4. Suppose that Z є R2p has a MVN distribution M2p(Hs, z). Partition Z as Z- where X є Rp and Y є...
4. Suppose that Z є R2p has a MVN distribution Nop( 12. E.). Partition Z as Z where X є Rp and Y є Rp. Denote the means of X and Y as μι and μy, respectively. Let μΔ-Ha-ty. Suppose that we obtain IID data Z1, ,Zn from the underlying distribution of Z. Let a (0,1) be a constant (a) Describe how to construct a (1-a)-level convex confidence region (CR) for μΔ when y, is known. Explain. (b) Describe how...
Question 3: A random variable X has a Bernoulli distribution with parameter θ є (0,1) if X {0,1} and P(X-1)-θ. Suppose that we have nd random variables y, x, following a Bernoulli(0) distribution and observed values y1,... . Jn a) Show that EIX) θ and Var[X] θ(1-0). b) Let θ = ỹ = (yit . .-+ yn)/n. Show that θ is unbiased for θ and compute its variance. c) Let θ-(yit . . . +yn + 1)/(n + 2) (this...
iid Let X1,, X, ^ X~P for some unknown distribution P with continuous cdf F. Below we describe a ? test for the null and alternative hypotheses We divide the sample space into 5 disjoint subsets refered to as bins A1(-00,-2), A2 -(-2,-0.5), As -(-0.5,0.5), A4 (0.5,2) As -(2, oo). as functions of X, by Now, define discrete random variables For example, if Xi --0.1, then Xi є Аз and so Y;-3. In other words, Y, is the label of...
8.4.12 Suppose that X, .., Y, are iid random variables having the ernoulli(p) distribution where p e (0, 1) is the unknown parameter. With (0, l ), derive the randomized UMP level α test for l, P-Po p reassigned oE versus H p Po where p, is a number between 0 and 1 8.4.12 Suppose that X, .., Y, are iid random variables having the ernoulli(p) distribution where p e (0, 1) is the unknown parameter. With (0, l ),...
2 Expectation, Co-variance and Independence [25pts + 5pts] Suppose X, Y and Z are three different random variables. Let X obeys Bernouli Distribution. The probability disbribution function is s 0.5 x=c 0.5 x= -c. c is a constant here. Let Y obeys the standard Normal (Gaussian) distribution, which can be written as Y ~ N(0,1). X and Y are independent. Meanwhile, let Z = XY p(x) = { 0.5 • Are Y and Z independent? (Just clarify) [3pts] • Show...
1 Expectation, Co-variance and Independence [25pts] Suppose X, Y and Z are three different random variables. Let X obeys Bernouli Distribution. The probability disbribution function is 0.5 x=1 0.5 x=-1 Let Y obeys the standard Normal (Gaussian) distribution, which can be written as Y are independent. Meanwhile, let Z = XY. N(0,1). X and Y (a) What is the Expectation (mean value) of X? 3pts (b) Are Y and Z independent? (Just clarify, do not need to prove) [2pts c)...
Suppose X, Y and Z are three different random variables. Let X obey Bernoulli Distribution. The probability distribution function is p(x) = Let Y obeys the standard Normal (Gaussian) distribution, which can be written as Y ∼ N(0, 1). X and Y are independent. Meanwhile, let Z = XY . (a) What is the Expectation (mean value) of X? (b) Are Y and Z independent? (Just clarify, do not need to prove) (c) Show that Z is also a standard...
A random varible X taking values from [0,1] has Beta distribution of parameters a and B, which we denote by Beta(a,b), if it has PDF _f(a+B) fa-1(1 – X)B-1, fx(x) = T(a)l(B) where I(z) is the Euler Gamma function defined by I(z) = Sx2-1e-*dx. Bob has a coin with unknown probability of heads. Alice has the following Beta prior: A = Beta(2,3). Suppose that Bob gives Alice the data on = {x1,...,xn), which is the outcome of n indepen- dent...
Please explain very carefully! 4. Suppose that x = (x1, r.) is a sample from a N(μ, σ2) distribution where μ E R, σ2 > 0 are unknown. (a) (5 marks) Let μ+σ~p denote the p-th quantile of the N(μ, σ*) distribution. What does this mean? (b) (10 marks) Determine a UMVU estimate of,1+ ơZp and justify your answer. 4. Suppose that x = (x1, r.) is a sample from a N(μ, σ2) distribution where μ E R, σ2 >...
Hello, please help solve problem and show all work thank you. (Linear models) Suppose we have a vector of n observations Y (response), which has distribution Nn(XB.ση where x is an n × p matrix of known values (indepedent variables), which has full column rank p, and β is a p x 1 vector of unknown parameters. The least squares estimator of ß is 4. a. Determine the distribution of β. xB. Determine the distribution of Y b. Let Y...