(a) Suppose the random variable, X, follows a geometric distribution with parameter 0 (0 < < 1). Let X₁, X2,..., X₁, be a random sample of size n from the population of X. Justify if == is an unbiased estimator for 0. 2 The differentiation approach to derive the maximum likelihood estimator (mle) is not appropriate in all the cases. Let X₁, X2, X₁, be a random sample of size n from the population of X. Consider the probability function of X 1 (1:0) = {6-(0-0), if 0≤x≤00 for-00<0 <0 otherwise. (g) Find the efficiencies of the mle and mme, and discuss under what condition the two estimators, and , are equally efficient.

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(a)
Suppose the random variable, X, follows a geometric distribution with parameter 0 (0 <
0 <1). Let X₁, X2,..., Xn be a random sample of size n from the population of X.
Justify if ô = i is an unbiased estimator for 0.
2
The differentiation approach to derive the maximum likelihood estimator (mle) is not
appropriate in all the cases. Let X₁, X2, X₁, be a random sample of size n from the
population of X. Consider the probability function of X
f(x; 0) =
Je-(2-0), if 0<x<∞ for -∞<<∞
0,
otherwise.
(g) Find the efficiencies of the mle and mme, and discuss under what condition the two
estimators, and , are equally efficient.
Transcribed Image Text:(a) Suppose the random variable, X, follows a geometric distribution with parameter 0 (0 < 0 <1). Let X₁, X2,..., Xn be a random sample of size n from the population of X. Justify if ô = i is an unbiased estimator for 0. 2 The differentiation approach to derive the maximum likelihood estimator (mle) is not appropriate in all the cases. Let X₁, X2, X₁, be a random sample of size n from the population of X. Consider the probability function of X f(x; 0) = Je-(2-0), if 0<x<∞ for -∞<<∞ 0, otherwise. (g) Find the efficiencies of the mle and mme, and discuss under what condition the two estimators, and , are equally efficient.
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