摘要
A Poisson distribution is well used as a standard model for analyzing count data. Most of the usual constructing confidence intervals are based on an asymptotic approximation to the distribution of the sample mean by using the Wald interval. That is, the Wald interval has poor performance in terms of coverage probabilities and average widths interval for small means and small to moderate sample sizes. In this paper, an approximate confidence interval for a Poisson mean is proposed and is based on an empirically determined the tail probabilities. Simulation results show that the pro- posed interval outperforms the others when small means and small to moderate sample sizes.
A Poisson distribution is well used as a standard model for analyzing count data. Most of the usual constructing confidence intervals are based on an asymptotic approximation to the distribution of the sample mean by using the Wald interval. That is, the Wald interval has poor performance in terms of coverage probabilities and average widths interval for small means and small to moderate sample sizes. In this paper, an approximate confidence interval for a Poisson mean is proposed and is based on an empirically determined the tail probabilities. Simulation results show that the pro- posed interval outperforms the others when small means and small to moderate sample sizes.
