Large sample techniques for statistics
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Large sample techniques for statistics
（Springer texts in statistics）
Springer, c2010
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Includes bibliographical references (p. [585]-601) and index
Description and Table of Contents
Description
In a way, the world is made up of approximations, and surely there is no exception in the world of statistics. In fact, approximations, especially large sample approximations, are very important parts of both theoretical and - plied statistics.TheGaussiandistribution,alsoknownasthe normaldistri- tion,is merelyonesuchexample,dueto thewell-knowncentrallimittheorem. Large-sample techniques provide solutions to many practical problems; they simplify our solutions to di?cult, sometimes intractable problems; they j- tify our solutions; and they guide us to directions of improvements. On the other hand, just because large-sample approximations are used everywhere, and every day, it does not guarantee that they are used properly, and, when the techniques are misused, there may be serious consequences. 2 Example 1 (Asymptotic? distribution). Likelihood ratio test (LRT) is one of the fundamental techniques in statistics. It is well known that, in the 2 "standard" situation, the asymptotic null distribution of the LRT is?,with the degreesoffreedomequaltothe di?erencebetweenthedimensions,de?ned as the numbers of free parameters, of the two nested models being compared (e.g., Rice 1995, pp. 310). This might lead to a wrong impression that the 2 asymptotic (null) distribution of the LRT is always? . A similar mistake 2 might take place when dealing with Pearson's? -test-the asymptotic distri- 2 2 bution of Pearson's? -test is not always? (e.g., Moore 1978).
Table of Contents
The ?-? Arguments.- Modes of Convergence.- Big O, Small o, and the Unspecified c.- Asymptotic Expansions.- Inequalities.- Sums of Independent Random Variables.- Empirical Processes.- Martingales.- Time and Spatial Series.- Stochastic Processes.- Nonparametric Statistics.- Mixed Effects Models.- Small-Area Estimation.- Jackknife and Bootstrap.- Markov-Chain Monte Carlo.
by "Nielsen BookData"
