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- 2)Let X1, X2, ..., Xn be a sample of n units from a population with a probability density function f (x I θ)=θxθ-1 , 0<x<1, θ>0 . According to this: Find the maximum likelihood estimator (MLE) of parameter θ.If X is a continuous random variable with X ∼ Uniform([0, 2]), what is E[X^3]?Find the moment-generating function of the contin-uous random variable X whose probability density is given by f(x) =1 for 0 < x < 10 elsewhere and use it to find μ1,μ2, and σ2.
- Show that if X is a random variable with continuous cumulative distribution function F(x), then F(x)=U is uniformly distributed over the interval (0,1).Let X and Y be two jointly Gaussian real random variables, each with zero mean,variance 1, and correlation coefficient ρ ∈ (0, 1). Let a, b ∈ R be such that a2 + b2 = 1, and defineW := aX + bY .a. Find values for a and b to maximize the variance of W . Hint: Use eigendecomposition.b. Does the optimal W (from part a) have a probability density function? If yes, derive it. Ifnot, explain why.If X1,X2,...,Xn constitute a random sample of size n from a gamma population with α =2, use the method of maximum likelihood to find a formula for estimating β.
- If Y is a continuous, uniformly distributed random variable over the interval(4,10), then the value of the PDF between 4 and 10 is?Let X1, X2, . . . , Xn be an i.i.d. random sample from a Beta distribution with density: f(x; θ) = Γ(2θ) Γ(θ) 2 x θ−1 (1 − x) θ−1 , 0 < x < 1, θ > 0. Find a sufficient statisticLet X1, X2, and X3 be independent random variables from (-1, 1). Find the probability density function and the expected value of the random variable [X(1) + X(2)]/2.
- If two random variables X1 and X2 have the joint density function given by f (x1, x2) = x1x2, 0 < x1 < 1, 0 < x2 < 2 0, otherwise Find the probability that (a) Both random variables will take on values less than 1 (b) The sum of the values taken on by the two random variables will be less than 1.1(a)Consider the random variable model with density function given byf(x)= { 2kx/45, 0≤x≤5 2k(30−x)/45 , 5≤x≤10 0, otherwise Compute the(i) expected value(ii) standard deviation.comment and discuss on the output of these two components with the type the random variable model defined (b). If f (x)=ce−0.2x , if 0≤x≤10 and 0, otherwiseBased on your computations below, explain and discuss the variousprobabilities with respect to the given exponential function.(i)deduce the constant c, (ii)compute the probabilities(alpha) p(x≥5) (beta) p(x<4) (gamma) p(3≤x≤11)Let X be a discrete random variable with probability mass function P(X= x) =p(1 −p)^x ; x= 0,1,2,.... Here p∈[0,1]. Calculate the moment generating function (MGF) of X, the mean, and variance of this distribution (using the MGF).
