The methods of moment estimator technique is a statistical method used to estimate unknown parameters in a probability distribution. It involves equating the theoretical moments of a distribution with the sample moments and solving for the unknown parameters.
This technique uses the sample moments, such as the mean and variance, to create equations that can be solved for the unknown parameters. These equations are based on the assumption that the sample moments are equal to the theoretical moments of the distribution. The solutions to these equations provide estimates for the unknown parameters.
The methods of moment estimator technique is relatively easy to understand and apply, making it a popular method for parameter estimation. It also does not require any assumptions about the shape of the distribution, making it a more flexible approach.
One limitation of this technique is that it may not always provide accurate estimates, especially when the sample size is small. It also assumes that the sample moments are equal to the theoretical moments, which may not always be true in practice.
The methods of moment estimator technique differs from other methods, such as maximum likelihood estimation, in that it does not require knowledge of the probability distribution function. Instead, it relies on the moments of the distribution to estimate the parameters.