We examine the finite sample properties of the maximum likelihood estimator for the binary logit model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error function for the maximum likelihood estimator in this model are derived, and we...

We examine the finite sample properties of the MLE for the Logit model with random covariates. We derive the second order bias and MSE function for the MLE in this model, and undertake some numerical evaluations to illustrate the analytic results. From these numerical results we find, for...

We examine the small-sample behaviour of the maximum likelihood estimator for the Poisson regression model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error for this estimator are derived, and we undertake some numerical evaluations to...

We investigate the small-sample quality of the maximum likelihood estimators (MLEs) of the parameters of the zero-inflated Poisson distribution. The finite-sample biases are determined to O(n-1) using an analytic bias reduction methodology based on the work of Cox and Snell (1968) and Cordeiro...

We derive expressions for the first-order bias of the MLE for a Poisson regression model and show how these can be used to adjust the estimator and reduce bias without increasing MSE. The analytic results are supported by Monte Carlo simulations and an empirical application.

Under the U.S. Department of Commerce’s ‘changed circumstances’ review, it is possible that the countervail duty on Canadian lumber can be lowered if administered stumpage prices are based on transaction evidence appraisal – on actual auction data and regression analysis. The Province of...

We model summer Olympic medal counts using count data analysis. The advantage of this methodology is its explicit recognition of the discrete non-negative form of the dependent variable; i.e. the total number of medals won by a nation in a summer Olympiad. Using data from the most recent 2004...

We discuss the modeling of count data whose empirical distribution is both multi-modal and overdispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special...

In this paper, we consider a simple preliminary-test estimation problem where the analyst's loss structure is represented by a ‘reflected Normal' penalty function. In particular we consider the estimation of the location parameter in a Normal sampling problem, where a preliminary test is...

We derive analytic expressions for the biases, to O(n-1), of the maximum likelihood estimators of the parameters of the generalized Rayleigh distribution family. Using these expressions to bias-correct the estimators is found to be extremely effective in terms of bias reduction, and generally...