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- W63695601 abstract "This chapter discusses frequentist inferences. The major operational difference between Bayesian and frequentist inferences is that in the latter, one must choose a reference set for the sample to obtain inferential probabilities. In the matter of choosing a reference set, Sherlock Holmes was right, and that many frequentist inferences are inadequate because of erroneous choices made prior to the experiment. Most of the mathematical development has to do with predata analysis. However, the question remain was that, “Is such-and-such likely to be a good procedure?” It is evident that relative frequency in a real sequence of repeated experiments is simply not a rich enough interpretation of probability. The brief general discussion has raised questions about the difference between pre- and postdata probability calculations, the legitimacy of exact theory when integrated with practical application, and careful specification and understanding of what a statistical model means in practical terms. The purpose of the more detailed discussion which follows is to consider four topics where naive application of frequentist statistical theory can lead to incorrect or unhelpful inferences, whereas careful attention to the above questions can lead to sensible frequentist inferences. The four topics to be discussed are likelihood inference, inference from transformed data, randomization in design of experiments and surveys, and robust estimation. The Bayesian analysis has the general advantage of responding to specific features of the data. The unconditional distribution theory prevalent in robustness literature is fine for choosing estimates which are generally good but not for analysis of a particular data set." @default.
- W63695601 created "2016-06-24" @default.
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- W63695601 date "1983-01-01" @default.
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- W63695601 title "Can Frequentist Inferences Be Very Wrong? A Conditional “Yes”" @default.
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- W63695601 doi "https://doi.org/10.1016/b978-0-12-121160-8.50010-7" @default.
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