(i)
X1 , X2 , .... , Xn are independent random variables Cov(Xi , Xj) = 0 i,j=1,2,...,n ; ij
(ii)
X1 , X2 , .... , Xn are not independent random variables.
Let ij be the correlation between Xi and Xj i,j=1,2,...,n ; ij
Cov(Xi , Xj) = ijij i,j=1,2,...,n ; ij
explan the answer 1l. Suppose that X1, X2,... Xn are independent random variables. Assume that ElXi]...
explan the answer . Suppose that Xi, X2,.... Xn are independent random variables. Assume that E[A]-: μί ald Var(Xi)-σ? where i-| , 2, , n. If ai, aam., an are constants. (i) Write down expression for (i) E{E:-aiX.) and (ii) Var(Σ-lai%). (i) Rewrite the expression if X,'s are not independent.
3. Suppose that X1, X2, , Xn are independent random variables with the same expectation μ and the same variance σ2. Let X--ΣΑι Xi. Find the expectation and variance of
. If X1, X2,..., Xn are independent random variables with common mean μ and variances σ1, σ2, . . ., σα , prove that Σί (Xi-T)2/[n(n-1)] is an ว. 102n unbiased estimate of var[X] 3. Suppose that in Exercise 2 the variances are known. LeTw Σί uiXi
L.11) Sums of independent random variables a) If X1 , X2 X, , , Xn are independent random variables all with Exponential μ distribution, then what is the distribution of XII + 2 +X3 + .tX b) If X is a random variable with Exponential[u] distribution, then what is the distribution of x +X1? c) If X1 , X2 , Х, , , X are independent random variables all with Normal 0. I distribution, then what is the distribution of...
How do you show this? 1.2.12. Accept the following definition. Discrete random variables X1, X2,.. , Xn, taking values in Ai, A2,..., An, are said to be independent if (1) P(Xi = ai , . . . ,x, = an) =11P(X, = a.) 仁1 for all ai E A1,., an E An. Then prove that random variables in any subsequence of a finite sequence of independent random variables are independent.
1. Let X1, ·s, Xn be independent random variables taking values 0 or 1 withP(Xi=1)=eθ-ai /(1+eθ-ai ), i=1, ……, nfor some given constants ai. Find a one-dimensional sufficient statistic for θ.
Suppose X1, X2,... are independent Geometric (number of trials) random variables where Xi ~ Geometric(p = 1/i^2) a) It is easily shown that Xn converges to a for some constant a. Name it. b) According to the Borel-Cantelli Lemmas, does Xn almost surely converge to a? Suppose Xi, X2, are independent Geometric (number of trials) random variables where x,~ Geometric(pal+) |. a) It is easily shown that Xa for some constant a. Name it. b) According to the Borel-Cantelli Lemmas,...
Let X1, X2, ....,. Xn, be a set of independent random variables, each distributed as a normal random variable with parameters μί and σ. Let х, ai Use properties of moment generating functions to determine the distribution of Y, meaning: find the type of distribution we get, and its expected value and variance
Let X1, X2, , xn are independent random variables where E(X)-? and Var(X) ?2 for all i = 1, 2, , n. Let X-24-xitx2+--+Xy variables. is the average of those random Find E(X) and Var(X).
Suppose the random variables X1, X2, ..., Xn are independent each with the distribution 020 *; 0) (0+1); X2 2. Find the Maximum Likelihood estimate for 0. On Žin(x) + • 8Žin(x) + n In(2) i= 1 { ince) -- OD. Žince) - n ince) -n In(2) i= 1 O e. None of the above.