X X,...,X X (t, t,..., t ) = E[e tx+t (X X)+ +t (X X) ] = E[e t X i+ t ix i t Xi i ] = E[e ( t +t i t)x i ], t = = E[e (t i t)+t = E[ e (t i t)+t X i ] = X i ] e µ (t i t)+t + σ ((t i t)+t) ((t i t)+t)+ σ ((t i t)+t) µ = e = e µt+ σ / t +µ (t i t)+ σ (ti t) + σ t (t i t) = e µt+ σ / t e σ (ti t) = M X (t)m (X X,X X,...,X X) (t,..., t ) X ad (X X, X X,..., X X) are idepedet. X ad S = (X i X) are idepedet. (c) () Z N(0, ), Z χ () () X χ (r ) ad Y χ (r ) are idepedet. X + Y χ (r + r ) Proof. m.g.f. of X + Y is M X+Y (t) = E(e t(x+y ) ) = E(e tx+ty ) = E(e tx )E(e ty ) = M X (t)m Y (t) X + Y χ (r + r ) (3) = ( t) r ( t) r = ( t) r +r X µ σ t i (X,..., X ) iid N(µ, σ), X µ,..., X µ σ σ iid N(0, )

12 (X µ), (X µ),..., (X µ) iid χ () σ σ σ (X i µ) σ = ( )s σ = (X i µ) σ (X i X) χ () σ χ ( ) ( t) = M (Xi µ) (t) = E(e t (Xi µ) σ ) σ = E(e t (Xi X+X µ) σ ) = E(e t (Xi X) +(X µ) = E(e t ( )s σ = E(e t ( )s σ e t (X µ) σ / ) )E(e t (X µ) σ / ) = M ( )s (t)m (X µ) (t) σ σ / = M ( )s σ (t)( t) M ( )s (t) = ( t) σ σ ) ( )s σ χ ( )

13 Chapter 3. Statistical Iferece Poit Estimatio Problem i statistics: A radom variables X with p.d.f. of the form f(x, θ) where fuctio f is kow but parameter θ is ukow. We wat to gai kowledge about θ. What we have for iferece: There is a radom sample X,..., X from f(x, θ). Poit estimatio: ˆθ = ˆθ(X,..., X ) Iterval estimatio: Estimatio Fid statistics T Statistical ifereces = t (X,..., X ), T = t (X,..., X ) such that α = P (T θ T ) Hypothesis testig: H 0 : θ = θ 0 or H 0 : θ θ 0. Wat to fid a rule to decide if we accept or reject H 0. Def. We call a statistic ˆθ = ˆθ(X,..., X ) a estimator of parameter θ if it is used to estimate θ. If X = x,..., X = x are observed, the ˆθ = ˆθ(x,..., x ) is called a estimate of θ. Two problems are cocered i estimatio of θ : (a) How ca we evaluate a estimator ˆθ for its use i estimatio of θ? Need criterio for this estimatio. (b) Are there geeral rules i derivig estimators? We will itroduce two methods for derivig estimator of θ. Def. We call a estimator θ ubiased for θ if it satisfies E θ (ˆθ(X,..., X )) = θ, θ. { E θ (ˆθ(X,..., X )) = ˆθ(x,..., x )f(x,..., x, θ)dx dx θ fˆθ(θ )dθ where ˆθ = ˆθ(X,..., X ) is a r.v. with pdf fˆθ(θ ) Def. If E θ (ˆθ(X,..., X )) θ for some θ, we said that ˆθ is a biased estimator. 3

14 iid Example : X,..., X N(µ, σ ), Suppose that our iterest is µ, X, E µ (X ) = µ, is ubiased for µ, (X + X ), E( X +X ) = µ, is ubiased for µ, X, E µ (X) = µ, is ubiased for µ, a a, if, for ɛ > 0, there exists N > 0 such that a a < ɛ if N. {X } is a sequece of r.v. s. How ca we defie X X as? Def. We say that X coverges to X, a r.v. or a costat, i probability if for ɛ > 0, P ( X X > ɛ) 0, as. I this case, we deote X Thm. Proof. P X. If E(X ) = a or E(X ) a ad Var(X ) 0, the X E[(X a) ] = E[(X E(X ) + E(X ) a) ] P a. = E[(X E(X )) ] + E[(E(X ) a) ] + E[(X E(X ))(E(X ) a)] = Var(X ) + E((X ) a) Chebyshev s Iequality : For ɛ > 0, P ( X X ɛ) E(X X) 0 P ( X a > ɛ) = P ((X a) > ɛ ) ɛ or P ( X µ kσ) k E(X a) ɛ = Var(X ) + (E(X ) a) ɛ 0 as. P ( X a > ɛ) 0, as. X P a. Thm. Weak Law of Large Numbers(WLLN) If X,..., X is a radom sample with mea µ ad fiite variace σ, the X P µ. 4

15 Proof. E(X) = µ, Var(X) = σ 0 as. X P µ. Def. We sat that ˆθ is a cosistet estimator of θ if ˆθ P θ. Example : X,..., X is a radom sample with mea µ ad fiite variace σ.is X a cosistet estimator of µ? E(X )=µ, X is ubiased for µ. Let ɛ > 0, P ( X µ > ɛ) = P ( X µ ɛ) = P (µ ɛ X µ + ɛ) = µ+ɛ µ ɛ X is ot a cosistet estimator of µ f X (x)dx > 0, 0 as. E(X) = µ, Var(X) = σ 0 as. X P µ. X is a cosistet estimator of µ. Ubiasedess ad cosistecy are two basic coditios for good estimator. Momets : Let X be a radom variable havig a p.d.f. momet is defied by x k f(x, θ) E θ (X k ) = all x xk f(x, θ)dx f(x, θ), the populatio k th, discrete, cotiuous The sample k th momet is defied by Note : E( X k i ) = X k i. E(X k i ) = 5 E θ (X k ) = E θ (X k )

