TOPIC:Application of the Central limit theorem.
5. A confidence interval for Poisson variables Bookmark this page (a) 2 points possible (graded) Let...
1 Bookmark this page Setup: For all problems on this page, suppose you have data X],...,x . N (0,1) that is a random sample of identically and independently distributed standard normal random variables. Useful facts: The following facts might be useful: For a standard normal random variable X1, we have: E[X] =0, E[X{1=1, E(X) = 3. Sample mean 1.5 points possible (graded, results hidden) Consider the sample mean: X = x + X2+...+X,). What are the mean E [Xn] and...
2. Biased and unbiased estimation for variance of Bernoulli variables A Bookmark this page 2 points possible (graded) Let X1, X, bed. Bernoull random variables, with unknown parameter PE (0,1). The aim of this exercise is to estimate the common variance of the X First, recall what Var (X) is for Bernoulli random variables. Var (X) - Let X, be the sample average of the Xi. X. - 3x Interested in finding an estimator for Var(X), and propose to use...
11. Chi-Squared Test for a Family of Discrete Distributions A Bookmark this page In the problems on this page you will apply the goodness of fit test to determine whether or not a sample has a binomial distribution So far, we have used the x test to determine if our data had a categorical distribution with specific parameters (e.s uniform on an set). element For the problems on this page, we extend the discussion on x tests beyond what was...
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...
3. The PDF of the maximum Bookmark this page Problem 3. The PDF of the maximum 3 points possible (graded) Let X and Y be independent random variables, each uniformly distributed on the interval [0, 1] Let Z = max(X, Y). Find the PDF of Z. Express your answer in terms of z using standard notation. For 0<z<1 f2(z) = 1. 2. Let Z max(2X, Y. Find the PDF of Z. Express your answer in terms of z using standard...
Degrees of Freedom of a Known Test 2 points possible graded) Let us consider a statistical model with parameter ER". Let O be the parameter that generates the n lid samples X1,..., X, Let I ) be the Fisher information and assume that the MLE is asymptotically normal. Assume that I(C) is a diagonal matrix with positive entries 1/t1,...,1/td. We wish to perform a test for the hypotheses H : 8 - and H:8 + . Let the test statistic...
Problem 4 True or False A Bookmark this page Instructions: Be very careful with the multiple choice questions below. Some are "choose all that apply," and many tests your knowledge of when particular statements apply As in the rest of this exam, only your last submission will count. 1 point possible (graded, results hidden) The likelihood ratio test is used to obtain a test with non-asymptotic level o True O False Submit You have used 0 of 3 attempts Save...
8. A Union-Intersection Test Bookmark this page Let X1,…,Xn be i.i.d. Bernoulli random variables with unknown parameter p∈(0,1). Suppose we want to test H0:p∈[0.48,0.51]vsH1:p∉[0.48,0.51] We want to construct an asymptotic test ψ for these hypotheses using X¯¯¯¯n. For this problem, we specifically consider the family of tests ψc1,c2 where we reject the null hypothesis if either X¯¯¯¯n<c1≤0.48 or X¯¯¯¯n>c2≥0.51 for some c1 and c2 that may depend on n, i.e. ψc1,c2=1((X¯¯¯¯n<c1)∪(X¯¯¯¯n>c2))where c1<0.48<0.51<c2. Throughout this problem, we will discuss possible choices...
Exercise 5.22. Let (Xn)nel be a sequence of i.i.d. Poisson(a) RVs. Let Sn-X1++Xn (i) Let Zn-(Sn-nA)/Vm. Show that as n-, oo, Zn converges to the standard normal RV Z ~ N(0,1) in distribution (ii) Conclude that if Yn~Poisson(nX), then ii) Fromii) deduce that we have the following approximation which becomes more accurate as noo.
Central Limit Theorem: let x1,x2,...,xn be I.I.D. random variables with E(xi)= U Var(xi)= (sigma)^2 defind Z= x1+x2+...+xn the distribution of Z converges to a gaussian distribution P(Z<=z)=1-Q((z-Uz)/(sigma)^2) Use MATLAB to prove the central limit theorem. To achieve this, you will need to generate N random variables (I.I.D. with the distribution of your choice) and show that the distribution of the sum approaches a Guassian distribution. Plot the distribution and matlab code. Hint: you may find the hist() function helpful