For , let have an n-dimensional normal distribution . For any , let denote the vector consisting of the last n-m coordinates of .
a. Find the mean vector and variance covariance matrix of
b. Show that is a (n-m) dimensional normal random vector.
For , let have an n-dimensional normal distribution . For any , let denote the vector...
3. For n 2 2, let X have n-dimensional normal distribution MN(i, V). For any 1 3 m < n, let X1 denote the vector consisting of the last n - m coordinates of X < n, let 1 (a). Find the mean vector and the variance-covariance matrix of X1. (b). Show that Xi is a (n- m)-dimensional normal random vector.
#4. Let , , and be a random sample from f. Find the UMVUE for We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
are order statistics from same distribution . Sample size is 3. Define and Finding marginal density of . We were unable to transcribe this imageplz) = 1 We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
Let be independent, identically distributed random variables with . Let and for , . (a) Show that is a martingale. (b) Explain why satisfies the conditions of the martingale convergence theorem (c) Let . Explain why (Hint: there are at least two ways to show this. One is to consider and use the law of large numbers. Another is to note that with probability one does not converge) (d) Use the optional sampling theorem to determine the probability that ever attains...
are order statistics from same distribution . Sample size is 3. Define and Finding joint density of and . We were unable to transcribe this imageplz) = 1 We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
Let be a sample (size n = 1) from the exponential distribution, which has the pdf where is an unknown parameter. Let's define a statistic as . Is a sufficient statistic for ? We were unable to transcribe this imagef(x: λ) = Xe We were unable to transcribe this imageT(X) = 11>2 T(X) We were unable to transcribe this image
For , let be the order statistics of independent draws from . (1) Find the PDF of . (2) Compute . We were unable to transcribe this imageWe were unable to transcribe this image(2n+1 Unif -1,1 We were unable to transcribe this imageWe were unable to transcribe this image
Let X and Y be independent random variables with . Assume that and . Demonstrate that Cov(X,Y) = 0 We were unable to transcribe this imageWe were unable to transcribe this image400 OC
Doob’s Decomposition: Let be a submartingale relative to the filtration . Show that there is a martingale and a predictable sequence such that for all and . (Xm)m>o We were unable to transcribe this image(Mm)m>o (Am)m20 We were unable to transcribe this imagem>0 We were unable to transcribe this image
0Let X1, ....., Xn be iid Random variable from a Uniform distribution with pdf given by . (1) Is the 2-dimensional statistics T1(X) = (X(1), X(n)) a complete sufficient statistics? Justify your answer (2) Is the one-dimensional statistic a complete sufficient statistic? Justify your answer We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image