16 Sample k th momet is ubiased for populatio k th momet. Var( X k i ) = Var( X k i ) = k X i P E θ (X k ). Var(X k i ) = Var(Xk ) 0 as. X k i is a cosistet estimator of E θ (X k ). Let X,..., X be a radom sample with mea µ ad variace σ.the sample variace is defied by S = (X i X) Wat to show that S is ubiased for σ. Var(X) = E[(X µ) ] = E[X µx + µ ] = E(X ) µ E(S ) = E( S = E(X ) = Var(X) + µ = Var(X) + (E(X)) E(X) = µ, Var(X) = σ (X i X) ) = E( Xi X = E( Xi X ) = [ E(Xi ) E(X )] = [σ + µ ( σ + µ )] = ( )σ = σ (X i X) is ubiased for σ. S = [ Xi X ] = s P σ [ X i + X ) Xi X P ] E(X ) µ = σ + µ µ = σ X,..., X are iid with mea µ ad variace σ X,..., X are iid r.v. s with mea E(X ) = µ + σ By WLLN, X P i E(X ) = µ + σ 6

17 Def. Let X,..., X f(x, θ) be a radom sample from a distributio with p.d.f. (a) If θ is uivariate, the method of momet estimator ˆθ solve θ for X = E θ (X) (b) If θ = (θ, θ ) is bivariate, the method of momet estimator ( ˆθ, ˆθ ) solves (θ, θ ) for X = E θ,θ (X), X i = E θ,θ (X ) (c) If θ = (θ,..., θ k ) is k-variate, the method of momet estimator ( ˆθ,..., ˆθ k ) solves θ,..., θ k for Example : X j i = E θ,...,θ k (X j ), j =,..., k iid (a) X,..., X Beroulli(p) Let X = E p (X) = p The method of momet estimator of p is ˆp = X By WLLN, ˆp = X P E p (X) = p ˆp is cosistet for p. E(ˆp) = E(X) = E(X) = p ˆp is ubiased for p. (b) Let X,..., X be a radom sample from Poisso(λ) Let X = E λ (X) = λ The method of momet estimator of λ is ˆλ = X E(ˆλ) = E(X) = λ ˆλ is ubiased for λ. ˆλ = X P E(X) = λ ˆλ is cosistet for λ. (c) Let X,..., X be a radom sample with mea µ ad variace σ. θ = (µ, σ ) Let X = E µ,σ (X) = µ X i = E µ,σ (X ) = σ + µ Method of momet estimator are ˆµ = X, 7

18 ˆσ = X i X = (X i X). X is ubiased ad cosistet estimator for µ. E(ˆσ ) = E( (Xi X) ) = E( (Xi X) ) = σ σ ˆσ is ot ubiased for σ ˆσ = X i X p E(X ) µ = σ ˆσ is cosistet for σ. Maximum Likelihood Estimator : Let X,..., X be a radom sample with p.d.f. f(x, θ). The joit p.d.f. of X,..., X is f(x,..., x, θ) = f(x i, θ), x i R, i =,..., Let Θ be the space of possible values of θ. We call Θ the parameter space. Def. The likelihood fuctio of a radom sample is defied as its joit p.d.f. as L(θ) = L(θ, x,..., x ) = f(x,..., x, θ), θ Θ. which is cosidered as a fuctio of θ. For (x,..., x ) fixed, the value L(θ, x,..., x ) is called the likelihood at θ. Give observatio x,..., x, the likelihood L(θ, x,..., x ) is cosidered as the probability that X = x,..., X = x occurs whe θ is true. Def. Let ˆθ = ˆθ(x,..., x ) be ay value of θ that maximizes L(θ, x,..., x ). The we call ˆθ = ˆθ(x,..., x ) the maximum likelihood estimator (m.l.e) of θ. Whe X = x,..., X = x is observed, we call ˆθ = ˆθ(x,..., x ) the maximum likelihood estimate of θ. Note : (a) Why m.l.e? Whe L(θ, x,..., x ) L(θ, x,..., x ), we are more cofidet to believe θ = θ tha to believe θ = θ 8

19 (b) How to derive m.l.e? l x = > 0 l x is i x x x If L(θ ) L(θ ), the l L(θ ) l L(θ ) If ˆθ is the m.l.e., the L(ˆθ, x,..., x ) = max L(θ, x,..., x ) ad θ Θ l L(ˆθ, x,..., x ) = max l L(θ, x,..., x ) θ Θ Two cases to solve m.l.e. : l L(θ) (b.) = 0 θ (b.) L(θ) is mootoe. Solve max L(θ, x,..., x ) from mootoe θ Θ property. Order statistics: Let (X,..., X ) be a radom sample with d.f. F ad p.d.f. f. Let (Y,..., Y ) be a permutatio (X,..., X ) such that Y Y Y. The we call (Y,..., Y ) the order statistic of (X,..., X ) where Y is the first (smallest) order statistic, Y is the secod order statistic,..., Y is the largest order statistic. If (X,..., X ) are idepedet, the P (X A, X A,..., X A ) = f(x,..., x )dx dx A A = f (x )dx f (x )dx A A = P (X A ) P (X A ) Thm. Let (X,..., X ) be a radom sample from a cotiuous distributio with p.d.f. f(x) ad d.f F (x). The the p.d.f. of Y = max{x,..., X } is g (y) = (F (y)) f(y) ad the p.d.f. of Y = mi{x,..., X } is g (y) = ( F (y)) f(y) Proof. This is a R R trasformatio. Distributio fuctio of Y is G (y) = P (Y y) = P (max{x,..., X } y) = P (X y,..., X y) = P (X y)p (X y) P (X y) = (F (y)) 9

