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A modern introduction to probability and statistics / F. M. Dekking

Public ISBD Titre : A modern introduction to probability and statistics : understanding why and how Type de document : texte imprimé Auteurs : F. M. Dekking, Auteur ; C. Kraaikamp, Auteur ; H. P. Lopuhaa, Auteur Editeur : New York : Springer-Verlag Année de publication : 2005 Collection : Springer Texts in Statistics Importance : 487 p. Présentation : couv. ill. en coul. Format : 24 cm. ISBN/ISSN/EAN : 978-1-85233-896-2 Langues : Anglais (eng) Catégories : MATHEMATIQUES Index. décimale : 04-05 Probabilité et statistique Résumé : Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study. A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap. This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects. Note de contenu : Contents 1 Why Probability and Statistics? 2 Outcomes, Events and Probability 3 Conditional Probability and Independence 4 Discrete Random Variables 5 Continuous Random Variables 6 Simulation 7 Expectation and Variance 8 Computations with Random Variables 9 Joint Distributions and Independence 10 Covariance and Correlation 11 More Computations with More Random Variables 12 The Poisson Process 13 The Law of Large Numbers 14 The Central Limit Theorem 15 Exploratory Data Analysis: Graphical Summaries 16 Exploratory Data Analysis: Numerical Summaries 17 Basic Statistical Models 18 The Bootstrap 19 Unbiased Estimators 20 Efficiency and Mean Squared Error 21 Maximum Likelihood 22 The Method of Least Squares 23 Confidence Intervals for the Mean 24 More on Confidence Intervals 25 Testing Hypotheses: Essentials 26 Testing Hypotheses: Elaboration 27 The t-test 28 Comparing Two Samples A: Summary of distributions B: Tables of the normal and t-distributions C: Answers to selected exercices D: Full solutions to selected exercises -Index A modern introduction to probability and statistics : understanding why and how [texte imprimé] / F. M. Dekking, Auteur ; C. Kraaikamp, Auteur ; H. P. Lopuhaa, Auteur . - New York : Springer-Verlag, 2005 . - 487 p. : couv. ill. en coul. ; 24 cm.. - (Springer Texts in Statistics) . ISSN : 978-1-85233-896-2 Langues : Anglais (eng) Catégories : MATHEMATIQUES Index. décimale : 04-05 Probabilité et statistique Résumé : Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study. A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap. This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects. Note de contenu : Contents 1 Why Probability and Statistics? 2 Outcomes, Events and Probability 3 Conditional Probability and Independence 4 Discrete Random Variables 5 Continuous Random Variables 6 Simulation 7 Expectation and Variance 8 Computations with Random Variables 9 Joint Distributions and Independence 10 Covariance and Correlation 11 More Computations with More Random Variables 12 The Poisson Process 13 The Law of Large Numbers 14 The Central Limit Theorem 15 Exploratory Data Analysis: Graphical Summaries 16 Exploratory Data Analysis: Numerical Summaries 17 Basic Statistical Models 18 The Bootstrap 19 Unbiased Estimators 20 Efficiency and Mean Squared Error 21 Maximum Likelihood 22 The Method of Least Squares 23 Confidence Intervals for the Mean 24 More on Confidence Intervals 25 Testing Hypotheses: Essentials 26 Testing Hypotheses: Elaboration 27 The t-test 28 Comparing Two Samples A: Summary of distributions B: Tables of the normal and t-distributions C: Answers to selected exercices D: Full solutions to selected exercises -Index

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2659	04-05-07	Livre	Bibliothèque de Génie Electrique- USTO	Documentaires	Exclu du prêt	2659
2660	04-05-07	Livre	Bibliothèque de Génie Electrique- USTO	Documentaires	Exclu du prêt	2660

Probability for Statisticians / Galen R. Shorack

Public ISBD Titre : Probability for Statisticians Type de document : texte imprimé Auteurs : Galen R. Shorack, Auteur Editeur : New York : Springer-Verlag Année de publication : 2000 Collection : Springer Texts in Statistics Importance : 585 p. Présentation : couv. ill. en coul., ill. Format : 26 cm. ISBN/ISSN/EAN : 978-0-387-98953-2 Langues : Anglais (eng) Catégories : AUTOMATISME Mots-clés : Brownian motion Law of large numbers Martingale mathematical statistics statistics Index. décimale : 25-02 Théorie et traitement du signal Résumé : Probability for Statisticians is intended as a text for a one year graduate course aimed especially at students in statistics. The choice of examples illustrates this intention clearly. The material to be presented in the classroom constitutes a bit more than half the text, and the choices the author makes at the University of Washington in Seattle are spelled out. The rest of the text provides background, offers different routes that could be pursued in the classroom, ad offers additional material that is appropriate for self-study. Of particular interest is a presentation of the major central limit theorems via Stein's method either prior to or alternative to a characteristic funcion presentation. Additionally, there is considerable emphasis placed on the quantile function as well as the distribution function. The bootstrap and trimming are both presented. The martingale coverage includes coverage of censored data martingales. The text includes measure theoretic preliminaries, from which the authors own course typically includes selected coverage. The author is a professor of Statistics and adjunct professor of Mathematics at the University of Washington in Seattle. He served as chair of the Department of Statistics 1986-- 1989. He received his PhD in Statistics from Stanford University. He is a fellow of the Institute of Mathematical Statistics, and is a former associate editor of the Annals of Statistics. Note de contenu : Contents Chapter 1 Measures Chapter 2 Measurable Functions and Convergence Chapter 3 Integration Chapter 4 Derivatives via Signed Measures Chapter 5 Measures and Processes on Products Chapter 6 General Topology and Hilbert Space Chapter 7 Distribution and Quantile Functions Chapter 8 Independence and Conditional Distributions Chapter 9 Special Distributions Chapter 10 WILL, SLLN, LIL, and Series Chapter 11 Convergence in Distribution Chapter 12 Brownian Motion and Empirical Processes Chapter 13 Characteristic Functions Chapter 14 CLTs via Characteristic Functions Chapter 15 Infinitely Divisible and Stable Distributions Chapter 16 Asymptotics via Empirical Proceses Chapter 17 Asymptotics via Stein’s Approach Chapter 18 Martingales Chapter 19 Convergence in Law on Metric Spaces Appendix Index Probability for Statisticians [texte imprimé] / Galen R. Shorack, Auteur . - New York : Springer-Verlag, 2000 . - 585 p. : couv. ill. en coul., ill. ; 26 cm.. - (Springer Texts in Statistics) . ISBN : 978-0-387-98953-2 Langues : Anglais (eng) Catégories : AUTOMATISME Mots-clés : Brownian motion Law of large numbers Martingale mathematical statistics statistics Index. décimale : 25-02 Théorie et traitement du signal Résumé : Probability for Statisticians is intended as a text for a one year graduate course aimed especially at students in statistics. The choice of examples illustrates this intention clearly. The material to be presented in the classroom constitutes a bit more than half the text, and the choices the author makes at the University of Washington in Seattle are spelled out. The rest of the text provides background, offers different routes that could be pursued in the classroom, ad offers additional material that is appropriate for self-study. Of particular interest is a presentation of the major central limit theorems via Stein's method either prior to or alternative to a characteristic funcion presentation. Additionally, there is considerable emphasis placed on the quantile function as well as the distribution function. The bootstrap and trimming are both presented. The martingale coverage includes coverage of censored data martingales. The text includes measure theoretic preliminaries, from which the authors own course typically includes selected coverage. The author is a professor of Statistics and adjunct professor of Mathematics at the University of Washington in Seattle. He served as chair of the Department of Statistics 1986-- 1989. He received his PhD in Statistics from Stanford University. He is a fellow of the Institute of Mathematical Statistics, and is a former associate editor of the Annals of Statistics. Note de contenu : Contents Chapter 1 Measures Chapter 2 Measurable Functions and Convergence Chapter 3 Integration Chapter 4 Derivatives via Signed Measures Chapter 5 Measures and Processes on Products Chapter 6 General Topology and Hilbert Space Chapter 7 Distribution and Quantile Functions Chapter 8 Independence and Conditional Distributions Chapter 9 Special Distributions Chapter 10 WILL, SLLN, LIL, and Series Chapter 11 Convergence in Distribution Chapter 12 Brownian Motion and Empirical Processes Chapter 13 Characteristic Functions Chapter 14 CLTs via Characteristic Functions Chapter 15 Infinitely Divisible and Stable Distributions Chapter 16 Asymptotics via Empirical Proceses Chapter 17 Asymptotics via Stein’s Approach Chapter 18 Martingales Chapter 19 Convergence in Law on Metric Spaces Appendix Index

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2646	25-02-61	Livre	Bibliothèque de Génie Electrique- USTO	Documentaires	Exclu du prêt	2646
2647	25-02-61	Livre	Bibliothèque de Génie Electrique- USTO	Documentaires	Exclu du prêt	2647