1. Let X1, X2 be i.i.d with this distribution: f(x) = 3e cx, x ≥ 0...
1. Let X1, X2 be i.i.d with this distribution: f(x) = 3e cx, x ≥ 0 a. Find the value of c b. Recognize this as a famous distribution that we’ve learned in class. Using your knowledge of this distribution, find the t such that P(X1 > t) = 0.98. c. Let M = max(X1, X2). Find P(M < 10)
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1. Let X1, X2 be i.i.d with this distribution: f(x) = 3e cx, x ≥
0...
7. Let X1, X2,.. be i.i.d. random variables, and let T(t)minn: X > t, t20. (a)...
7. Let X1, X2,.. be i.i.d. random variables, and let T(t)minn: X > t, t20. (a) Determine the distribution of T(t) (b) Show that, if p= P(X1> t)0 astoo, then pT(t)Exp(1) as to
7. Let X1, X2,.. be i.i.d. random variables, and let T(t)minn: X > t, t20. (a) Determine the distribution of T(t) (b) Show that, if p= P(X1> t)0 astoo, then pT(t)Exp(1) as to
Problem 2. (The Convergence of Extreme Value) Let X1, X2, ... be i.i.d sample from the...
Problem 2. (The Convergence of Extreme Value) Let X1, X2, ... be i.i.d sample from the distribution with density function as: f(x) = >1 10 otherwise Define Mn = min(X1, X2, ... , Xn), answer the following questions. 1) Show that Mn P 1 as n +0. 2) Show that n(Mn – 1) converges in distribution as n + 00. Find out the limit distri- bution.
Let X1,...X be i.i.d with density f()(1/0)exp(-/0) for r >0 and 0> 0. a. Find the...
Let X1,...X be i.i.d with density f()(1/0)exp(-/0) for r >0 and 0> 0. a. Find the pitman estimator of 0 b. Show that the pitman estimator has smaller risk than the UMVUE of when the loss function is (t-0)2 02 Suppose C. f(x)= 0exp(-0x) and that 0 has a gamma prior with parameters a and p, find the Bayes estimator of 0 d. Find the minimum Bayes risk e. Find the minimax estimator of 0 if one exists. 1
Let...
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi , . . . , X,.), V=min(X1, ,X,). (a) Find the distribution functio...
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi , . . . , X,.), V=min(X1, ,X,). (a) Find the distribution function and the density function of U and of V (b) Show that the joint density function of U and V is fe,y(u, u)= n(n-1)/(u)/(v)[F(v)-F(u)]n-1, ifu < u.
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi...
Let X1, X2, ..., Xn be an i.i.d. sample from a Uniform [0,theta] distribution Find the...
Let X1, X2, ..., Xn be an i.i.d. sample from a Uniform [0,theta] distribution Find the MLE of theta. Find the density function of the MLE of theta you found above. Find the bias, variance, and mean squared error of the MLE.
Let X1,..., X, be an i.i.d. sample from a Rayleigh distribution with parameter e > 0:...
Let X1,..., X, be an i.i.d. sample from a Rayleigh distribution with parameter e > 0: f(x\C) = e ==/(20?), x20 (This is an alternative parametrization of that of Example A in Section 3.6.2.) a. Find the method of moments estimate of e. b. Find the mle of C. Find the asymptotic variance of the mle.
5. Let X1, X2,... , X100 be i.i.d. random variables, following the normal distribution N(0, 102)....
5. Let X1, X2,... , X100 be i.i.d. random variables, following the normal distribution N(0, 102). Let α denote the probability that there are at least 3 variables among them whose absolute value is larger than 19.6. Compute α, and give an approxi- mate value of α with an error less than 0.01 according to the Poisson distribution. 15pts]
5. Let X1, X2,... , X100 be i.i.d. random variables, following the normal distribution N(0, 102). Let α denote the probability...
Let X1, X2, · · · Xn be a i.i.d. sample from Bernoulli(p) and let ....