20 p.d.f. of Y is g (y) = D y (F (y)) = (F (y)) f(y) Distributio fuctio of Y is G (y) = P (Y y) = P (mi{x,..., X } y) = P (mi{x,..., X } > y) = P (X > y, X > y,..., X > y) = P (X > y)p (X > y) P (X > y) = ( F (y)) p.d.f. of Y is g (y) = D y ( ( F (y)) ) = ( F (y)) f(y) Example : Let (X,..., X ) be a radom sample from U(0, θ). Fid m.l.e. of θ. Is it ubiased ad cosistet? sol: The p.d.f. of X is f(x, θ) = { θ if 0 x θ 0 elsewhere. Cosider the idicator fuctio I (a,b) (x) = { if a x b 0 elsewhere. The f(x, θ) = θ I [0,θ](x). The likelihood fuctio is L(θ) = f(x i, θ) = θ I [0,θ](x i ) = θ I [0,θ] (x i ) Let Y = max{x,..., X } The I [0,θ] (x i ) = 0 x i θ, for all i =,..., 0 y θ We the have L(θ) = θ I [0,θ](y ) = θ I [y, ](θ) = { θ if θ y 0 if θ < y L(θ) is maximized whe θ = y. The m.l.e. of θ is ˆθ = Y The d.f. of x is F (x) = P (X x) = x 0 θ dt = x θ, 0 x θ 0

21 The p.d.f. of Y is g (y) = ( y θ ) θ = y θ, 0 y θ E(Y ) = θ y y dy = θ θ m.l.e. ˆθ = Y 0 θ + is ot ubiased. However, E(Y ) = θ θ as, m.l.e. ˆθ is asymptotically ubiased. + E(Y ) = Var(Y ) = E(Y ) (EY ) = θ 0 y y θ dy = + θ + ) θ θ θ = 0 as. + θ ( P Y θ m.l.e. ˆθ = Y is cosistet for θ. Is there ubiased estimator for θ? E( + Y ) = + E(Y ) = + +Y is ubiased for θ. Example : (a) Y b(, p) The likelihood fuctio is l L(p) p L(p) = f Y (y, p) = l L(p) = l = y p y p = 0 y p = y p + θ = θ ( ) p y ( p) y y ( ) + y l p + ( y) l ( p) y m.l.e. ˆp = Y E(ˆp) = E(Y ) = p m.l.e. ˆp = Y is ubiased. Var(ˆp) = Var(Y ) = p( p) 0 as m.l.e. ˆp = Y is cosistet for p. y( p) = p( y) y = p (b) X,..., X are a radom sample from N(µ, σ ). Wat m.l.e. s of µ ad σ The likelihood fuctio is L(µ, σ ) = π(σ ) e (x i µ) σ = (π) (σ ) (x e i µ) σ

22 l L(µ, σ ) = ( ) l (π) l σ σ l L(ˆµ, σ ) σ l L(µ, σ ) µ = σ + σ 4 (x i µ) = (x σ i µ) = 0 ˆµ = X (x i x) = 0 ˆσ = (x i x) E(ˆµ) = E(X) = µ (ubiased),var(ˆµ) = Var(X) = σ 0 as m.l.e. ˆµ is cosistet for µ. E(ˆσ ) = E( (Xi X) ) = σ σ (biased). E(ˆσ ) = σ σ as ˆσ is asymptotically ubiased. Var(ˆσ ) = Var( (x i x) ) = (x i x) Var(σ ) σ (x i x) = σ4 Var( ( ) ) = σ 4 0 as σ m.l.e. ˆσ is cosistet for σ. Suppose that we have m.l.e. ˆθ = ˆθ(x,..., x ) for parameter θ ad our iterest is a ew parameter τ(θ), a fuctio of θ. What is the m.l.e. of τ(θ)? The space of τ(θ) is T = {τ : θ Θ s.t τ = τ(θ)} Thm. If ˆθ = ˆθ(x,..., x ) is the m.l.e. of θ ad τ(θ) is a - fuctio of θ, the m.l.e. of τ(θ) is τ(ˆθ) Proof. The likelihood fuctio for θ is L(θ, x,..., x ). The the likelihood fuctio for τ(θ) ca be derived as follows : L(θ, x,..., x ) = L(τ (τ(θ)), x,..., x ) = M(τ(θ), x,..., x ) = M(τ, x,..., x ), τ T

23 M(τ(ˆθ), x,..., x ) = L(τ (τ(ˆθ), x,..., x ) τ(ˆθ) is m.l.e. of τ(θ). This is the ivariace property of m.l.e. Example : ()If Y b(, p), m.l.e of p is ˆp = Y τ(p) m.l.e of τ(p) p p = ( Y ) p p = e p e p Y ê p = e Y ê p = e Y = L(ˆθ, x,..., x ) L(θ, x,..., x ), θ Θ = L(τ (τ(θ)), x,..., x ) = M(τ(θ), x,..., x ), θ Θ = M(τ, x,..., x ), τ T p( p) is ot a - fuctio of p. iid () X,..., X N(µ, σ ), m.l.e. s of (µ, σ ) is (X, (Xi X) ). m.l.e. s of (µ, σ) is (X, (Xi X) ) ( σ (0, ) σ σ is -) You ca also solve l L(µ, σ, x,..., x ) = 0 µ l L(µ, σ, x,..., x ) = 0 for µ, σ σ (µ, σ) is ot a - fuctio of (µ, σ ). ( µ (, ) µ µ is t -) Best estimator : Def. A ubiased estimator ˆθ = ˆθ(X,..., X ) is called a uiformly miimum variace ubiased estimator (UMVUE) or best estimator if for ay ubiased estimator ˆθ,we have Var θ ˆθ Varθ ˆθ, for θ Θ (ˆθ is uiformly better tha ˆθ i variace. ) 3

