# Properties of two modified moment estimators for parameters of the negative binomial distribution

1965

## Publisher

Virginia Polytechnic Institute

## Abstract

This dissertation deals with the properties of two modified moment estimators for parameters of the negative binomial distribution (NBD).

Several parametric forms have been suggested for the NBD. The estimation problems vary according to the form which is used. In particular, the form proposed by Anscombe [Biometrika, 37 (1950), pp. 358-382), with parameters λ and α, has received wide attention and was selected for study in this investigation. In Anscombe's parametric form, the mean of the NBD is λ and the variance is λ + λ²/α.

While the parameter λ is universally estimated by the sample mean, many different methods of estimation for α have been attempted. Among these, the maximum likelihood estimator α* and the simple moment estimator â are most often used. However, α* is quite difficult to obtain numerically and often this computation requires the use of an electronic computer. In addition, â, while not difficult to compute, is often inefficient. For these reasons, it was felt that a study of the two modified moment estimators â₁ and â₂, suggested by Shenton and Wallington [Moment Estimators and Modified Moment Estimators with Special Reference to the Negative Binomial Distribution (unpublished)], was needed.

In the text, the method of obtaining modified moment estimators in general is given in detail. The application of this method to the NBD is discussed and, in particular, the derivations of â₁ and â₂ are presented. Since orthogonal statistics play an important part in this work, their definition and applications are reviewed.

In order to evaluate the small sample properties of â₁ and â₂, asymptotic expansions, in powers of 1/n, of their biases, variances, covariance determinants, and higher moments were determined numerically in the parameter space (1 ≤ α ≤ 100, 1 ≤ λ ≤ 100), through terms to n⁻⁴. The computational method for this work is described in detail. Tables and charts which display the nature of the expansions are given in the text.

The results show that the behavior patterns of the moment expansions for â₁ and â₂ are somewhat similar to those for â and α*. For both â₁ and â₂, the n⁻⁴ term contributes heavily in all the expansions when α > λ. Thus, as with the other estimators, a first term approximation would not suffice for the properties of â₁ and â₂.

Further, the results give evidence that â₁ and â₂ are highly efficient for most α and λ, and, in some regions of the parameter space, have less bias than α* and â. Some experimental data was fitted to the NBD using the estimators â₁, â₂, â, and α*. In all of the examples given, the modified moment estimators provided a better fit of the data than did the simple moment estimator and, in one instance, a better fit than was obtained by the maximum likelihood estimator.