![(i) Show that 15 (ii) Show that (X) 5/12 and E(Y) 5/8 3(1 - 2X2 +X4) 4(2- 3X +X3) (iii) Show that 3(y|X) (iv) Verify thatE(Y)E(Y) 14] 7. (a) State Chebyshevs inequality and prove it using Markovs inequality 15] (b) Let (2, P) be a probability space representing a random experiment that can be repeated many times under the same conditions, and let A C S2 be a random event. Suppose the experiment is repeated n times (i) Write down an expression for the relative frequency of event A (ii) Show that the relative frequence of A converges in probability to P(A) 13J as the number of repetitions -> oo 17] (c) Let X be a random variable whose mean ,1 and variance ơ2 are both finite and let Xi, X2,... , Xn be a random sample from the distribution of X. Show that the distribution of the statistic TI TE converges to the standard normal distribution N(0, 1) asn-c [10] PLEASE TURN OVER](http://img.homeworklib.com/questions/74533a60-7013-11ea-a3ab-df623373ff6a.png?x-oss-process=image/resize,w_560)
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(i) Show that 15 (ii) Show that (X) 5/12 and E(Y) 5/8 3(1 - 2X2 +X4)...
7. (a) State Chebyshev's inequality and prove it using Markov's inequality. 151 (b) Let (2, P)...
7. (a) State Chebyshev's inequality and prove it using Markov's inequality. 151 (b) Let (2, P) be a probability space representing a random experiment that can be repeated many times under the same conditions, and let A S2 be a random event. Suppose the experiment is repeated n times. (i) Write down an expression for the relative frequency of event A 131 ) Show that the relative frequence of A converges in probability to P(A) as the number of repetitions...
1) Let X and Y be random variables. Show that Cov( X + Y, X-Y) Var(X)--Var(Y)...
1) Let X and Y be random variables. Show that Cov( X + Y, X-Y) Var(X)--Var(Y) without appealing to the general formulas for the covariance of the linear combinations of sets of random variables; use the basic identity Cov(Z1,22)-E[Z1Z2]- E[Z1 E[Z2, valid for any two random variables, and the properties of the expected value 2) Let X be the normal random variable with zero mean and standard deviation Let ?(t) be the distribution function of the standard normal random variable....
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y)...
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
Please answer all the parts neatly with all details. 3. Assume X1, X2,... are a sequence...
Please answer all the parts neatly with all details.
3. Assume X1, X2,... are a sequence of i.i.d. random variables having finite first moment, that is, v = E(Xi oo. Let Yn = (|X1| .+ |Xn|)/n. (a) Show that Yn ->v in probability. (b) Show that E(Y,) -- v. (c) Show that E(|X, - /u|) -0 where u = E(X)
3. Assume X1, X2,... are a sequence of i.i.d. random variables having finite first moment, that is, v = E(Xi...
Dr. Beldi Qiang STATWOB Flotllework #1 1. Let X.,No X~ be a i.İ.d sample form Exp(1),...
Dr. Beldi Qiang STATWOB Flotllework #1 1. Let X.,No X~ be a i.İ.d sample form Exp(1), and Y-Σ-x. (a) Use CLT to get a large sample distribution of Y (b) For n 100, give an approximation for P(Y> 100) (c) Let X be the sample mean, then approximate P(.IX <1.2) for n 100. x, from CDF F(r)-1-1/z for 1 e li,00) and ,ero 2Consider a random sample Xi.x, 、 otherwise. (a) Find the limiting distribution of Xim the smallest order...
May 21, 2019 R 3+3+5-11 points) (a) Let X1,X2, . . Xn be a random sample from G distribution. Show that T(Xi, . . . , x,)-IT-i xi is a sufficient statistic for a (Justify your work). (b) Is Unifo...
May 21, 2019 R 3+3+5-11 points) (a) Let X1,X2, . . Xn be a random sample from G distribution. Show that T(Xi, . . . , x,)-IT-i xi is a sufficient statistic for a (Justify your work). (b) Is Uniform(0,0) a complete family? Explain why or why not (Justify your work) (c) Let X1, X2, . .., Xn denote a random sample of size n >1 from Exponential(A). Prove that (n - 1)/1X, is the MVUE of A. (Show steps.)....
5. Let Xi,..., X, be iid N(e, 1). (a) Show that X is a complete sufficient statistic. (b) Show th...
1.(c)
2.(a),(b)
5. Let Xi,..., X, be iid N(e, 1). (a) Show that X is a complete sufficient statistic. (b) Show that the UMVUE of θ 2 is X2-1/n x"-'e-x/θ , x > 0.0 > 0 6. Let Xi, ,Xn be i.i.d. gamma(α,6) where α > l is known. ( f(x) Γ(α)θα (a) Show that Σ X, is complete and sufficient for θ (b) Find ElI/X] (c) Find the UMVUE of 1/0 -e λ , X > 0 2) (x...
σ2). 6. Suppose X1, Yİ, X2, Y2, , Xn, Y, are independent rv's with Xi and Y both N(μ, All parameters μί, 1-1, ,n, and σ2 are unknown. For example, Xi and Yi muay be repeated measurements on a lab...