24 There are several ways i derivig UMVUE of θ. Cramer-Rao lower boud for variace of ubiased estimator : Regularity coditios : (a) Parameter space Θ is a ope iterval. costats ot depedig o θ. (a, ), (a, b), (b, ), a,b are (b) Set {x : f(x, θ) = 0} is idepedet of θ. (c) f(x,θ) dx = θ θ f(x, θ)dx = 0 (d) If T = t(x,..., x ) is a ubiased estimator, the f(x, θ) t dx = tf(x, θ)dx θ θ Thm. Cramer-Rao (C-R) Suppose that the regularity coditios hold. If ˆτ(θ) = t(x,..., X ) is ubiased for τ(θ), the (τ (θ)) Var θ ˆτ(θ) l f(x,θ) E θ [( ) θ ] = (τ (θ)) E θ [( l f(x,θ) )] for θ Θ θ Proof. Cosider oly the cotiuous distributio. E[ l f(x, θ) ] = θ l f(x, θ) f(x, θ)dx = θ = f(x, θ)dx = 0 θ τ(θ) = E θˆτ(θ) = E θ (t(x,..., x )) = t(x,..., x ) f(x, θ) dx θ f(x i, θ) dx i Takig derivatives both sides. τ (θ) = t(x,..., x ) f(x i, θ) dx i τ(θ) f(x i, θ) dx i θ θ = t(x,..., x ) f(x i, θ) dx i τ(θ) f(x i, θ) dx i θ θ = (t(x,..., x ) τ(θ))( f(x i, θ)) dx i θ 4

25 Now, θ f(x i, θ) = θ [f(x, θ)f(x, θ) f(x, θ)] = ( θ f(x, θ)) i f(x i, θ) + + ( θ f(x, θ)) i f(x i, θ) = = = j= j= j= θ f(x j, θ) f(x i, θ) i j l f(x j, θ) f(x j, θ) f(x i, θ) θ i j l f(x j, θ) f(x i, θ) θ j= Cauchy-Swartz Iequality The τ (θ) = [E(XY )] E(X )E(Y ) (t(x,..., x ) τ(θ))( = E[(t(x,..., x ) τ(θ)) j= (τ (θ)) E[(t(x,..., x ) τ(θ)) ] E[( Var(ˆτ(θ)) Sice E[( j= (τ (θ)) E[( l f(x j,θ) ) θ ] j= l f(x j, θ) ) ] = θ = j= j= j= l f(x j, θ) ) θ l f(x j, θ) ] θ j= E( l f(x j, θ) ) + θ i j E( l f(x j, θ) ) θ = E( l f(x j, θ) ) θ 5 l f(x j, θ) ) ] θ f(x i, θ) E( l f(x j, θ) θ dx i l f(x i, θ) ) θ

26 The, we have You may further check that Var θ ˆτ(θ) (τ (θ)) l f(x,θ) E θ [( ) θ ] E θ ( l f(x, θ) l f(x, θ) ) = E θ θ ( ) θ Thm. If there is a ubiased estimator ˆτ(θ) with variace achievig the Cramer-Rao lower boud,the ˆτ(θ) is a UMVUE of τ(θ). (τ (θ)) E θ [( l f(x,θ) θ )] Note: If τ(θ) = θ, the ay ubiased estimator ˆθ satisfies Example: Var θ (ˆθ) (τ (θ)) E θ ( l f(x,θ) θ ) (a)x,..., X iid Poisso(λ), E(X) = λ, Var(X) = λ. MLE ˆλ = X, E(ˆλ) = λ, Var(ˆλ) = λ. p.d.f. f(x, λ) = λx e λ x!, x = 0,,... l f(x, λ) = x l λ λ l x! λ l f(x, λ) = x λ λ l f(x, λ) = x λ E( λ l f(x, λ)) = E( x λ ) = E(X) λ Cramer-Rao lower boud is ( λ ) = λ = Var(ˆλ) MLE ˆλ = X is the UMVUE of λ. = λ 6

27 (b)x,..., X iid Beroulli(p), E(X) = p, Var(X) = p( p). Wat UMVUE of p. p.d.f f(x, p) = p x ( p) x l f(x, p) = x l p + ( x) l( p) p l f(x, p) = x p x p p l f(x, p) = x p + x ( p) E( p l f(x, p)) = E( X p + X ( p) ) = p + p = p( p) C-R lower boud for p is p( p) ( = ) p( p) m.l.e. of p is ˆp = X E(ˆp) = E(X) = p, Var(ˆp) = Var(X) = p( p) MLE ˆp is the UMVUE of p. = C-R lower boud. 7

