Principles of Statistical InferenceCambridge University Press, 10 août 2006 - 219 pages In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses. |
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aid to interpretation answers Appendix application arguments aspects assessment Barnard based Bayes Bayesian approach book broadly calibrate chain chosen clarify Fisher's coherency Comparable conceptual conditional confidence intervals confidence limits contribution controversial conveni critical data under analysis data-generating process degree of belief developed direct error especially example experimental design explicitly Fisher Flat priors formal frequentist hypothetical ideas impact important inconsistent inference influential information additional internal consistency involved issues justification largely ignored lead least approximately likelihood link major measure ment methods of analysis Neyman and Pearson notably notion obtained p-value paper parameter of interest particular partly personal decision-making personalistic probability pioneer possible primarily principle prior distribution problem procedures provide qualitative quantitative question Ramsey reason recognize regarded rejection relation represented role route significance tests single real number situations sometimes sources of information specific statistical model strongly subject-matter systematic theorem theory translation uncertainty underlying utility view of Bayesian view of probability writing wrong