σ2). 6. Suppose X1, Yİ, X2, Y2, , Xn, Y, are independent rv's with Xi and Y both N(μ, All parameters μί, 1-1, ,n, and σ2 are unknown. For example, Xi and Yi muay be repeated measurements on a laboratory specimen from the ith individual, with μί representing the amount of some antigen in the specimen; the measuring instrument is inaccurate, with normally distributed errors with constant variability. Let Z, X/V2. (a) Consider the estimate σ2- (b) Show that the...
2. Suppose X ~ N (μ,5). Find the asymptotic distribution of X(1-X) using A-methods. 3. Let...
2. Suppose X ~ N (μ,5). Find the asymptotic distribution of X(1-X) using A-methods. 3. Let X denote that the sample mean of a random sample of Xi,Xn from a distribution that has pdf Let Y,-VFi(x-1). Note that X = lari Xi- (a) Show that Mx(t) = (ca-tryM f(x) = e-z, x > 0. Find lim+oo My, (t)
C2.3 Let X and Y be random variables with finite variance, so that EX2o0 (i) Show...
C2.3 Let X and Y be random variables with finite variance, so that EX2o0 (i) Show that E(X) - (EX) E(X - EX)2, and hence that the variance of (ii) By considering (|XI Y)2, or otherwise, show that XY has finite expecta- (iii) Let q(t) = E(X + tY)2. Show that q(t)2 0, and by considering the roots of and EY2 < oo. X is always non-negative. tion the equation q(t) 0, deduce that
7. (a) State Chebyshev's inequality and prove it using Markov's inequality. 151 (b) Let (2, P) be a probability space representing a random experiment that can be repeated many times under the same conditions, and let A S2 be a random event. Suppose the experiment is repeated n times. (i) Write down an expression for the relative frequency of event A 131 ) Show that the relative frequence of A converges in probability to P(A) as the number of repetitions...
1) Let X and Y be random variables. Show that Cov( X + Y, X-Y) Var(X)--Var(Y) without appealing to the general formulas for the covariance of the linear combinations of sets of random variables; use the basic identity Cov(Z1,22)-E[Z1Z2]- E[Z1 E[Z2, valid for any two random variables, and the properties of the expected value 2) Let X be the normal random variable with zero mean and standard deviation Let ?(t) be the distribution function of the standard normal random variable....
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
Please answer all the parts neatly with all details.
3. Assume X1, X2,... are a sequence of i.i.d. random variables having finite first moment, that is, v = E(Xi oo. Let Yn = (|X1| .+ |Xn|)/n. (a) Show that Yn ->v in probability. (b) Show that E(Y,) -- v. (c) Show that E(|X, - /u|) -0 where u = E(X)
3. Assume X1, X2,... are a sequence of i.i.d. random variables having finite first moment, that is, v = E(Xi...
Dr. Beldi Qiang STATWOB Flotllework #1 1. Let X.,No X~ be a i.İ.d sample form Exp(1), and Y-Σ-x. (a) Use CLT to get a large sample distribution of Y (b) For n 100, give an approximation for P(Y> 100) (c) Let X be the sample mean, then approximate P(.IX <1.2) for n 100. x, from CDF F(r)-1-1/z for 1 e li,00) and ,ero 2Consider a random sample Xi.x, 、 otherwise. (a) Find the limiting distribution of Xim the smallest order...
May 21, 2019 R 3+3+5-11 points) (a) Let X1,X2, . . Xn be a random sample from G distribution. Show that T(Xi, . . . , x,)-IT-i xi is a sufficient statistic for a (Justify your work). (b) Is Uniform(0,0) a complete family? Explain why or why not (Justify your work) (c) Let X1, X2, . .., Xn denote a random sample of size n >1 from Exponential(A). Prove that (n - 1)/1X, is the MVUE of A. (Show steps.)....
1.(c)
2.(a),(b)
5. Let Xi,..., X, be iid N(e, 1). (a) Show that X is a complete sufficient statistic. (b) Show that the UMVUE of θ 2 is X2-1/n x"-'e-x/θ , x > 0.0 > 0 6. Let Xi, ,Xn be i.i.d. gamma(α,6) where α > l is known. ( f(x) Γ(α)θα (a) Show that Σ X, is complete and sufficient for θ (b) Find ElI/X] (c) Find the UMVUE of 1/0 -e λ , X > 0 2) (x...
σ2). 6. Suppose X1, Yİ, X2, Y2, , Xn, Y, are independent rv's with Xi and Y both N(μ, All parameters μί, 1-1, ,n, and σ2 are unknown. For example, Xi and Yi muay be repeated measurements on a laboratory specimen from the ith individual, with μί representing the amount of some antigen in the specimen; the measuring instrument is inaccurate, with normally distributed errors with constant variability. Let Z, X/V2. (a) Consider the estimate σ2- (b) Show that the...
2. Suppose X ~ N (μ,5). Find the asymptotic distribution of X(1-X) using A-methods. 3. Let X denote that the sample mean of a random sample of Xi,Xn from a distribution that has pdf Let Y,-VFi(x-1). Note that X = lari Xi- (a) Show that Mx(t) = (ca-tryM f(x) = e-z, x > 0. Find lim+oo My, (t)
C2.3 Let X and Y be random variables with finite variance, so that EX2o0 (i) Show that E(X) - (EX) E(X - EX)2, and hence that the variance of (ii) By considering (|XI Y)2, or otherwise, show that XY has finite expecta- (iii) Let q(t) = E(X + tY)2. Show that q(t)2 0, and by considering the roots of and EY2 < oo. X is always non-negative. tion the equation q(t) 0, deduce that
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