For Poisson or multinomial contingency table data the conditional distribution is product multinomial when conditioning on observed values of explanatory variables. Birch (1963) showed that under the restriction formed by keeping the marginal totals of one margin fixed at their observed values the Poisson, multinomial and product multinomial likelihoods are proportional and give the same estimates for common parameters in the log linear model. Here the inverses of the Fisher information matrices are shown to be identical over common parameters so that the asymptotic covariance matrices of the estimates correspond. Some key words: Conditional inference; Contingency table; Fisher information; Log linear model. 1
In this work, we derived new asymptotic results for multinomial models. To obtain these results, we ...
Matrix algebra is extensively used in the study of linear models and multivariate analysis (see for ...
Fisher matrices play an important role in experimental design and in data analysis. Their primary ro...
Abstract: The nature of linear information models (LIM) Analysis of categorical data by linear infor...
The paper considers general multiplicative models for complete and incomplete contingency tables tha...
In the statsitical analysis of observations from multinomial distribution, it is somtimes estimate t...
AbstractThe paper considers general multiplicative models for complete and incomplete contingency ta...
Categorical data frequently arise in applications in the Social Sciences. In such applications, the ...
Abstract The inverse of the Fisher Information Matrix is a lower bound for the co-variance matrix of...
In categorical data analysis, log-linear models are widely used statistical tools for analyzing the ...
AbstractIn this paper we consider categorical data that are distributed according to a multinomial, ...
International audienceThis paper is devoted to the link between the Fisher Information Matrix invert...
Analysis of large dimensional contingency tables is rather difficult. Fienberg and Kim (1999, Journa...
In this work, we derived new asymptotic results for multinomial models. To obtain these results, we ...
Categorical data in contingency tables are collected in many investigations. In order to underst and...
In this work, we derived new asymptotic results for multinomial models. To obtain these results, we ...
Matrix algebra is extensively used in the study of linear models and multivariate analysis (see for ...
Fisher matrices play an important role in experimental design and in data analysis. Their primary ro...
Abstract: The nature of linear information models (LIM) Analysis of categorical data by linear infor...
The paper considers general multiplicative models for complete and incomplete contingency tables tha...
In the statsitical analysis of observations from multinomial distribution, it is somtimes estimate t...
AbstractThe paper considers general multiplicative models for complete and incomplete contingency ta...
Categorical data frequently arise in applications in the Social Sciences. In such applications, the ...
Abstract The inverse of the Fisher Information Matrix is a lower bound for the co-variance matrix of...
In categorical data analysis, log-linear models are widely used statistical tools for analyzing the ...
AbstractIn this paper we consider categorical data that are distributed according to a multinomial, ...
International audienceThis paper is devoted to the link between the Fisher Information Matrix invert...
Analysis of large dimensional contingency tables is rather difficult. Fienberg and Kim (1999, Journa...
In this work, we derived new asymptotic results for multinomial models. To obtain these results, we ...
Categorical data in contingency tables are collected in many investigations. In order to underst and...
In this work, we derived new asymptotic results for multinomial models. To obtain these results, we ...
Matrix algebra is extensively used in the study of linear models and multivariate analysis (see for ...
Fisher matrices play an important role in experimental design and in data analysis. Their primary ro...