Let X1, X2, · · · Xn be a i.i.d. sample from Bernoulli(p) and let . Show that Yn converges to a degenerate distribution at 0 as n → ∞.
Let X1, X2,..., Xn be a r.s. from f(x) = 0x0-1, for 0 < x <1,0...
Let X1, X2,..., Xn be a r.s. from f(x) = 0x0-1, for 0 < x <1,0 < a < 0o. (a) Find the MLE of 0. (b) Let T = -log X. Find the pdf of T. (c) Find the pdf of Y = DIT: (i.e., distribution of Y = - , log Xi). (d) Find E(). (e) Find E( ). (f) Show that the variance of 0 MLE → as n → 00. (g) Find the MME of 0.
Let X1....Xn be i.i.d sample with a continous distribution function F(.) and X(1)<......<X(n) are the orser-statistics...
Let X1....Xn be i.i.d sample with a continous distribution function F(.) and X(1)<......<X(n) are the orser-statistics of the sample. Let the constant Mp be defined by F(Mp)=p. Show that for 1≤k1≤k2≤ n, P{X(k1) ≤Mp ≤X(k2)}=P{k1 ≤Bionmial(n,p) k2}
7. Let X1, X2,.. be i.i.d. random variables, and let T(t)minn: X > t, t20. (a) Determine the distribution of T(t) (b) Show that, if p= P(X1> t)0 astoo, then pT(t)Exp(1) as to
7. Let X1, X2,.. be i.i.d. random variables, and let T(t)minn: X > t, t20. (a) Determine the distribution of T(t) (b) Show that, if p= P(X1> t)0 astoo, then pT(t)Exp(1) as to
Problem 2. (The Convergence of Extreme Value) Let X1, X2, ... be i.i.d sample from the distribution with density function as: f(x) = >1 10 otherwise Define Mn = min(X1, X2, ... , Xn), answer the following questions. 1) Show that Mn P 1 as n +0. 2) Show that n(Mn – 1) converges in distribution as n + 00. Find out the limit distri- bution.
Let X1,...X be i.i.d with density f()(1/0)exp(-/0) for r >0 and 0> 0. a. Find the pitman estimator of 0 b. Show that the pitman estimator has smaller risk than the UMVUE of when the loss function is (t-0)2 02 Suppose C. f(x)= 0exp(-0x) and that 0 has a gamma prior with parameters a and p, find the Bayes estimator of 0 d. Find the minimum Bayes risk e. Find the minimax estimator of 0 if one exists. 1
Let...
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi , . . . , X,.), V=min(X1, ,X,). (a) Find the distribution function and the density function of U and of V (b) Show that the joint density function of U and V is fe,y(u, u)= n(n-1)/(u)/(v)[F(v)-F(u)]n-1, ifu < u.
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi...
Let X1,..., X, be an i.i.d. sample from a Rayleigh distribution with parameter e > 0: f(x\C) = e ==/(20?), x20 (This is an alternative parametrization of that of Example A in Section 3.6.2.) a. Find the method of moments estimate of e. b. Find the mle of C. Find the asymptotic variance of the mle.
5. Let X1, X2,... , X100 be i.i.d. random variables, following the normal distribution N(0, 102). Let α denote the probability that there are at least 3 variables among them whose absolute value is larger than 19.6. Compute α, and give an approxi- mate value of α with an error less than 0.01 according to the Poisson distribution. 15pts]
5. Let X1, X2,... , X100 be i.i.d. random variables, following the normal distribution N(0, 102). Let α denote the probability...
Let X1, X2,..., Xn be a r.s. from f(x) = 0x0-1, for 0 < x <1,0 < a < 0o. (a) Find the MLE of 0. (b) Let T = -log X. Find the pdf of T. (c) Find the pdf of Y = DIT: (i.e., distribution of Y = - , log Xi). (d) Find E(). (e) Find E( ). (f) Show that the variance of 0 MLE → as n → 00. (g) Find the MME of 0.
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