Titlebook: Parametric and Nonparametric Inference from Record-Breaking Data; Sneh Gulati,William J. Padgett Book 2003 Springer Science+Business Media

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Nefarious

Bayesian Models,ic Bayes and the empirical Bayes estimators of the underlying survival function for such data under a Dirichlet process prior and squared-error loss function. In this chapter, all of the work done on Bayesian inference from record-breaking data is summarized, starting with the work of Dunsmore (1983).

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Introduction,henomenon. What was the coldest day last year (or ever), which city has the lowest crime rate, what was the shortest time recorded to complete a marathon, who holds the record in eating the most number of hot dogs in the shortest period, what was the highest stock value thus far? The list could go o

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Parametric Inference,lues to determine if upper records were from an i.i.d sequence of observations (details of the test are presented in the next chapter). After that, however, statistical inference from record-breaking data remained virtually unexplored until the late 1970s when some statisticians started investigatin

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Smooth Function Estimation,s obtained for the probability density function and the cumulative distribution function were discrete. Typically, these estimates were hard to update and required more computation than necessary. This was not a serious problem if the functions being estimated were themselves discrete, but most life