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An Introduction to Order Statistics / Mohammad Ahsanullah / Amsterdam ; Pékin ; Paris : Atlantis Press (2013)

Titre : An Introduction to Order Statistics Type de document : document électronique Auteurs : Mohammad Ahsanullah ; SpringerLink (Online service) ; Valery B. Nevzorov ; Shakil, Mohammad Editeur : Amsterdam ; Pékin ; Paris : Atlantis Press Année de publication : 2013 Collection : Atlantis Studies in Probability and Statistics, ISSN 1879-6893 num. 3 Importance : X, 244 p Présentation : online resource ISBN/ISSN/EAN : 978-94-91216-83-1 Langues : Anglais ( eng)Descripteurs : Biostatistique La biostatistique (appelée aussi biométrie) est l'application des statistiques à un large éventail de sujets en biologie. Cela englobe la conception des expériences biologiques (dans la médecine et l'agriculture), la collecte des informations, la compilation et analyse des données chiffrées de ces expériences, l'interprétation des résultats en vue d'avancer une conclusion., ÉchantillonnageTags : Statistics Statistical methods Mathematical statistics Economics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences Biostatistics Résumé : This book presents the theory of order statistics in a way, such that beginners can get easily acquainted with the very basis of the theory without having to work through heavily involved techniques. At the same time more experienced readers can check their level of understanding and polish their knowledge with certain details. This is achieved by, on the one hand, stating the basic formulae and providing many useful examples to illustrate the theoretical statements, while on the other hand an upgraded list of references will make it easier to gain insight into more specialized results. Thus this book is suitable for a readership working in statistics, actuarial mathematics, reliability engineering, meteorology, hydrology, business economics, sports analysis and many more Note de contenu : Basic definitions -- Distributions of order statistics -- Sample quantiles and ranges -- Representations for order statistics -- Conditional distributions of order statistics.-Order statistics for discrete distributions -- Moments of order statistics: general relations -- Moments of uniform and exponential order statistics -- Moment relations for order statistics: normal distribution -- Asymptotic behavior of the middle and intermediate order statistics -- Asymptotic behavior of the extreme order statistics -- Some properties of estimators based on order statistics -- Minimum variance linear unbiased estimators -- Minimum variance linear unbiased estimators and predictors based on censored samples -- Estimation of parameters based on fixed number of sample quantiles -- Order statistics from extended samples -- Order statistics and record values -- Characterizations of distributions based on properties of order statistics -- Order statistics and record values based on F? distributions -- Generalized order statistics -- Compliments and problems Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=108437 An Introduction to Order Statistics [document électronique] / Mohammad Ahsanullah ; SpringerLink (Online service) ; Valery B. Nevzorov ; Shakil, Mohammad . - Amsterdam ; Pékin ; Paris : Atlantis Press, 2013 . - X, 244 p : online resource. - (Atlantis Studies in Probability and Statistics, ISSN 1879-6893; 3) .ISBN: 978-94-91216-83-1

Langues : Anglais (eng)

Descripteurs : Biostatistique La biostatistique (appelée aussi biométrie) est l'application des statistiques à un large éventail de sujets en biologie. Cela englobe la conception des expériences biologiques (dans la médecine et l'agriculture), la collecte des informations, la compilation et analyse des données chiffrées de ces expériences, l'interprétation des résultats en vue d'avancer une conclusion., ÉchantillonnageTags : Statistics Statistical methods Mathematical statistics Economics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences Biostatistics Résumé : This book presents the theory of order statistics in a way, such that beginners can get easily acquainted with the very basis of the theory without having to work through heavily involved techniques. At the same time more experienced readers can check their level of understanding and polish their knowledge with certain details. This is achieved by, on the one hand, stating the basic formulae and providing many useful examples to illustrate the theoretical statements, while on the other hand an upgraded list of references will make it easier to gain insight into more specialized results. Thus this book is suitable for a readership working in statistics, actuarial mathematics, reliability engineering, meteorology, hydrology, business economics, sports analysis and many more Note de contenu : Basic definitions -- Distributions of order statistics -- Sample quantiles and ranges -- Representations for order statistics -- Conditional distributions of order statistics.-Order statistics for discrete distributions -- Moments of order statistics: general relations -- Moments of uniform and exponential order statistics -- Moment relations for order statistics: normal distribution -- Asymptotic behavior of the middle and intermediate order statistics -- Asymptotic behavior of the extreme order statistics -- Some properties of estimators based on order statistics -- Minimum variance linear unbiased estimators -- Minimum variance linear unbiased estimators and predictors based on censored samples -- Estimation of parameters based on fixed number of sample quantiles -- Order statistics from extended samples -- Order statistics and record values -- Characterizations of distributions based on properties of order statistics -- Order statistics and record values based on F? distributions -- Generalized order statistics -- Compliments and problems Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=108437 Topics from Australian Conferences on Teaching Statistics / MacGillivray, Helen / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2014)

