Inadequacy of the Poisson assumption, due to the presence of overdispersion, in analysing count data has been reported by several authors (see McCaughran and Arnold (1976), Bliss and Owen (1958) etc.). Negative binomial distribution has been widely used to incorporate overdispersion in analysing the count data. Several test statistics for detecting negative binomial variation have been presented-C($\alpha$) tests, range-justified tests (appealing to the nonnegativity of the dispersion parameter) are compared with the static presented by Collings and Margolin (1985). One-way layout of data in the form of counts is often reported as a result of laboratory experiment or field work. Assuming the underlying distribution for the groups to be nega...
Discrete data in the form of counts arise in many health science disciplines such as biology and epi...
The new Log-Linear Test (TL) is proposed to identify when the Poisson model fails for a collection o...
Violations of Poisson assumptions usually result in overdispersion, where the variance of the model ...
Two C(a) statistics (Neyman, 1959) for testing the equality of the means of several groups of count ...
This thesis submitted in partial fulfillment of the requirements for the degree of Master of Science...
An efficient score statistic for testing the equality of the means of several groups of count data i...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
This thesis consists of two parts, referred as Part I and Part II. Part I. Testing homogeneity of se...
<p>Poisson and negative binomial models are frequently used to analyze count data in clinical trials...
Data in the form of proportions arise in toxicology (Weil, 1970; Williams, 1975) and other similar f...
SUMMARY. We consider the problem of testing whether a sample of observations comes from a single Poi...
Abstract: This paper represents the comparison between Negative Binomial Regression model and Genera...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
The Poisson and the Negative Binomial distributions are commonly used as analytic tools to model cou...
Discrete data in the form of counts arise in many health science disciplines such as biology and epi...
The new Log-Linear Test (TL) is proposed to identify when the Poisson model fails for a collection o...
Violations of Poisson assumptions usually result in overdispersion, where the variance of the model ...
Two C(a) statistics (Neyman, 1959) for testing the equality of the means of several groups of count ...
This thesis submitted in partial fulfillment of the requirements for the degree of Master of Science...
An efficient score statistic for testing the equality of the means of several groups of count data i...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
This thesis consists of two parts, referred as Part I and Part II. Part I. Testing homogeneity of se...
<p>Poisson and negative binomial models are frequently used to analyze count data in clinical trials...
Data in the form of proportions arise in toxicology (Weil, 1970; Williams, 1975) and other similar f...
SUMMARY. We consider the problem of testing whether a sample of observations comes from a single Poi...
Abstract: This paper represents the comparison between Negative Binomial Regression model and Genera...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
The Poisson and the Negative Binomial distributions are commonly used as analytic tools to model cou...
Discrete data in the form of counts arise in many health science disciplines such as biology and epi...
The new Log-Linear Test (TL) is proposed to identify when the Poisson model fails for a collection o...
Violations of Poisson assumptions usually result in overdispersion, where the variance of the model ...