28 Chapter 4. Cotiue to Poit Estimatio-UMVUE Sufficiet Statistic: A,B are two evets. The coditioal probability of A give B is P (A B) = P (A B), A S. P (B) P ( B) is a probability set fuctio with domai of subsets of sample space S. Let X,Y be two r.v s with joit p.d.f f(x, y) ad margial p.d.f s f X (x) ad f Y (y). The coditioal p.d.f of Y give X = x is f(y x) = f(x, y) f X (x), y R Fuctio f(y x) is a p.d.f satisfyig f(y x)dy = I estimatio of parameter θ, we have a radom sample X,..., X from p.d.f f(x, θ). The iformatio we have about θ is cotaied i X,..., X. Let U = u(x,..., X ) be a statistic havig p.d.f f U (u, θ) The coditioal p.d.f X,..., X give U = u is f(x,..., x u) = f(x,..., x, θ), {(x,..., x ) : u(x,..., x ) = u} f U (u, θ) Fuctio f(x,..., x u) is a joit p.d.f with u(x,...,x )=u f(x,..., x u)dx dx = Let X be r.v. ad U = u(x) f(x U = u) = f(x, u) f U (u) = { fx (x) f U (u) 0 = 0 f U (u) if u(x) = u if u(x) u If, for ay u, coditioal p.d.f f(x,..., x, θ u) is urelated to parameter θ, the the radom sample X,..., X cotais o iformatio about θ whe U = u is observed. This says that U cotais exactly the same amout of iformatio about θ as X,..., X. Def. Let X,..., X be a radom sample from a distributio with p.d.f f(x, θ), θ Θ. We call a statistic U = u(x,..., X ) a sufficiet statistic if, for ay value U = u, the coditioal p.d.f f(x,..., x u) ad its domai all ot 8

29 deped o parameter θ. Let U = (X,..., X ). The f(x,..., x, θ u = (x, x,..., x )) = { f(x,...,x,θ) f(x,x,...,x,θ) The (X,..., X ) itself is a sufficiet statistic of θ. if x = x, x = x,..., x = x 0 if x i x i for some i s. Q: Why sufficiecy? A: We wat a statistic with dimesio as small as possible ad cotais iformatio about θ the same amout as X,..., X does. Def. If U = u(x,..., X ) is a sufficiet statistic with smallest dimesio, it is called the miimal sufficiet statistic. Example: (a) Let (X,..., X ) be a radom sample from a cotiuous distributio with p.d.f f(x, θ). Cosider the order statistic Y = mi{x,..., X },..., Y = max{x,..., X }. If Y = y,..., Y = y are observed, sample X,..., X have equal chace to have values i {(x,..., x ) : (x,..., x ) is a permutatio of (y,..., y )}. The the coditioal joit p.d.f of X,..., X give Y = y,..., Y = y is { if x f(x,..., x, θ y,..., y ) =!,..., x is a permutatio of y,..., y. 0 otherwise. The order statistic (Y,..., Y ) is also a sufficiet statistic of θ. Order statistic is ot a good sufficiet statistic sice it has dimesio. (b)let X,..., X be a radom sample from Beroulli distributio. The joit p.d.f of X,..., X is f(x,..., x, p) = p x i ( p) x i = p x i ( p) x i, x i = 0,, i =,...,. Cosider the statistic Y = X i which has biomial distributio b(, p) with p.d.f f Y (y, p) = ( ) p y ( p) y, y = 0,,..., y 9

30 If Y = y, the space of (X,..., X ) is {(x,..., x ) : The coditioal p.d.f of X,..., X give Y = y is x i( p) x i p x i = y} = py ( p) y ( y)p f(x,..., x, p y) = y ( p) y ( = y)p y ( p) y ( y) = if ( x i ) 0 if x i = y x i y which is idepedet of p. Hece, Y = X i is a sufficiet statistic of p ad is a miimal sufficiet statistic. (c)let X,..., X be a radom sample from uiform distributio U(0, θ). Wat to show that the largest order statistic Y = max{x,..., X } is a sufficiet statistic. The joit p.d.f of X,..., X is f(x,..., x, θ) = The p.d.f of Y is = { θ I(0 < x i < θ) = θ θ I(0 < x i < θ) if 0 < x i < θ, i =,..., 0 otherwise. f Y (y, θ) = ( y θ ) θ = y θ, 0 < y < θ Whe Y = y is give, X,..., X be values with 0 < x i y, i =,..., The coditioal p.d.f of X,..., X give Y = y is f(x,..., x y) = f(x,..., x, θ) f Y (y, θ) = θ = y y θ 0 otherwise. idepedet of θ. So, Y = max{x,..., X } is a sufficiet statistic of θ. 0 < x i y, i =,..., Q: 30

31 (a) If U is a sufficiet statistic, are U+5, U, cos(u) all sufficiet for θ? (b) Is there easier way i fidig sufficiet statistic? T = t(x,..., X ) is sufficiet for θ if coditioal p.d.f f(x,..., x, θ t) is idep. of θ. Idepedece:.fuctio f(x,..., x, θ t) ot deped o θ..domai of X,..., X ot deped o θ. Thm. Factorizatio Theorem. Let X,..., X be a radom sample from a distributio with p.d.f f(x, θ). A statistic U = u(x,..., X ) is sufficiet for θ iff there exists fuctios K, K 0 such that the joit p.d.f of X,..., X may be formulated as f(x,..., x, θ) = K (u(x,..., X ), θ)k (x,..., x ) where K is ot a fuctio of θ. Proof. Cosider oly the cotiuous r.v s. ) If U is sufficiet for θ, the f(x,..., x, θ u) = f(x,..., x, θ) is ot a fuctio of θ f U (u, θ) f(x,..., x, θ) = f U (u(x,..., X ), θ)f(x,..., x u) = K (u(x,..., X ), θ)k (x,..., x ) ) Suppose that f(x,..., x, θ) = K (u(x,..., X ), θ)k (x,..., x ) Let Y = u (X,..., X ), Y = u (X,..., X ),..., Y = u (X,..., X ) be a - fuctio with iverse fuctios x = w (y,..., y ), x = w (y,..., y ),..., x = w (y,..., y ) ad Jacobia J = x y The joit p.d.f of Y,..., Y is. x y x y x y. (ot deped o θ.) f Y,...,Y (y,..., y, θ) = f(w (y,..., y ),..., w (y,..., y ), θ) J = K (y, θ)k (w (y,..., y ),..., w (y,..., y ), θ) J 3

