Influence diagnostics for stratified ordinal contingency tables
Liming Xiang, Kelvin K.W. Yau, Siu‐Keung Tse
Abstract
Liming Xiang, Kelvin K.W. Yau, Siu‐Keung Tse
Abstract
Influence diagnostics are investigated in this study. In particular, an approach based on the generalized linear mixed model setting is presented for formulating ordered categorical counts in stratified contingency tables. Deletion diagnostics and their first-order approximations are developed for assessing the stratum-specific influence on parameter estimates in the models. To illustrate the proposed model diagnostic technique, the method is applied to analyze two sets of data: a clinical trial and a survey study. The two examples demonstrate that the presence of influential strata may substantially change the results in ordinal contingency table analysis.
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Influence diagnostics are investigated in this study. In particular, an approach based on the generalized linear mixed model setting is presented for formulating ordered categorical counts in stratified contingency tables. Deletion diagnostics and their first-order approximations are developed for assessing the stratum-specific influence on parameter estimates in the models. To illustrate the proposed model diagnostic technique, the method is applied to analyze two sets of data: a clinical trial and a survey study. The two examples demonstrate that the presence of influential strata may substantially change the results in ordinal contingency table analysis.
Key concepts: Contingency table, Ordinal data, Categorical variable, Mathematics, Ordinal regression, Statistics, Ordinal Scale, Stratified sampling