Titre : Topics from Australian Conferences on Teaching Statistics : OZCOTS 2008-2012 Type de document : document électronique Auteurs : MacGillivray, Helen ; Phillips, Brian ; SpringerLink (Online service) ; Michael A. Martin Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2014 Collection : Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009 num. 81 Importance : XXIII, 418 p. 74 illus., 46 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-1-4939-0603-1 Langues : Anglais ( eng)Descripteurs : Échantillonnage Tags : Statistics Mathematical statistics Statistics for Social Science Behavorial Science Education Public Policy and Law Statistical Theory and Methods Résumé : The first OZCOTS conference in 1998 was inspired by papers contributed by Australians to the 5th International Conference on Teaching Statistics. In 2008, as part of the program of one of the first National Senior Teaching Fellowships, the 6th OZCOTS was held in conjunction with the Australian Statistical Conference, with Fellowship keynotes and contributed papers, optional refereeing and proceedings. This venture was so successful that the 7th and 8th OZCOTS were similarly run, conjoined with Australian Statistical Conferences in 2010 and 2012. Authors of papers from these OZCOTS conferences were invited to develop chapters for refereeing and inclusion in this volume. There are sections on keynote topics, undergraduate curriculum and learning, professional development, postgraduate learning, and papers from OZCOTS 2012. Because OZCOTS aim to unite statisticians and statistics educators, the approaches this volume takes are immediately relevant to all who have a vested interest in good teaching practices. Globally, statistics as a discipline, statistical pedagogy and statistics in academia and industry are all critically important to the modern information society. This volume addresses these roles within the wider society as well as questions that are specific to the discipline itself. Other chapters share research on learning and teaching statistics in interdisciplinary work and student preparation for futures in academia, government and industry. Note de contenu : Interacting with data, concepts and models: illustrations from the rpanel package for R -- An elephant never forgets ? effective analogies for teaching statistical modelling -- Experience early, logic later -- Transforming statistics education in South Africa -- Beyond the statistical fringe -- The development of a first course in statistical literacy for undergraduates -- Spreadsheets and Simulation for teaching a range of statistical concepts -- Navigating in a new pedagogical landscape with an introductory course in applied statistics -- The Golden Arches: an approach to teaching statistics in a first year university service course -- How do students learn statistical packages? A qualitative study -- A comparison of first year statistics units? content and contexts in a multinational study, with a case study for the validation of assist in Australia -- Understanding the quantitative skill base of business students for statistics -- Square PEGs in round holes: academics teaching statistics in industry -- Raising the capability of producers and users of official statistics -- Education for a workplace statistician -- Improving teachers' professional statistical literacy -- Statistical training in the workplace -- Engaging research students in online statistics courses -- Engaging entry level researchers in agriculture in statistical communication and collaboration: why? and how? -- Researchers? use of statistics in creative and qualitative disciplines -- Evaluation of the learning objects in a largely online postgraduate teaching program: the effects of learning style -- Problem-based learning of statistical sampling concepts using fantasy sports team data -- BizStats: a data and story library for business statistics -- Statistics training for multiple audiences Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=110492 Topics from Australian Conferences on Teaching Statistics : OZCOTS 2008-2012 [document électronique] / MacGillivray, Helen ; Phillips, Brian ; SpringerLink (Online service) ; Michael A. Martin . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2014 . - XXIII, 418 p. 74 illus., 46 illus. in color : online resource. - (Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009; 81) .ISBN: 978-1-4939-0603-1

Langues : Anglais (eng)

Descripteurs : Échantillonnage Tags : Statistics Mathematical statistics Statistics for Social Science Behavorial Science Education Public Policy and Law Statistical Theory and Methods Résumé : The first OZCOTS conference in 1998 was inspired by papers contributed by Australians to the 5th International Conference on Teaching Statistics. In 2008, as part of the program of one of the first National Senior Teaching Fellowships, the 6th OZCOTS was held in conjunction with the Australian Statistical Conference, with Fellowship keynotes and contributed papers, optional refereeing and proceedings. This venture was so successful that the 7th and 8th OZCOTS were similarly run, conjoined with Australian Statistical Conferences in 2010 and 2012. Authors of papers from these OZCOTS conferences were invited to develop chapters for refereeing and inclusion in this volume. There are sections on keynote topics, undergraduate curriculum and learning, professional development, postgraduate learning, and papers from OZCOTS 2012. Because OZCOTS aim to unite statisticians and statistics educators, the approaches this volume takes are immediately relevant to all who have a vested interest in good teaching practices. Globally, statistics as a discipline, statistical pedagogy and statistics in academia and industry are all critically important to the modern information society. This volume addresses these roles within the wider society as well as questions that are specific to the discipline itself. Other chapters share research on learning and teaching statistics in interdisciplinary work and student preparation for futures in academia, government and industry. Note de contenu : Interacting with data, concepts and models: illustrations from the rpanel package for R -- An elephant never forgets ? effective analogies for teaching statistical modelling -- Experience early, logic later -- Transforming statistics education in South Africa -- Beyond the statistical fringe -- The development of a first course in statistical literacy for undergraduates -- Spreadsheets and Simulation for teaching a range of statistical concepts -- Navigating in a new pedagogical landscape with an introductory course in applied statistics -- The Golden Arches: an approach to teaching statistics in a first year university service course -- How do students learn statistical packages? A qualitative study -- A comparison of first year statistics units? content and contexts in a multinational study, with a case study for the validation of assist in Australia -- Understanding the quantitative skill base of business students for statistics -- Square PEGs in round holes: academics teaching statistics in industry -- Raising the capability of producers and users of official statistics -- Education for a workplace statistician -- Improving teachers' professional statistical literacy -- Statistical training in the workplace -- Engaging research students in online statistics courses -- Engaging entry level researchers in agriculture in statistical communication and collaboration: why? and how? -- Researchers? use of statistics in creative and qualitative disciplines -- Evaluation of the learning objects in a largely online postgraduate teaching program: the effects of learning style -- Problem-based learning of statistical sampling concepts using fantasy sports team data -- BizStats: a data and story library for business statistics -- Statistics training for multiple audiences Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=110492 A Course in Mathematical Statistics and Large Sample Theory / Rabindra Nath Bhattacharya / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2016)

Titre : A Course in Mathematical Statistics and Large Sample Theory Type de document : document électronique Auteurs : Rabindra Nath Bhattacharya (1937-....) ; Lizhen Lin ; SpringerLink (Online service) ; Victor Patrangenaru Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2016 Collection : Springer Texts in Statistics, ISSN 1431-875X Importance : XI, 389 p. 9 illus., 2 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-1-4939-4032-5 Langues : Anglais ( eng)Descripteurs : Bibliographie Ensai 2e Année Tags : Statistics Mathematical statistics Biostatistics Probabilities Statistical Theory and Methods Probability and Statistics in Computer Science Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Statistics and Computing/Statistics Programs Résumé : This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics. Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics ? parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods. Large Sample theory with many worked examples, numerical calculations, and simulations to illustrate theory Appendices provide ready access to a number of standard results, with many proofs Solutions given to a number of selected exercises from Part I Part II exercises with a certain level of difficulty appear with detailed hints Rabi Bhattacharya, PhD,has held regular faculty positions at UC, Berkeley; Indiana University; and the University of Arizona. He is a Fellow of the Institute of Mathematical Statistics and a recipient of the U.S. Senior Scientist Humboldt Award and of a Guggenheim Fellowship. He has served on editorial boards of many international journals and has published several research monographs and graduate texts on probability and statistics, including Nonparametric Inference on Manifolds, co-authored with A. Bhattacharya. Lizhen Lin, PhD, is Assistant Professor in the Department of Statistics and Data Science at the University of Texas at Austin. She received a PhD in Mathematics from the University of Arizona and was a Postdoctoral Associate at Duke University. Bayesian nonparametrics, shape constrained inference, and nonparametric inference on manifolds are among her areas of expertise. Vic Patrangenaru, PhD, is Professor of Statistics at Florida State University. He received PhDs in Mathematics from Haifa, Israel, and from Indiana University in the fields of differential geometry and statistics, respectively. He has many research publications on Riemannian geometry and especially on statistics on manifolds. He is a co-author with L. Ellingson of Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis. Note de contenu : 1 Introduction -- 2 Decision Theory -- 3 Introduction to General Methods of Estimation -- 4 Sufficient Statistics, Exponential Families, and Estimation -- 5 Testing Hypotheses -- 6 Consistency and Asymptotic Distributions and Statistics -- 7 Large Sample Theory of Estimation in Parametric Models -- 8 Tests in Parametric and Nonparametric Models -- 9 The Nonparametric Bootstrap -- 10 Nonparametric Curve Estimation -- 11 Edgeworth Expansions and the Bootstrap -- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces -- 13 Multiple Testing and the False Discovery Rate -- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory -- 15 Miscellaneous Topics -- Appendices -- Solutions of Selected Exercises in Part 1 Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=123067 A Course in Mathematical Statistics and Large Sample Theory [document électronique] / Rabindra Nath Bhattacharya (1937-....) ; Lizhen Lin ; SpringerLink (Online service) ; Victor Patrangenaru . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2016 . - XI, 389 p. 9 illus., 2 illus. in color : online resource. - (Springer Texts in Statistics, ISSN 1431-875X) .ISBN: 978-1-4939-4032-5

