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Titlebook: Large Sample Techniques for Statistics; Jiming Jiang Textbook 2022Latest edition The Editor(s) (if applicable) and The Author(s), under ex

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發(fā)表于 2025-3-21 19:58:20 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Large Sample Techniques for Statistics
編輯Jiming Jiang
視頻videohttp://file.papertrans.cn/582/581349/581349.mp4
概述Focuses on analytical skills as well as applying formulae.Provides motivations and intuition so that readers can apply concepts.Second Edition implements challenges of contemporary data science
叢書名稱Springer Texts in Statistics
圖書封面Titlebook: Large Sample Techniques for Statistics;  Jiming Jiang Textbook 2022Latest edition The Editor(s) (if applicable) and The Author(s), under ex
描述This book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, .Large Sample Techniques for Statistics.?begins with fundamental techniques, and connects theory and applications in engaging ways..The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models.?.The book‘s case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics.
出版日期Textbook 2022Latest edition
關(guān)鍵詞Approximations; Asymptotic Theory; Large Sample Theory; Limit Theorems; Parametric statistics; Random var
版次2
doihttps://doi.org/10.1007/978-3-030-91695-4
isbn_softcover978-3-030-91697-8
isbn_ebook978-3-030-91695-4Series ISSN 1431-875X Series E-ISSN 2197-4136
issn_series 1431-875X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Big ,, Small ,, and the Unspecified ,,ce quantities by equivalents that are of simpler form. For example, recall Example 2 in the Preface. Here, the problem is to estimate the mean of a random variable. In the first case, the mean can be expressed as ., where .., …, .. are i.i.d. observations with E(..)?=?.?≠?0. In this case, as mention
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Asymptotic Expansions,on. The most well-known asymptotic expansion is the Taylor expansion, which is a mathematical tool, rather than a statistical method. However, the method is used so extensively in both theoretical and applied statistics that its role in statistics can hardly be overstated. Several other expansions,
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Empirical Processes,stead of a random variable. A closer look reveals that the random function was constructed based on sum of i.i.d. random variables and equal to the latter at particular values of its variable. Since, in practice, random variables often represent observations, we call a function constructed from obse
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Time and Spatial Series,y rainfall total of a certain region, and the CD4+ cell count over time of an individual infected with the HIV virus may all be viewed as time series. A time series is usually denoted by .., .?∈?., where . is a set of times, or .(.), .?∈?., although in this book the latter notation is reserved for (
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