On Sequential Binomial Estimation

Estimation of Count Data using Bivariate Negative Binomial Regression Models

Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...

Information and Estimation over Binomial and Negative Binomial Models

In recent years, a number of results have been developed which connect information measures and estimation measures under various models, including, predominently, Gaussian and Poisson models. More recent results due to Taborda and Pérez-Cruz relate the relative entropy to certain mismatched estimation errors in the context of binomial and negative binomial models, where, unlike in the case of ...

On Nonparametric Sequential Point Estimation

On the Estimation of Relative Risks via Log Binomial Regression

Given the well known convergence difficulties in fitting log binomial regression with standard GLM software, we implement a direct solution via constrained optimization which avoids the circumventions found in the literature. The use of a log binomial model is motivated by our interest in directly estimating relative risks adjusted for confounders. A Bayesian log binomial regression model is al...

Improving interval estimation of binomial proportions

In this paper, we propose one new confidence interval for the binomial proportion; our interval is based on the Edgeworth expansion of a logit transformation of the sample proportion. We provide theoretical justification for the proposed interval and also compare the finite-sample performance of the proposed interval with the three best existing intervals-the Wilson interval, the Agresti-Coull ...