Consider a sample of i.i.d. random variables X1,..., X and assume their common density is given...
Consider a sample of i.i.d. random variables X1,..., X, and assume their common density is given by fo(a) = exp (3) 1(220), where is an unknown parameter Consider the following set of hypotheses: H:0=1 and H:0 1. You will perform the likelihood ratio test at significance level 7% for these hypotheses. Assume that the sample size is n=500. You observe that • The sample mean is 1 , X; = 0.86: • The (biased) sample variance is 15. (X- Xn)...
Let X1,... Xn i.i.d. random variable with the following riemann density: with the unknown parameter θ E Θ : (0.00) (a) Calculate the distribution function Fo of Xi (b) Let x1, .., xn be a realization of X1, Xn. What is the log-likelihood- function for the parameter θ? (c) Calculate the maximum-likelihood-estimator θ(x1, , xn) for the unknown parameter θ
2. Recap: Maximum Likelihood Estimators and Fisher information Bookmark this page Instructions: For each of the following distributions, compute the maximum likelihood estimator based on n i.i.d. observations X1,..., Xn and the Fisher information, if defined. If it is not enter DNE in each applicable input box. (d) 7 points possible (graded) X; ~N (u,0?), u ER, o? > 0, which means that each X1 has density Hint: Keep in mind that we consider o? as the parameter, not o....
Instructions: For each of the following distributions, compute the maximum likelihood estimator based on n i.d. observations X····, Xn and the Fisher information, if defined. If it is not, enter DNE in each applicable input box. which means that each X1 has density exp (-( 1)2 202 Hint: Keep in mind that we consider σ2 as the parameter, not σ . You may want to write τ-σ2 in your computation. (Enter barx_n for the sample average Xn and bar(X_n 2)...
6. Suppose that X1, ..., Xn is a random sample from a population with the probability density function f(x;0), 0 E N. In this case, the esti- mator ÔLSE = arg min (X; – 6)? n DES2 i=1 is called the least square estimator of Ô. Now, suppose that X1, ..., Xn is a random sample from N(u, 1), u E R. Prove that the least square estimator of u is the same as maximum likelihood estimator of u.
Let X1,X2,...,Xn denote a random sample from the Rayleigh distribution given by f(x) = (2x θ)e−x2 θ x > 0; 0, elsewhere with unknown parameter θ > 0. (A) Find the maximum likelihood estimator ˆ θ of θ. (B) If we observer the values x1 = 0.5, x2 = 1.3, and x3 = 1.7, find the maximum likelihood estimate of θ.
2. Suppose Xi,X2,..., Xn are i.i.d. random variables such that a e [0, 1] and has the following density function: r (2a) (1a-1 where ? > 0 is the parameter for the distribution. It is known that E(X) = 2 Compute the method of moments estimator for a
Multi-part question: Let X1, ..... , Xn be random variables that describe the height of students from a class, in the logarithmic scale. A) Write the statistical model (there might be more than one suitable distribution). B) Assume that X1, ... ,Xn form a random sample from the normal distribution with known mean θ and unknown variance σ^2 . Find the maximum likelihood estimator of the variability of the height (in log scale) of the students, this is, find the...
X = (X1, X2), i.i.d. Bernoulli(θ) random variables, where θ is unknown. (b) Consider the following three estimators 01(X1,X2) 2 the L2 error for each of these estimators, given by 2 (ii) Compute the L2 error for each of these estimators, under the assumption that the unknown but true value of the parameter is θο, for any θο E Θ. [6 marks]
Let X1, ..., X., be i.i.d random variables N(u, 02) where u is known parameter and o2 is the unknown parameter. Let y() = 02. (i) Find the CRLB for yo?). (ii) Recall that S2 is an unbiased estimator for o2. Compare the Var(S2) to that of the CRLB for