A Bayesian generalized random regression model for estimating heritability using overdispersed count data
We consider the problem of constructing confidence intervals for the mean of a Negative Binomial random variable based upon sampled data. When the sample size is large, it is a common practice to rely upon a Normal distribution approximation to construct these intervals. However, we demonstrate that the sample mean of highly dispersed Negative Binomials exhibits a slow convergence in distribution to the Normal as a function of the sample size. As a result, standard techniques (such as the Normal approximation and bootstrap) will construct confidence intervals for the mean that are typically too narrow and significantly undercover at small sample sizes or high dispersions. To address this problem, we propose techniques based upon Bernstein's inequality or the Gamma and Chi Square distributions as alternatives to the standard methods. We investigate the impact of imposing a heuristic assumption of boundedness on the data as a means of improving the Bernstein method. Furthermore, we propose a ratio statistic relating the Negative Binomial's parameters that can be used to ascertain the applicability of the Chi Square method and to provide guidelines on evaluating the length of all proposed methods. We compare the proposed methods to the standard techniques in a variety of simulation experiments and consider data arising in the serial analysis of gene expression and traffic flow in a communications network.