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The data analytic methods routinely applied in the analysis of recidivism data in criminology have been expanded to include immune individuals. This book describes the theory and methods of incorporating them. The analysis of survival data via a model-fitting paradigm consists of the estimation of the parameters of a hypothesised model and the testing of the hypotheses concerning these parameters. The effects of covariates are assessed by asymptotic analyses. The accuracy and power of the asymptotic results are assessed by simulations of real situations. The authors develop the theoretical results required and exemplify the methodology by analysis of a variety of data sets. Probabilistic tools such as the law of large numbers and the central limit theorem are applied.
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The data analytic methods routinely applied in the analysis of recidivism data in criminology have been expanded to include immune individuals. This book describes the theory and methods of incorporating them. The analysis of survival data via a model-fitting paradigm consists of the estimation of the parameters of a hypothesised model and the testing of the hypotheses concerning these parameters. The effects of covariates are assessed by asymptotic analyses. The accuracy and power of the asymptotic results are assessed by simulations of real situations. The authors develop the theoretical results required and exemplify the methodology by analysis of a variety of data sets. Probabilistic tools such as the law of large numbers and the central limit theorem are applied.