32 The margial p.d.f of U = Y is f U (y, θ) = K (y, θ) K (w (y,..., y ),..., w (y,..., y )) J dy dy } {{ } ot deped o θ. The the coditioal p.d.f of X,..., X give U = u is f(x,..., x, θ u) = f(x,..., x, θ) f U (u, θ) K (x,..., x ) = K (w (y,..., y ),..., w (y,..., y ), θ) J dy dy which is idepedet of θ. This idicates that U is sufficiet for θ. Example : (a)x,..., X is a radom sample from Poisso(λ).Wat sufficiet statistic for λ. Joit p.d.f of X,..., X is λ x i e λ f(x,..., x, λ) = = λ x i e λ x i! = λ x i e λ x i! x i! = K ( X i is sufficiet for λ. We also have X = x i, λ)k (x,..., x ) f(x,..., x, λ) = λ x e λ = K (x, λ)k (x,..., x ) x i! X i is sufficiet for λ. We also have f(x,..., x, λ) = λ (x ) e λ = K (x, λ)k (x,..., x ) x i! X is sufficiet for λ. 3

33 (b)let X,..., X be a radom sample from N(µ, σ ).Wat sufficiet statistic for (µ, σ ). Joit p.d.f of X,..., X is f(x,..., x, µ, σ ) = (x i µ) = e (x i µ) πσ (x i x+x µ) = f(x,..., x, µ, σ ) = (s = (π) (σ ) (X, s ) is sufficiet for (µ, σ ). σ = (π) (σ ) (x i µ) e σ (x i x) +(x µ) = ( )s +(x µ) (x i x) ) e ( )s +(x µ) σ = K (x, s, µ, σ )K (x,..., x ) What is useful with a sufficiet statistic for poit estimatio? Review : X, Y r.v. s with joi p.d.f f(x, y). Coditioal p.d.f f(x, y) f(y x) = f X (x) f(x, y) = f(y x)f X(x) f(x, y) f(x y) = f Y (y) f(x, y) = f(x y)f Y (y) Coditioal expectatio of Y give X = x is E(Y x) = yf(y x)dy The radom coditioal expectatio E(Y X) is fuctio E(Y x) with x replaced by X. Coditioal variace of Y give X = x is Var(Y x) = E[(Y E(Y x)) x] = E(Y x) (E(Y x)) The coditioal variace Var(Y X) is Var(Y x) replacig x by X. Thm. Let Y ad X be two r.v. s. (a) E[E(Y x)] = E(Y ) (b) Var(Y ) = E(Var(Y x)) + Var(E(Y x) 33

34 Proof. (a) E[E(Y x)] = = = = = = E(Y ) E(Y x)f X (x)dx y( yf(y x)dyf X (x)dx yf(x, y)dxdy yf Y (y)dy f(x, y)dx)dy (b) Var(Y x) = E(Y x) (E(Y x)) E(Var(Y x)) = E[E(Y x)] E[(E(Y x)) ] = E(Y ) E[(E(Y x)) ] Also, Var(E(Y x) = E[(E(Y x)) ] E[(E(Y x))] = E[(E(Y x)) ] (E(Y )) E(Var(Y x)) + Var(E(Y x) = E(Y ) (E(Y )) = Var(Y ) Now, we comeback to the estimatio of parameter fuctio τ(θ). We have a radom sample X,..., X from f(x, θ). Lemma. Let ˆτ(X,..., X ) be a ubiased estimator of τ(θ) ad U = u(x,..., X ) is a statistic.the (a)e θ [ˆτ(X,..., X ) U] is ubiased for τ(θ) (b)var θ (E[ˆτ(X,..., X ) U]) Var θ (ˆτ(X,..., X )) Proof. (a) E θ [E(ˆτ(X,..., X ) U)] = E θ (ˆτ(X,..., X )) = τ(θ), θ Θ. The E θ [ˆτ(X,..., X ) U] is ubiased for τ(θ). (b) Var θ (ˆτ(X,..., X )) = E θ [Var θ (ˆτ(X,..., X ) U)] + Var θ [E θ (ˆτ(X,..., X ) U) Var θ [E θ (ˆτ(X,..., X ) U)], θ Θ. 34

