Concept explainers
Let
(a) Compute
(b) Determine
(c) If Y equals the maximum of
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- Let X₁, X₂ be a random sample from N (1,1) and Y₂₁, Y₂ be a random sample from N (0,1), where the samples are independent. Determine the distribution of the following Y1+Y2 2 (X1-X₂)²+(Y₁-Y₂)² 4arrow_forwardLet X1, ... , Xn be a random sample (i.i.d.) from Geometric(p) distribution with PMF P(X = x) = (1 – p)*p, x = 0, 1, 2, . .. The mean of this distribution is (1 – p)/p. (a) :) Find the MLE of p. (b) ( s) Find the estimator for p using method of moments. (c) Now let's think like a Bayesian. Consider a Beta prior on p, i.e., p~ Beta(a, B). Find the posterior distribution of p. Hint: For Geometric likelihood, the conjugate prior on p is a Beta distribution. (d) ) What is the Bayes estimator of p under squared error loss? Denote it by PB. (e) What happens to på if both a and ß goes to 0?arrow_forwardDraw all possible samples each of size 2 from the population 2, 4, 6 and 8 using sampling with replacement. Find mean of each sample and verify that (6) Mean of = u and (ii) V(X) (ii) V(R) %3Darrow_forward
- 28. Let X,..., X, be a random sample from EXP(6), and define ô, = X and Ô, = nX/(n + 1). (a) Find the variances of 6, and 8. (b) Find the MSES of é, and ô,. (c) Compare the variances of 0, and 8, for n= 2. (d) Compare the MSES of ô, and ô, for n = 2. (e) Find the Bayes risk of 6, using 8 - EXP(2).arrow_forwardLet x = (x1, x2 ..., xi, ..., xn) be a data set with a sample mean x̄ and a sample variance Sx2. Also, let y = (y1, y2 ..., yi, ..., yn) be a data set with a sample mean ȳ and sample variance Sy2. Finally, let z = x - y such that z = (x1 - y1, x2 - y2 ..., xi - yi, ... xn - yn). Show that z̄ = x̄ - ȳ.arrow_forwardX,X, and X, is a random sample of size 3 from a population 3. with mean value u and variance o2, T, T, T, are the 3. estimators used to estimate mean value u, where T=X,+X,-X,T,= 2X,+3X, - 4X,& T, ax,+x,+x) 1 (ax,+X,+X,) (i) Are T, and T, unbiased estimators ? 1 2 (ii) Find the value of such that T, is unbiased estimator 3 for u. With this value of A is T, a consistent estimator ? (iii) 3 (iv) Which is the best estimator ?arrow_forward
- 5) Consider the random variable X and Y that represent the number of vehicles that arrive at two separate street corners during a certain 2-minute period. These street corners are fairly close together so it is important that traffic engineers deal with them jointly if necessary. The joint distribution of X and Y is known to be f(x, y) = 9 16 1 4(x+y)' for x = 0, 1, 2,... and y = 0, 1, 2, .... (a) Are the two random variables X and Y indepen- dent? Explain why or why not. (b) What is the probability that during the time pe- riod in question less than 4 vehicles arrive at the two street corners?arrow_forwardSuppose Y1,Y2,··· ,Yn are i.i.d. continuous uniform(0,1) distributed.(a) Prove that the kth-order statistic, Y(k), has Beta distribution with α = k and β = n−k+ 1.(b) What is the distribution of the median of Y1,Y2,··· ,Yn when n is an odd integer?(c) Find the joint density of the middle two of Y(1),Y(2),··· ,Y(n) when n is an even integerarrow_forward2. A random variable X follows Beta(2,3) distribution. (a) Show that the cumulative distribution function F of a Beta(2,3) random variable is F(x) = 6x? 823 + 3x4, r € [0, 1]. (b) Find the sampling distribution (pdf) of the median for a random sample of size n = 2m +1 from Beta(2,3).arrow_forward
- Let Y₂ represent the ith normal population with unknown mean 4, and unknown variance of for i=1,2. Consider independent random samples, Y₁₁, Yi2,,Yin, of size ni, from the ith population with sample mean Y, and sample variance S?=²-1₁-1(Y - Y₁². (a) What is the distribution of Y;? State all the relevant parameters of the distribution. (b) Find a level a test (that is, the rejection region) for testing Ho: ₁ = o versus Ha Hiio when of is unknown and n; is small. (c) In the context of the test in part (b), state the Type I error and give a probability statement for the level of significance, a.arrow_forward2. Suppose the distribution of Y given X = x is a normal random variable with mean x and variance x². And X follows an uniform distribution on (0,1) (a) Find E(XY) (b) Let U = X, V = Y/X. Show that U and V are independentarrow_forwardLet X1, X2,...Xn be a random sample from a Poisson distribution with variance 0. Find an unbiased estimator for Pe(X=0).arrow_forward
- A First Course in Probability (10th Edition)ProbabilityISBN:9780134753119Author:Sheldon RossPublisher:PEARSON