i) Let X be a random variable with the PDF f(x: 0) =e-(1-", 00, S(x; A, jµ) = { ^†" -e/", x<0, where A>0 and u> 0 are unknown parameters. Find (simple) sufficient statistics for (A, u) and determine the MLES (A, î) of (A. µ). Now suppose that n 1. Is A an unbiased estimator of A? Justify your answer.
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![i) Let X be a random variable with the PDF
f(x: 0) =e-(--, 0<x<o,
where 0 has a prior distribution the exponential distribution with mean 1. Determine the
posterior distribution of 0.
Find the optimal Bayes estimator of 0 based on X under quadratic loss.
(ii) Let X, ..., X, be a sample from the PDF
-e-/A, x>0,
(x; A, µ) = { ^†"
-e/". x<0,
where A> 0 and u > 0 are unknown parameters. Find (simple) sufficient statistics for
(A, u) and determine the MLES (A, î) of (A, µ).
Now suppose that n 1. Is A an unbiased estimator of A? Justify your answer.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fda0d207b-5640-4b80-b8c0-0f5641085046%2F8840463b-f0d7-40f4-95a4-5465ab244572%2Fteg7zx_processed.jpeg&w=3840&q=75)
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- Let X and Y be independent Exp(2) random variables. Define W = X + Y. a) Determine the correlation between X and W. The joint pdf of X and W is: fx,w(x, w) = 1^(2) e^(-1w) I{OWe have a random variable X that is uniformly distributed on interval [-1,2]. Let Y = |X| - 1. We want to find pdf using CDF technique. What is the support and pdf for the random variable Y? What would fy(-.2) be? What about Fy(-.2) be?Identify the distributions of the random variables with the moment- generating functions shown below. For each random variable also indicate what the mean and the variance are. a) m(t) = e^2.2(e^t−1)b) m(t) = 1/(1 − 2t)^2 c) m(t) =( e^5t−e^t )/4tIf i have a variable Sigma for standard deviation and Z(T) which is a wiener process. If we take expectation of the formula below, what is the outcome if we follow a normal distribution. My text book says the variable becomes (1/2*Var) Sigma = Standard deviation Z(T) = Wiener process E_0(Sigma*Z(T))An ordinary (fair) coin is tossed 3 times. Outcomes are thus triple of “heads” (h) and tails (t) which we write hth, ttt, etc. For each outcome, let R be the random variable counting the number of tails in each outcome. For example, if the outcome is hht, then R (hht)=1. Suppose that the random variable X is defined in terms of R as follows X=6R-2R^2-1. The values of X are given in the table below. A) Calculate the values of the probability distribution function of X, i.e. the function Px. First, fill in the first row with the values X. Then fill in the appropriate probability in the second row.If a discrete random variable X has the following probability distribution: X -2, - 1, 0, 1, 2 P(X) 0.2, 0.3, 0.15, 0.2, 0.15 Use this to find the following: (a) The mean of X and E[X^2]. (b) The probability distribution for Y = 2X^2 + 2 (i.e, all values of Y and P(Y )). (c) Using part (b) (i.e, the probability distribution forY ), find E[Y ]. (d) Using part (a), verify your answer in part (c) for E[Y ]. **Note: Please do not just copy from Chegg!If X is a continuous random variable, what is the value of X that will be exceeded 30% of the time, if X has a gamma distribution with a mean of 2 and a variance of 2? (just set up the equation in itssimplest form)X is a normally normally distributed variable with mean u =10 and standard deviation a =4. Find A) P(x 1) C) P(10Let X1, X2, X3, ..., X, be a random sample from a distribution with known variance Var(X,) = o², and unknown mean EX, = 0. Find a (1 – a) confidence interval for 0. Assume that n is large.SEE MORE QUESTIONSRecommended textbooks for youMATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th…StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C…StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage LearningElementary Statistics: Picturing the World (7th E…StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. FreemanMATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th…StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C…StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage LearningElementary Statistics: Picturing the World (7th E…StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman