Statistical Theory and ModellingD.V. Hinkley, N. Reid, E. J. Snell Statistical Theory and Modelling is a celebration of the work of Sir David Cox, FRS, and reflects his many interests in statistical theory and methods. It is a series of review articles, intended as an introduction to a variety of topics suitable for the graduate student and practicing statistician. Many of the topics are the subject of book-length treatments by Sir David and authors of this volume. Each chapter leads to a larger literature. Topics range the breadth of statistics and include modern degvelopments in statistical theory and methods. Special topics covered are generalized linear models, residuals and diagnostics, survival analysis, sequential analysis, time series, stochastic modelling of spatial data, design of experiments, likelihood inference and statistical approximation. |
Contents
Applied statistics | 30 |
Generalized linear models | 55 |
38 | 81 |
Copyright | |
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Common terms and phrases
algorithm analysis ancillary statistic applications approximation assumed assumption binary binomial Biometrika calculation components computed conditional confidence intervals considered covariance cumulants D-optimum defined density depend derived design of experiments deviance discussed equation error estimating function example experimental explanatory variables exponential distribution exponential family factors failure given H₁ hazard function independent infection inference least squares likelihood function likelihood ratio statistic linear model log-likelihood logistic marginal likelihood matrix maximum likelihood estimator McCullagh mean methods minimal multivariate Nelder nonlinear normal distribution nuisance parameters null hypothesis observed obtained optimal optimum design orthogonal overdispersion plot point processes Poisson process possible probability problems procedure properties quasi-likelihood rainfall random variables regression models renewal process residuals response sample score sequential simple SPRT standard stationary stochastic process sufficient statistic survival test statistic theorem theory transformation treatment usually values vector Y₁ zero