| 書目名稱 | Large Sample Techniques for Statistics | | 編輯 | Jiming Jiang | | 視頻video | http://file.papertrans.cn/582/581348/581348.mp4 | | 概述 | Focuses on thinking skills rather than just what formulae to use.Provides motivations, and intuition, rather than detailed proofs.Begins with very simple and basic techniques, and connects theory and | | 叢書名稱 | Springer Texts in Statistics | | 圖書封面 |  | | 描述 | 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 | | 出版日期 | Textbook 20101st edition | | 關(guān)鍵詞 | Approximations; Asymptotic Theory; Large Sample Theory; Limit Theorems; Parametric statistics; Random var | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4419-6827-2 | | isbn_softcover | 978-1-4614-2623-3 | | isbn_ebook | 978-1-4419-6827-2Series ISSN 1431-875X Series E-ISSN 2197-4136 | | issn_series | 1431-875X | | copyright | Springer Science+Business Media, LLC 2010 |
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