Large sample techniques for statistics:
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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer Nature
[2022]
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Springer Texts in Statistics
|
| Schlagworte: | |
| Zusammenfassung: | 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. 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. |
| Beschreibung: | xv, 685 Seiten Diagramme |
| ISBN: | 9783030916947 9783030916961 |
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| id | DE-604.BV049049071 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:21:00Z |
| indexdate | 2024-07-10T09:53:47Z |
| institution | BVB |
| isbn | 9783030916947 9783030916961 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034311471 |
| oclc_num | 1337561040 |
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| owner_facet | DE-83 |
| physical | xv, 685 Seiten Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Nature |
| record_format | marc |
| series2 | Springer Texts in Statistics |
| spelling | Jiang, Jiming Verfasser (DE-588)131582119 aut Large sample techniques for statistics Jiming Jiang Second edition Cham, Switzerland Springer Nature [2022] xv, 685 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Springer Texts in Statistics 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. 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. Datenanalyse (DE-588)4123037-1 gnd rswk-swf Stichprobe (DE-588)4057502-0 gnd rswk-swf Probabilities. Statistics . Stichprobe (DE-588)4057502-0 s Datenanalyse (DE-588)4123037-1 s DE-604 9783030916954 Erscheint auch als Online-Ausgabe 2nd ed. 2022. Cham : Springer International Publishing, 2022 1 Online-Ressource(XV, 685 p. 9 illus., 2 illus. in color.) 9783030916954 |
| spellingShingle | Jiang, Jiming Large sample techniques for statistics Datenanalyse (DE-588)4123037-1 gnd Stichprobe (DE-588)4057502-0 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4057502-0 |
| title | Large sample techniques for statistics |
| title_auth | Large sample techniques for statistics |
| title_exact_search | Large sample techniques for statistics |
| title_exact_search_txtP | Large sample techniques for statistics |
| title_full | Large sample techniques for statistics Jiming Jiang |
| title_fullStr | Large sample techniques for statistics Jiming Jiang |
| title_full_unstemmed | Large sample techniques for statistics Jiming Jiang |
| title_short | Large sample techniques for statistics |
| title_sort | large sample techniques for statistics |
| topic | Datenanalyse (DE-588)4123037-1 gnd Stichprobe (DE-588)4057502-0 gnd |
| topic_facet | Datenanalyse Stichprobe |
| work_keys_str_mv | AT jiangjiming largesampletechniquesforstatistics |