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Exercise 3.16: A sample of n independent observations is taken on a rv. X having a logarithmic series distribution, x=1, 2, EWT-0), , x In . Show that the MLE θ of θ where θ is an unknown parameter i...
We have n independent observations from a geometric distribution with unknown parameter θ. Po(X,-k-θ(1-0)4-1 for k-1, 2, 3, . . . We wish to test the null hypothesis θ-1/2 versus the alternative θ 7|/2. we can show that the MLE θ-1/2. Write out the appropriate LRT statistic as a function of the r, the mean of the observations
4. We have n independent observations from a geometric distribution with unknown parameter θ. PoX, k 0(1- 0)1 or1,2,3,... We wish to test the null hypothesis θ-1/2 versus the alternative θ 1 /2. we can show that the MLE θ-1/2. Write out the appropriate LRT statistic as a function of the x, the mean of the observations
4. We have n independent observations from a geometric distribution with unknown parameter θ. PoX, k 0(1- 0)1 or1,2,3,... We wish to test the null hypothesis θ-1/2 versus the alternative θ 1 /2. we can show that the MLE θ-1/2. Write out the appropriate LRT statistic as a function of the x, the mean of the observations
4. We have n independent observations from a geometric distribution with unknown parameter Pe(X = k} = θ(1-0)k-1 for k = 1.2.3. We wish to test the null hypothesis θ-1/2 versus the alternative θ 1/2, we can show that the MLE θ = 1/z. write out the appropriate LRT, statistic as a function of the z, the mean of the observations.
Suppose a simple random sample (Xi i.i.d. observations) was taken from a uniform[0,θ] distribution with an unknown parameter θ Sample observations are below: 1.984, 3.551, 2.367, 3.201, 3.690, 3.256, 2.949, 1.872, 3.357, 2.026, 2.358, 3.337, 1.582, 0.902, 0.718, 1.850, 2.026, 1.326, 3.491 Let τ=P(X≤0.50). MLE of τ
11. Obtain the MLE estimate for the beta parameter in Gamma distribution defined below for n iid (identical and independent) observations in a sample. Show steps. Obtain the MLE estimate for the alpha parameter. The continuous random variable X has a gamma distribution, with param eters α and β, if its density function is given by x>0, elsewhere, .tor"-le-z/ß, f(x; α, β)-Ί where α > 0 and β > 0. (You will also need the beta estimate, use the direct...
Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-re, x > 0 ) is sufficient for θ, using x/θ the definition ofsuficiency. Then show that the mle of θ is a function of the sufficient statistic.
Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-re, x > 0 ) is...
Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-",x > 0 ) is sufficient for θ, using the definition ofsuficiency. Then show that the mle of θ is a function of the sufficient x10 statistic.
Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-",x > 0 ) is sufficient for...
Let Xi , X2,. … X, denote a random sample of size n > 1 from a distribution with pdf f(x:0)--x'e®, x > 0 and θ > 0. a. Find the MLE for 0 b. Is the MLE unbiased? Show your steps. c. Find a complete sufficient statistic for 0. d. Find the UMVUE for θ. Make sure you indicate how you know it is the UMVUE.
Let Xi , X2,. … X, denote a random sample of size n...
Problem 1: Let (Xi,..., Xn) denote a random variable from X having a Log-normal density fx (x) = d(L m)/ x, x 〉 0 n(x) - where m is an unknown parameter. Show n-1 Σ'al Ln(X) is a MVU estimator for m.
Problem 1: Let (Xi,..., Xn) denote a random variable from X having a Log-normal density fx (x) = d(L m)/ x, x 〉 0 n(x) - where m is an unknown parameter. Show n-1 Σ'al Ln(X) is a...