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- Let X₁, X2, X3,..., Xn denote a random sample of size n from the population distributed with the following probability density function: I e) f) g) where k> 0 is a constant. f(x; 2) = 2-kxk-1e (k-1)! if x > 0 elsewhere " Justify whether or not  is a uniform minimum variance unbiased estimator of 1. Check whether or not the MLE of λ is a consistent. Suggest with a Fisher's factorization theorem, the sufficient estimator of 1.4. Let X be a random variable with cumulative distribution function given by: F(r) = 1 – e-Az. Find the probability density function and so the expected value of variable X.Let X ~ Geom(p), a geometric distribution with parameter p € (0, 1). This is a discrete probability distribution on the positive integers, with p.m.f. given by fx (k) = (1 − p)k-¹p, k = = 1, 2,... (i) Verify (by calculation) that the log-likelihood function for the parameter p is given by l(x;p) = n (logp + (x − 1) log(1 − p)). - (ii) Derive the MLE estimator for p, and use it to compute the MLE estimate for the following sample data ¹: 1 3 1 1 1 2 3 1 1 1 1 (iii) What is the MLE estimator for 1/p? Verify this estimator is unbiased.
- 3. Let X1,.., Xn be a random sample from a uniform distribution on the interval [a, b). In other words, X U(a, b). Obtain method of moments estimators for a and b.Let X; € {1,2, 3, ...} be the number of days until relapse for patient i who is diagnosed with multiple sclerosis and currently in remission. We model this data using a geometric distribution with pmf iid X1, X2,..., Xn fxp(x | p) = (1 – p)*-'p for 0 < p < 1 defined on x E {1,2, 3, ...} and 0 elsewhere. Here, p is the risk of relapse on each day. 1. Using a p~ Beta(a, B) prior, derive the posterior density of p, fp|X, (p | Xn).5. Suppose that X is a discrete random variable with probability density function p(x) = cx², x = 1, 2, 3, 4. (c) Find Var(X). Select one: O a. 18.4 O b. 354 O c. none O d. 342.8Q3. Generate three random variates according to each of the following distributions using your own uniform random variates from U(0, 1) and list them: • Discrete Uniform: U (15, 17) • Uniform: U (0.57, 1.5). • Triangular: Triang (2, 10, 4). • Binomial: Bin (12, 0.4). • Lognormal: LN (5, 1). Beta: Beta (5, 5). • Negbin (8, 0.4) • Weibull (.5, 2.5) Geom (0.5) • PT5 (0.5, 0.5)The random variable X has expected value E(X)=4 and Var(X)=4. Here, we want to approximate the probability that X lies in the interval [3.4,5.4] using a transformation to a standard normal distribution Z. Then we will need to calculate the probability that Z lies in the interval [z1,z2] where z1= z2= Suggest z1 and z2 Do NOT attempt to make any sort of continuity correction.Recommended textbooks for youA First Course in Probability (10th Edition)ProbabilityISBN:9780134753119Author:Sheldon RossPublisher:PEARSONA First Course in Probability (10th Edition)ProbabilityISBN:9780134753119Author:Sheldon RossPublisher:PEARSON