Modelling Frequency and Count DataCategorical data analysis is a special area of generalised linear models, which has become the most important area of statistical applications in many disciplines, from medicine to social sciences. This text presents the standard models and many newly developed ones in a language which can be immediately applied in many modern statistical packages such as GLIM, GENSTAT, S-Plus, as well as SAS and LISP-STAT. The book is structure around the distinction between independent events occurring to different individuals, resulting in frequencies, and repeated events occurring to the same individuals, yielding counts. The book demonstates that much of modern statistics can be seen as special cases of categorical data models; both generalized linear models and proportional hazards models can be fitted as log linear models. More specialized topics such as Markov chains, overdispersion and random effects, are also covered. |
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analysis attitude binary binomial distribution CAlculate categorical data cells centre change in deviance Chapter Clinic Cook's distances data of Table dependence describe these data deviance diagonal dose effect eliminated Endmac Estimate s.e. example explanatory variables exponential family factor variable Find an appropriate fitted values following table frequencies gamma given in Table Haberman independence model indicating interaction iteratively large number Lindsey log linear model logistic model look Macro Male marginal Markov chain model fits model of Equation model to describe months mover-stayer model normal probability plot Observation number Fig Ordered normal Ordered residual ordinal variable overdispersion parameter estimates patients period plotted in Figure Poisson distribution Poisson process Poisson Residuals problem Q-Q Plot regression model response variable saturated model scale standard errors stationarity stayers sufficient statistics suicide T1TOTAL total number transition matrix treatment trials UREA vagotomy vector