X1,... ,Xm and Yi,... .Yn are independent with common mean ξ and variances σ2 and T2,...
Suppose that X1, X2,.... Xn and Y1, Y2,.... Yn are independent random samples from populations with the same mean μ and variances σ., and σ2, respectively. That is, x, ~N(μ, σ ) y, ~ N(μ, σ ) 2X + 3Y Show that is a consistent estimator of μ.
Let X1, ...., Xm be iid N(μ1,σ2) and Y1, ..., Yn be iid N(μ2,σ2), and X's and Y's are independent. Here -∞<μ1,μ2<∞ and 0<σ<∞ are unknown. Derive the MLE for (μ1,μ2,σ2). Is the MLE sufficient for (μ1,μ2,σ2)? Also derive the MLE for (μ1-μ2)/σ.
4. (24 marks) Suppose that the random variables Yi,..., Yn satisfy Y-B BX,+ Ei, 1-1, , n, where βο and βι are parameters, X1, ,X, are con- stants, and e1,... ,en are independent and identically distributed ran- dom variables with Ei ~ N (0,02), where σ2 is a third unknown pa- rameter. This is the familiar form for a simple linear regression model, where the parameters A, β, and σ2 explain the relationship between a dependent (or response) variable Y...
Problem 3. Consider two independent samples, X1, . . . , Xm from a N(µ1, σ12 ) distribution and Y1, . . . , Yn from a N(µ2, σ22 ) distribution. Here µ1, µ2, σ12 and σ2 are unknown. Consider testing the null hypothesis that the two population variance are equal, H0 : σ12 = σ22 , against the alternative that these variances are different, H1 : σ12 ≠ σ12 . (a) Derive the LR test statistic Λ
Suppose X1,X2, ,Xm are iid exponential with mean A. Suppose Yı,Yo, exponential with mean β2-Suppose the samples are independent. , Yn are iid (a) Derive the likelihood ratio test (LRT) statistic λ(x,y) for testing versus and show that it is a function of ti-ti (x)-Σ-iz; and t2-t2(y)-Σ1Uj. (b) Show how you could perform a size a test in part (a) using the F distribution Suppose X1,X2, ,Xm are iid exponential with mean A. Suppose Yı,Yo, exponential with mean β2-Suppose the...
. 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
01) Let X1, X2, ...., Xm be m independent growth observations on the first type with E[X:] = u and V(x) = 02. And Let Y, Y2, ...., Yn be n independent growth observations on the second type with E[Y] = u and V(Y) = 402 And let Z1, Z2, ....,Zk be k independent growth observations on the third type with E[Z] = 2u and V(Z) = 302 Show that the estimator û = 32 - 2x - 37 is...
3. You have two independent random samples: XiXX from a population with mean In and variance σ2 and Y, Y2, , , , , Y,n from a population with mean μ2 and variance σ2. Note that the two populations share a common variance. The two sample variances are Si for the first sample and Si for the second. We know that each of these is an unbiased estimator of the common population variance σ2, we also know that both of...
e (4 marks) Let m be an integer with the property that m 2 2. Consider that X1, X2,.. ., Xm are independent Binomial(n,p) random variables, where n is known and p is unknown. Note that p E (0,1). Write down the expression of the likelihood function We assume that min(x1, . . . ,xm) 〈 n and max(x1, . . . ,xm) 〉 0 5 marks) Find , and give all possible solutions to the equation dL dL -...
Let X1,X2,...,Xn denote independent and identically distributed random variables with mean µ and variance 2. State whether each of the following statements are true or false, fully justifying your answer. (a) T =(n/n-1)X is a consistent estimator of µ. (b) T = is a consistent estimator of µ (assuming n7). (c) T = is an unbiased estimator of µ. (d) T = X1X2 is an unbiased estimator of µ^2. We were unable to transcribe this imageWe were unable to transcribe...