1. Let X Xn be independent and identically distributed as N(0,9) (here 9 is the variance)...
1. Let X,.., Xn be independent and identically distributed as N (0,9) (here 9 is the variane (a) What is the distribution of Y-1X,? (Verify it using MGF) (b) What is the distribution of Xn X? (Again verify it using MGF) (c) Assume n -25. What is the probability that an observed value of X lies inside the interval [-1.2,1.2] (d) Give a lower bound on the probability that Xn lies inside the interval1.2,1.2] using Chebyshev's inequality. Compare it with...
3. Let X , X, be an independent and identically distributed random sample from a distribution with pdf f(X; B) = B Xe 4X X20 a. Find the Maximum Likelihood Estimator of B, and denote it ß. b. Find the lower bound for Var() using Cramer-Rao inequality for ſunbiased.
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...
Let X and Y be two independent and identically distributed random variables with expected value 1 and variance 2.56. First, find a non-trivial upper bound for P(|X + Y − 2| ≥ 1). Now suppose that X and Y are independent and identically distributed N(1,2.56) random variables. What is P(|X + Y − 2| ≥ 1) exactly? Why is the upper bound first obtained so different from the exact probability obtained?
1. Let X1, X2,... , Xn be independent and identically distributed according to the unifornm distribution on (0,1). Let Xn and fn denote the 6th smallest and its pdf, respectively Determine fn(x) limn
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...
(1 point) In Unit 3, I claimed that the sum of independent, identically distributed exponential random variables is a gamma random variable. Now that we know about moment generating functions, we can prove it. Let X be exponential with mean A 4. The density is 4 a) Find the moment generating function of X, and evaluate at t 3.9 The mgf of a gamma is more tedious to find, so l'll give it to you here. Let W Gamma(n, A...
Consider n independent and identically distributed random variables X1,X2, following a uniform distribution on the interval [0,1] ,Xn, each a) What is the pdf of Mmin(X1,X2, .. ,Xn)? b) Give the expectation and variance of XX 1-1лі.
Let X1,X2,...,Xn be an independent and identically distributed (i.i.d.) random sample of Beta distribution with parameters α = 2 and β = 1, i.e., with probability density function fX(x) = 2x for x ∈ (0,1). Find the probability density function of the first and last order statistics Y1 and Yn.
Let X1...Xn be independent, identically distributed random sample from a poisson distribution with mean theta. a. Find the meximum liklihood estimator of theta, thetahat b. find the large sample distribution for (sqrt(n))*(thetahat-theta) c. Construct a large sample confidence interval for P(X=k; theta)