35 Coclusios: (a) For ay estimator ˆτ(X,..., X ) which is ubiased for τ(θ), ad ay statistic U, E θ [ˆτ(X,..., X ) U] is ubiased for τ(θ) ad with variace smaller tha or equal to ˆτ(X,..., X ). (b) However, E θ [ˆτ(X,..., X ) U] may ot be a statistic. If it is ot, it caot be a estimator of τ(θ). (c)if U is a sufficiet statistic, f(x,..., x, θ u) is idepedet of θ, the E θ [ˆτ(X,..., X ) u] is idepedet of θ. So, E θ [ˆτ(X,..., X ) U] is a ubiased estimator. If U is ot a sufficiet statistic, f(x,..., x, θ u) is ot oly a fuctio of u but also a fuctio of θ, the E θ [ˆτ(X,..., X ) u] is a fuctio of u ad θ. Ad E θ [ˆτ(X,..., X ) u] is ot a statistic. Thm. Rao-Blackwell If ˆτ(X,..., X ) is ubiased for τ(θ) ad U is a sufficiet statistic, the (a)e θ [ˆτ(X,..., X ) U] is a statistic. (b)e θ [ˆτ(X,..., X ) U] is ubiased for τ(θ). (c)var θ (E[ˆτ(X,..., X ) U]) Var θ (ˆτ(X,..., X )), θ Θ. If ˆτ(θ) is a ubiased estimator for τ(θ) ad U, U,... are sufficiet statistics, the we ca improve ˆτ(θ) with the followig fact: Var θ (E[ˆτ(θ) U ]) Var θˆτ(θ) Var θ E(E(ˆτ(θ) U ) U ) Var θ E(ˆτ(θ) U ) Var θ E[E(E(ˆτ(θ) U ) U ) U 3 ] Var θ E(E(ˆτ(θ) U ) U ) Will this process eds with Cramer-Rao lower boud? This ca be solved with complete statistic. Note: Let U be a statistic ad h is a fuctio. (a) If h(u) = 0 the E θ (h(u)) = E θ (0) = 0, θ Θ.. 35

36 (b) If P θ (h(u) = 0) =, θ Θ.h(U) has a p.d.f {, if h = 0 f h(u) (h) = The E θ (h(u)) = hf h(u) (h) = 0 0, otherwise. all h Def. X,..., X is radom sample from f(x, θ). A statistic U = u(x,..., X ) is a complete statistic if for ay fuctio h(u) such that E θ (h(u)) = 0, θ Θ, the P θ (h(u) = 0) =, for θ Θ. Q : For ay statistic U, how ca we verify if it is complete or ot complete? A : () To prove completeess, you eed to show that for ay fuctio h(u) with 0 = E θ (h(u)), θ Θ.the followig = P θ (h(u) = 0), θ Θ hold. ()To prove i-completeess, you eed oly to fid oe fuctio h(u) that satisfies E θ (h(u)) = 0, θ Θ ad P θ (h(u) = 0) <, for some θ Θ. Examples: iid (a)x,..., X Beroulli(p) Fid a complete statistic ad i-complete statistic? sol: (a.) We show that Y = X i is a complete statistic. Y b(, p). Suppose that fuctio h(y ) satisfies 0 = E p h(y ), 0 < p < Now, ( ) 0 = E p h(y ) = h(y) p y ( p) y y y=0 ( ) = ( p) p h(y) ( y p )y, 0 < p < 0 = y=0 ( ) p h(y) ( y p )y, 0 < p < y=0 (Let θ = p p, 0 < p < 0 < θ < ) ( ) 0 = h(y) θ y, 0 < θ < y y=0 36

37 A order + polyomial equatio caot have ifiite solutios except that coefficiets are zero s. ( ) h(y) = 0, y = 0,..., for 0 < θ < y h(y) = 0, y = 0,..., for 0 < p <. P p (h(y ) = 0) P p (Y = 0,..., ) = Y = X i is complete (a.) We show that Z = X X is ot complete. E p Z = E p (X X ) = E p X E p X = p p = 0, 0 < p < P p (Z = 0) = P p (X X = 0) = P p (X = X = 0 or X = X = ) = P p (X = X = 0) + P p (X = X = ) = ( p) + p < for 0 < p <. Z = X X is ot complete. (b)let (X,..., X ) be a radom sample from U(0, θ). We have to show that Y = max{x,..., X } is a sufficiet statistic. Here we use Factorizatio theorem to prove it agai. f(x,..., x, θ) = θ I(0 < x i < θ) = θ = θ I(0 < y < θ) Y is sufficiet for θ Now, we prove it complete. The p.d.f of Y is I(0 < x i < θ, i =,..., ) f Y (y) = ( y θ ) θ = θ y, 0 < y < θ 37

38 Suppose that h(y ) satisfies 0 = E θ h(y ), 0 < θ < 0 = E θ h(y ) = 0 = θ 0 θ 0 h(y) θ y dy = θ h(y)y dy θ h(y)y dy, θ > 0 Takig differetiatio both sides with θ. 0 = h(θ)θ, θ > 0 0 = h(y), 0 < y < θ, θ > 0 P θ (h(y ) = 0) = P θ (0 < Y < θ) =, θ > 0 Y = max{x,..., X } is complete. Def. If the p.d.f of r.v. X ca be formulated as f(x, θ) = e a(x)b(θ)+c(θ)+d(x), l < x < q where l ad q do ot deped o θ, the we say that f belogs to a expoetial family. Thm. Let X,..., X be a radom sample from f(x, θ) which belogs to a expoetial family as f(x, θ) = e a(x)b(θ)+c(θ)+d(x), l < x < q The a(x i ) is a complete ad sufficiet statistic. Note: We say that X = Y if P (X = Y ) =. Thm. Lehma-Scheffe Let X,..., X be a radom sample from f(x, θ). Suppose that U = u(x,..., X ) is a complete ad sufficiet statistic. If ˆτ = t(u) is ubiased for τ(θ), the ˆτ is the uique fuctio of U ubiased for τ(θ)ad is a UMVUE of τ(θ). (Ubiased fuctio of complete ad sufficiet statistic is UMVUE.) Proof. If ˆτ = t (U) is also ubiased for τ(θ), the E θ (ˆτ ˆτ ) = E θ (ˆτ) E θ (ˆτ ) = τ(θ) τ(θ) = 0, θ Θ. = P θ (ˆτ ˆτ = 0) = P (ˆτ = ˆτ ), θ Θ. ˆτ = ˆτ, ubiased fuctio of U is uique. If T is ay ubiased estimator of τ(θ) the Rao-Blackwell theorem gives: 38 0

