Let X1,X2,...,Xn denote independent and identically distributed random variables with variance 2. Which of the following is sucient to conclude that the estimator T = f(X1,...,Xn) of a parameter ✓ is consistent (fully justify your answer):
(a) Var(T)=
(b) E(T)= and Var(T)= .
(c) E(T)=.
(d) E(T)= and Var(T)=
Let X1,X2,...,Xn denote independent and identically distributed random variables with variance 2. Which of the following...
2. Let X1, X2,. . , Xn denote independent and identically distributed random variables with variance σ2, which of the following is sufficient to conclude that the estimator T f(Xi, , Xn) of a parameter 6 is consistent (fully justify your answer): (a) Var(T) (b) E(T) (n-1) and Var(T) (c) E(T) 6. (d) E(T) θ and Var(T)-g2. 72 121
Let X1,X2,...,Xn denote independent and identically distributed random variables with mean µ and variance 2. State whether each of the following statements are true or false, fully justifying your answer. (a) T =(n/n-1)X is a consistent estimator of µ. (b) T = is a consistent estimator of µ (assuming n7). (c) T = is an unbiased estimator of µ. (d) T = X1X2 is an unbiased estimator of µ^2. We were unable to transcribe this imageWe were unable to transcribe...
Let Xi, X2,... , Xn denote independent and identically distributed uniform random variables on the interval 10, 3β) . Obtain the maxium likelihood estimator for B, B. Use this estimator to provide an estimate of Var[X] when r1-1.3, x2- 3.9, r3-2.2
15. Let X,, X2,.. . be independent, identically distributed random variables, EIXI oo, and denote S,-X1+... + Xn. Prove that [Use symmetry in the final step.] 15. Let X,, X2,.. . be independent, identically distributed random variables, EIXI oo, and denote S,-X1+... + Xn. Prove that [Use symmetry in the final step.]
Let X1,, Xn be independent and identically distributed random variables with unknown mean μ and unknown variance σ2. It is given that the sample variance is an unbiased estimator of ơ2 Suggest why the estimator Xf -S2 might be proposed for estimating 2, justify your answer
3. Suppose X1,X2, are independent identically distributed random variables with mean μ and variance σ2. Let So = 0 and for n > 0 let Sn denote the partial sumi Let Fn denote the information contained in X1, ,Xn. (1) Verify that Sn nu is a martingale. (2) Assume that μ 0, verify that Sn-nơ2 is a martingale. 3. Suppose X1,X2, are independent identically distributed random variables with mean μ and variance σ2. Let So = 0 and for n...
(5) Let X1,X2,,Xn be independent identically distributed (i.i.d.) random variables from 1.1 U(0,1). Denote V max{Xi,..., Xn) and W min{Xi,..., Xn] (a) Find the distributions and the densities and the distributions of each of V and W. (b) Find E(V) and E(W) (5) Let X1,X2,,Xn be independent identically distributed (i.i.d.) random variables from 1.1 U(0,1). Denote V max{Xi,..., Xn) and W min{Xi,..., Xn] (a) Find the distributions and the densities and the distributions of each of V and W. (b)...
Question 6 Let X1, . . . , Xn denote a sequence of independent and identically distributed i.id. N(14x, σ2) random variables, and let Yı, . . . , Yrn denote an independent sequence of iid. Nụy, σ2) ran- dom variables. il Λί and Y is an unbiased estimator of μ for any value of λ in the unit interval, i.e. 0 < λ < 1. 2. Verify that the variance of this estimator is minimised when and determine the...
Suppose we have 5 independent and identically distributed random variables X1, X2, X3, X4,X5 each with the moment generating function 212 Let the random variable Y be defined as Y = Σ We were unable to transcribe this image
Let Xi,X2, , Xn be independent and identically distributed (ii.d.) Exponential(1) random variables. 14] [41 (a) Find the method of moments estimator for X (b) Find the method of moments estimator for (c) Find the bias, variance and MSE (mean square erop) for the essimator in part () Total: [16] Let Xi,X2, , Xn be independent and identically distributed (ii.d.) Exponential(1) random variables. 14] [41 (a) Find the method of moments estimator for X (b) Find the method of moments...