Preface; 1. Preliminaries; 2. Some concepts and simple applications; 3. Significance tests; 4. More complicated situations; 5. Some interpretational issues; 6. Asymptotic theory; 7. Further aspects of maximum likelihood; 8. Additional objectives; 9. Randomization-based analysis; Appendix A. A brief history; Appendix B. A personal view; References; Author index; Index.
A comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies.
D. R. Cox is one of the world's preeminent statisticians. Author or co-author of sixteen books and roughly 250 papers, his work on the proportional hazards regression model is one of the most-cited and most influential papers in modern statistics.
'A deep and beautifully elegant overview of statistical inference, from one of the towering figures who created modern statistics. This book should be essential reading for all who call themselves 'statistician'.' David Hand, Imperial College London 'The explanations of key concepts are written so clearly ... that they may be understood even if the mathematical details are skipped.' MAA Online 'The text is very well written and gives a balanced view of the frequentist and Bayesian notions of probability, without favouring one over the other.' Journal of Applied Statistics '... ideally suited for statisticians at all levels who want to refresh their own understanding of the theory of statistical inference without having to wade through theorems and proofs.' Biometrics
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