39 (a) E(T U) is ubiased estimator of τ(θ). By uiqueess, E(T U) = ˆτ with probability. (b) Var θ (ˆτ) = Var θ (E(T U)) Var θ (T ), θ Θ. This holds for every ubiased estimator T. The ˆτ is UMVUE of τ(θ) Two ways i costructig UMVUE based o a complete ad sufficiet statistic U: (a) If T is ubiased for τ(θ), the E(T U) is the UMVUE of τ(θ). This is easy to defie but difficult to trasform it i a simple form. (b) If there is a costat such that E(U) = c θ, the T = U is the UMVUE c of θ. Example : (a)let X,..., X be a radom sample from U(0, θ). Wat UMVUE of θ. sol: Y = max{x,..., X } is a complete ad sufficiet statistic. The p.d.f of Y is f Y (y, θ) = ( y θ ) θ = y, 0 < y < θ E(Y ) = θ We the have E( +Y ) = +E(Y ) = θ. So, +Y is the UMVUE of θ. 0 θ y y θ dy = + θ. (b) Let X,..., X be a radom sample from Beroulli(p). Wat UMVUE of θ. sol: The p.d.f is f(x, p) = p x ( p) x p = ( p)( p )x = e x l( X i is complete ad sufficiet. E( X i ) = E(X i ) = p ˆp = X i = X is UMVUE of p. p p )+l( p) 39

40 (c)x,..., X iid N(µ, ). Wat UMVUE of µ. sol: The p.d.f of X is f(x, µ) = e (x µ) = e (x µx+µ ) x µx = e µ l π π π X i is complete ad sufficiet. E( X i ) = E(X i ) = µ ˆµ = X i = X is UMVUE of µ. Sice X is ubiased, we see that E(X X i ) = X (d)x,..., X iid Possio(λ). Wat UMVUE of e λ. sol: The p.d.f of X is f(x, λ) = x! λx e λ x l λ λ l x! = e X i is complete ad sufficiet. E(I(X = 0)) = P (X = 0) = f(0, λ) = e λ where I(X = 0) is a idicator fuctio. I(X = 0) is ubiased for e λ E(I(X = 0) X i ) is UMVUE of e λ. 40

41 Chapter 5. Cofidece Iterval Let Z be the r.v. with stadard ormal distributio N(0, ) We ca fid z α ad z α that satisfy α = P (Z z α ) = P (Z z α ) ad α = P ( z α Z z α ). A table of z α is the followig : α z α (z 0. ) (z 0.05 ) (z 0.05 ) (z ) (z ) Def. Suppose that we have a radom sample from f(x, θ). For 0 < α <, if there exists two statistics T = t (X,..., X ) ad T = t (X,..., X ) satisfyig α = P (T θ T ) We call the radom iterval (T, T ) a 00( α)% cofidece iterval of parameter θ. If X = x,..., X = x is observed, we also call (t (X,..., X ), t (X,..., X )) a 00( α)% cofidece iterval(c.i.) for θ Costructig C.I. by pivotal quatity: Def. A fuctio of radom sample ad parameter, Q = q(x,..., X, θ), is called a pivotal quatity if its distributio is idepedet of θ With a pivotal quatity q(x,..., X, θ), there exists a, b such that α = P (a q(x,..., X, θ) b), θ Θ. The iterest of pivotal quatity is that there exists statistics T = t (X,..., X ) ad T = t (X,..., X ) with the followig - trasformatio a q(x,..., X, θ) b iff T θ T The we have α = P (T θ T ) ad (T, T ) is a 00( α)% C.I. for θ Cofidece Iterval for Normal mea: Let X,..., X be a radom sample from N(µ, σ ). We cosider the C.I. of 4

42 parameter µ. (I) σ = σ 0 is kow X N(µ, σ 0 ) X µ σ 0 / N(0, ) (X z α α = P ( z α Z z α ), Z N(0, ) = P ( z α X µ σ 0 / z α ) σ 0 σ = P ( z α X µ z α 0 ) = P (X z α σ 0 µ X + z α σ 0, X + z α σ 0 ) is a 00( α)% C.I. for µ. σ 0 ) ex: = 40, σ 0 = iid 0, x = 7.64 (X,..., X 40 N(µ, 0).) Wat a 80% C.I. for µ. sol: A 80% C.I. for µ. is σ 0 σ (X z α 0 0 0, X + z α ) = (7.64.8, ) P (X z α = (6.53, 7.805) σ 0 µ X + z α σ 0 ) = α = 0.8 (II)σ is ukow. P (6.53 µ 7.805) = or 0 Def. If Z N(0, ) ad χ (r) are idepedet, we call the distributio of the r.v. T = Z χ (r) r a t-distributio with r degrees of freedom. The p.d.f of t-distributio is f T (t) = Γ( r+ ) Γ( r ) rπ( + t ) r+ r, < t < 4

43 f T ( t) = f T (t) t-distributio is symmetric at 0. iid Now X,..., X N(µ, σ ). We have { X N(µ, σ ( )s σ ) χ ( ) idep. { X µ σ/ ( )s σ N(0, ) χ ( ) idep. Let t α satisfies T = X µ σ/ ( )s σ ( ) = X µ s/ t( ) (X t α s, X + t α α = P ( t α X µ s/ t α = P ( t α = P (X t α ) s X µ t α s ) s µ X + t α s ) is a 00( α)% C.I. for µ. s ) ex: Suppose that we have = 0, x = 3. ad s =.7. We also have t 0.05 =.6. Wat a 95% C.I. for µ. sol: A 95% C.I. for µ is ( , ) = (.34, 4.0) 43