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100   $a20220404d2022      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aLarge Sample Techniques for Statistics /$fby Jiming Jiang
205   $a2nd ed. 2022.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022.
215   $a1 online resource (689 pages)
225 1 $aSpringer Texts in Statistics,$x2197-4136
311 08$aPrint version: Jiang, Jiming Large Sample Techniques for Statistics Cham : Springer International Publishing AG,c2022 9783030916947 
320   $aIncludes bibliographical references and index.
327   $aChapter 1. The -? Arguments -- Chapter 2. Modes of Convergence -- Chapter 3. Big O, Small o, and the Unspecified c -- Chapter 4. Asymptotic Expansions -- Chapter 5. Inequalities -- Chapter 6. Sums of Independent Random Variables -- Chapter 7. Empirical Processes -- Chapter 8. Martingales -- Chapter 9. Time and Spatial Series -- Chapter 10. Stochastic Processes -- Chapter 11. Nonparametric Statistics -- Chapter 12. Mixed Effects Models -- Chapter 13. Small-Area Estimation -- Chapter 14. Jackknife and Bootstrap -- Chapter 15. Markov-Chain Monte Carlo -- Chapter 16. Random Matrix Theory.
330   $aThis book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to usemethods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites.
410  0$aSpringer Texts in Statistics,$x2197-4136
606   $aProbabilities
606   $aStatistics
606   $aProbability Theory
606   $aStatistical Theory and Methods
606   $aMostreig (Estadística)$2thub
608   $aLlibres electrònics$2thub
615  0$aProbabilities.
615  0$aStatistics.
615 14$aProbability Theory.
615 24$aStatistical Theory and Methods.
615  7$aMostreig (Estadística)
676   $a519.52
676   $a519.2
700   $aJiang$b Jiming$0614598
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910559398903321
996   $aLarge sample techniques for statistics$91131692
997   $aUNINA