![]() ![]() Generally we use a low-order form.Īssuming the sample mean is $\overline X$, it is not difficult to calculate a low-order moment estimate of the parameter $p$ as $\frac \ne p. We know that there are two kinds of moment estimates for the geometric distribution parameter $p$. ![]() Use the following data to give a point estimate of p: 3 34 7 4 19 2 1 19 43 2 22 4 19 11 7 1 2 21 15 16 This problem has been solved You'll get a detailed solution from a subject matter expert that helps you learn core concepts. Explain intuitively why your estimate makes good sense. Suppose that the maximum value of Lx occurs at u(x) for each x S. In the method of maximum likelihood, we try to find the value of the parameter that maximizes the likelihood function for each value of the data vector. First introduced in 1887 by Chebychev in his proof on the Central Limit Theorem, the method of moments was then developed in the last 1800s by Karl Pearson. The likelihood function at x S is the function Lx: 0, ) given by Lx() f(x). ![]() I recently had trouble calculating the moment estimates for the parameter $p$ of the geometric distribution: Use the method of moments to find a point estimate for p. Method of moments is thought to be one of the oldest, if not the oldest method for finding point estimators. ![]()
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