Isbn: 9783030916947 - large sample techniques for statistics (19 risultati)

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  • Lingua: Inglese

    Editore: Springer, 2022

    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Editore: Springer Nature Switzerland AG, CH, 2022

    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Hardback. Condizione: New. Second Edition 2022. 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.

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    Editore: Springer, 2022

    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Hardcover. Condizione: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Lingua: Inglese

    Editore: Springer, 2022

    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Condizione: New. 2nd ed. 2022 edition NO-PA16APR2015-KAP.

  • Lingua: Inglese

    Editore: Springer Nature Switzerland AG, CH, 2022

    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Hardback. Condizione: New. Second Edition 2022. 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.

  • Lingua: Inglese

    Editore: Springer, 2022

    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 Statisticsbegins 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.

  • Lingua: Inglese

    Editore: Springer Nature Switzerland AG, Cham, 2022

    3030916944 / 9783030916947

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    Hardcover. Condizione: new. Hardcover. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

  • Lingua: Inglese

    Editore: Springer Nature, 2022

    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Hardcover. Condizione: Brand New. 2nd edition. 700 pages. 9.25x6.10x1.54 inches. In Stock. This item is printed on demand.

  • Lingua: Inglese

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    3030916944 / 9783030916947

    Serie: Libro 103 di 111 - Springer Texts in Statistics

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    Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 Statisticsbegins 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. 704 pp. Englisch.

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    Editore: Springer International Publishing, 2022

    3030916944 / 9783030916947

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    Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Focuses on analytical skills as well as applying formulaeProvides motivations and intuition so that readers can apply conceptsSecond Edition implements challenges of contemporary data scienceJiming Jiang is Professor of Stati.

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  • Lingua: Inglese

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    Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 704 pp. Englisch.