Can anyone explain blue writing? Thank you!!
Query 1: No Moment generating function is not the only way of getting the density of the linear combination. Jacobian method can also be used for the same.
Query 2:Yes this is a formula and it would be helpful if you could remembe it, But anyway you can readily derieve this if required.
Query 3:
Can anyone explain blue writing? Thank you!! Let Yı and Y2 be independent, Normal random variables,...
Let yı, y2,-. ., yn be a sample drawn from a normal population with unknown mean μ an model d unknown variance σ2. One way to estimate μ is to fit the linear (2.61) and use the least squares (LS), that is, to minimize the sum of squares, Σ (Vi-A)2. Another way is to use the least absolute value (L AV), that is, to minimize the sum of absolute value of the vertical distances, Σ bi-μ| (a) Show that the...
Let Y1, Y2, , Yn be independent, normal random variables, each with mean μ and variance σ^2. (a) Find the density function of f Y(u) = (b) If σ^2 = 25 and n = 9, what is the probability that the sample mean, Y, takes on a value that is within one unit of the population mean, μ? That is, find P(|Y − μ| ≤ 1). (Round your answer to four decimal places.) P(|Y − μ| ≤ 1) = (c)...
Let Xi, x,, ,X, be independent random variables with mean and variance σ . Let Y1-Y2, , Y, be independent random variables with mhean μ and variance a) Compute the expected value of W b) For what value of a is the variance of W a minimum? σ: Let W-aX + (1-a) Y, where 0 < a < 1. Let Xi, x,, ,X, be independent random variables with mean and variance σ . Let Y1-Y2, , Y, be independent random...
Let Yı, Y2, Ys, and Y4 be independent, identically distributed random variables from a mean u and a variance 02. Consider a different estimator of u: W=Y+Y2+2Y3+ Y 00 This is an example of a weighted average of the Y a) Show that W is a linear estimator. b) Is W an unbiased estimator of u? Show that it is - or it isn't (E(W) = Find the variance of W and compare it to the variance of the sample...
Let the independent normal random variables Y1,Y2, . . . ,Yn have the respective distributions N(μ, γ 2x2i ), i = 1, 2, . . . , n, where x1, x2, . . . , xn are known but not all the same and no one of which is equal to zero. Find the maximum likelihood estimators for μ and γ 2.
Please prove the following theorem: Let Yı, Y2, ... ,Yn be independent normally distributed random variables with E(Y;) = Hi and V(Y) = 0;, for i = 1, 2,..., n, and let 21, 22, ...,an be constants. If maiYi = ajY1 + a2Y2 + ...anYn i=1 then U is a normally distributed random variable with E(U) = Žar, and v(u) = 4:07. i= 1 (Hint: the moment generating function of Y ~ N(u,02) is 02t2 m(t) = E(etY) = exp...
1. Let Xi l be a random sample from a normal distribution with mean μ 50 and variance σ2 16. Find P (49 < Xs <51) and P (49< X <51) 2. Let Y = X1 + X2 + 15 be the sun! of a random sample of size 15 from the population whose + probability density function is given by 0 otherwise 1. Let Xi l be a random sample from a normal distribution with mean μ 50 and...
QUESTION 2 Let Xi.. Xn be a random sample from a N (μ, σ 2) distribution, and let S2 and Š-n--S2 be two estimators of σ2. Given: E (S2) σ 2 and V (S2) - ya-X)2 n-l -σ (a) Determine: E S2): (l) V (S2); and (il) MSE (S) (b) Which of s2 and S2 has a larger mean square error? (c) Suppose thatnis an estimator of e based on a random sample of size n. Another equivalent definition of...