Let X, X2,X, be a random sample from the pdf given 2x f(x; 0) =,0 sxs0, zero elsewhere. Show that the variance of is less than the Cramér-Rao lower bound for f(x; 0 ). Is A UMVUE? Interpret the results.
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- Let X and Y be two continuous random variables having joint pdffX,Y (x, y) = (1 + XY)/4, −1 ≤x ≤1, −1 ≤y ≤1.Show that X ^2 and Y ^2 are independent.Let X1, X2, ... , Xn be a random sample from N(μ, σ2). Find the Moment Generating Function of X̅. If n = 16 and σ = 2, compute P(-1 ≤ X̅ - μ ≤ 1).Suppose that the lifetime, X, and brightness, Y, of a light bulb are modeled as continuous random variables. Let their joint pdf be given by:f(x,y)=λ_1λ_2e^{-λ_1x-λ_2y},x,y>0 •Are lifetime and brightness independent?•Are lifetime and brightness uncorrelated?
- Let Mx, y be the moment generating function of random variables that are not independent of X and Y. Which of the following / which are not the properties of the function Mx, y?Suppose that X is uniformly distributed on [0, 4]. Define Y = 2X + 5. Compute the pdf and cdf of Y.Let X1, .... Xn be a random sample from a population with location pdf f(x-Q). Show that the order statistics, T(X1, ...., Xn) = (X(1), ... X(n)) are a sufficient statistics for Q and no further reduction is possible?
- Let X1, X2, ... Xn random variables be independent random variables with a Poisson distribution whose parameters are l1, l2, ... ln, respectively. Which of the following is the moment generating function of the random variable Z defined as (the little image)?Let X be a random variable with pdff(x) = 4x^3 if 0 < x < 1 and zero otherwise. Use thecumulative (CDF) technique to determine the pdf of each of the following random variables: 1) Y=X^4, 2) W=e^(-x) 3) Z=1-e^(-x) 4) U=X(1-X)Let pX(x) be the pmf of a random variable X. Find the cdf F(x) of X and sketch its graph along with that of pX(x) if pX(x)=1/3,x=−1,0,1, zero elsewhere
- Let X1, ..., Xn be a random sample from N(μ, σ2), where σ2is known.a) Show that Y = (X1 + X2)/2 is an unbiased estimator of μ.b) Find the Cramer-Rao lower bound for the variance of an unbiasedestimator of μ for a general n.c) What is the efficiency of Y in part (a) above?Find the moment-generating function of the continuous random variable X whose probability density is given by f(x) = 1 for 0 < x < 1 0 elsewhere and use it to find μ’1,μ’2, and σ^2.Let X1, . . . , Xn be random variables corresponding to n independent bids for an item on sale. Suppose each Xi is uniformly distributed on [100, 200]. If the seller sells to the highest bidder, what is the expected sale price? A)Find the pdf of W = Max (X1, X2, …, Xn). B) Find E(W). Hint: Let W = Max (X1, X2, …, Xn). 1. P[W ≤ c] = P[Max (X1, X2, …, Xn) ≤ c] = P[X1 ≤ c, X2 ≤ c,…, Xn ≤ c] 2. Obtain the pdf of W by differentiating its cdf of W.