
Let X1, ..., Xn)a probability vector. Assume that E[gı(X1)...gn(Xn)] = E[91(X1))...E[gn(Xn)] for every measurable functions 91...On...
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1. Let f g be measurable functions defined on a measurable domain E. Let A, = {x € Elg(x) = 0}. It is clear that the domain of() is A . Prove the following: a. A, is a measurable set. b. (1) is a measurable function on Ap. Hint: Show that for every a € R, {xea |^)(x) < a} is m easureable. Start by proving that {x e Aol (6) (x)...
2. Let X ..Xn be continuous random variables. Assume that Show that X1, ..., Xn are independent.
NB: Please do it for Let X1, X2, ;;;;, Xn are independent and
Not identiically instead of identically
Not identically Let X ,X2, ..., Xy are independent and Identically distributed standard uniform random variables. Find the following expectations: (a) E[max(X1, X2, ...,XN)] (b) E[min(X1, X2, ...,Xy)]
Let X1, X2,...be a
sequence of random variables. Suppose that Xn?a in probability for
some a ? R. Show that (Xn) is Cauchy convergent in probability,
that is, show that for all
> 0 we have P(|Xn?Xm|> )?0 as n,m??.Is the converse true?
(Prove if “yes”, find a counterexample if “no”)
(5) Let (. A, /u) be a measure space. Let f,g : O > R* be a pair functions. Assume that f is measurable and that f = g almost everywhere. (a) Prove that q is measurable on A. Prove that g is integrable (b) Let A E A and assume that f is integrable on A and A
(5) Let (. A, /u) be a measure space. Let f,g : O > R* be a pair functions. Assume that...
Let X1,.-. , Xn ~ N(2, 1) be independent, where E R is unknown. (i) Show that X := -1X; is a minimum sufficient statistic. (ii) Show that X is a complete statistic.
4. Let X1, X2, . .. be independent random variables satisfying E(X) E(Xn) --fi. (a) Show that Y, = Xn - E(Xn) are independent and E(Yn) = 0, E(Y2) (b) Show that for Y, = (Y1 + . . + Y,)/n, <B for some finite B > 0 and VB,E(Y) < 16B. 16B 6B 1 E(Y) E(Y) n4 i1 n4 n3 (c) Show that P(Y, > e) < 0 and conclude Y, ->0 almost surely (d) Show that (i1 +...
1. Let X.Xn be discrete random variables. Assume that Пр Show that X1, Xn are independent.
Let f and g be measurable unsigned functions on R. Assume that integral of f dx ≤ integral of g dx. Is it true that f(x) ≤ g(x) for almost every x? If so, prove it. If not, give a counterexample.
Let Ņ, X1. X2, . . . random variables over a probability space It is assumed that N takes nonnegative inteqer values. Let Zmax [X1, -. .XN! and W-min\X1,... ,XN Find the distribution function of Z and W, if it suppose N, X1, X2, are independent random variables and X,, have the same distribution function, F, and a) N-1 is a geometric random variable with parameter p (P(N-k), (k 1,2,.)) b) V - 1 is a Poisson random variable with...