Langues : Anglais (eng)

Descripteurs : Bibliographie Ensai 2e Année Tags : Statistics Mathematical statistics Biostatistics Probabilities Statistical Theory and Methods Probability and Statistics in Computer Science Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Statistics and Computing/Statistics Programs Résumé : This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics. Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics ? parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods. Large Sample theory with many worked examples, numerical calculations, and simulations to illustrate theory Appendices provide ready access to a number of standard results, with many proofs Solutions given to a number of selected exercises from Part I Part II exercises with a certain level of difficulty appear with detailed hints Rabi Bhattacharya, PhD,has held regular faculty positions at UC, Berkeley; Indiana University; and the University of Arizona. He is a Fellow of the Institute of Mathematical Statistics and a recipient of the U.S. Senior Scientist Humboldt Award and of a Guggenheim Fellowship. He has served on editorial boards of many international journals and has published several research monographs and graduate texts on probability and statistics, including Nonparametric Inference on Manifolds, co-authored with A. Bhattacharya. Lizhen Lin, PhD, is Assistant Professor in the Department of Statistics and Data Science at the University of Texas at Austin. She received a PhD in Mathematics from the University of Arizona and was a Postdoctoral Associate at Duke University. Bayesian nonparametrics, shape constrained inference, and nonparametric inference on manifolds are among her areas of expertise. Vic Patrangenaru, PhD, is Professor of Statistics at Florida State University. He received PhDs in Mathematics from Haifa, Israel, and from Indiana University in the fields of differential geometry and statistics, respectively. He has many research publications on Riemannian geometry and especially on statistics on manifolds. He is a co-author with L. Ellingson of Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis. Note de contenu : 1 Introduction -- 2 Decision Theory -- 3 Introduction to General Methods of Estimation -- 4 Sufficient Statistics, Exponential Families, and Estimation -- 5 Testing Hypotheses -- 6 Consistency and Asymptotic Distributions and Statistics -- 7 Large Sample Theory of Estimation in Parametric Models -- 8 Tests in Parametric and Nonparametric Models -- 9 The Nonparametric Bootstrap -- 10 Nonparametric Curve Estimation -- 11 Edgeworth Expansions and the Bootstrap -- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces -- 13 Multiple Testing and the False Discovery Rate -- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory -- 15 Miscellaneous Topics -- Appendices -- Solutions of Selected Exercises in Part 1 Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=123067 Statistics and Data Analysis for Financial Engineering / David Ruppert / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2015)

Titre : Statistics and Data Analysis for Financial Engineering : with R examples Type de document : document électronique Auteurs : David Ruppert ; Matteson, David S ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2015 Collection : Springer Texts in Statistics, ISSN 1431-875X Importance : XXVI, 719 p. 221 illus., 108 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-1-4939-2614-5 Langues : Anglais ( eng)Descripteurs : Échantillonnage , Rééchantillonnage Technique d'inférence statistique basée sur une succession de rééchantillonnagesTags : Statistics Finance Mathematical statistics Economics Statistics for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Statistical Theory and Methods Finance/Investment/Banking Résumé : The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, multivariate volatility and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science at Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Journal of the American Statistical Association-Theory and Methods and former Editor of the Electronic Journal of Statistics and of the Institute of Mathematical Statistics's Lecture Notes?Monographs. Professor Ruppert has published over 125 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction. David S. Matteson is Assistant Professor of Statistical Science at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering. Professor Matteson received his PhD in Statistics at the University of Chicago. He received a CAREER Award from the National Science Foundation and won Best Academic Paper Awards from the annual R/Finance conference. He is an Associate Editor of the Journal of the American Statistical Association-Theory and Methods, Biometrics, and Statistica Sinica. He is also an Officer for the Business and Economic Statistics Section of the American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society Note de contenu : Introduction -- Returns -- Fixed income securities -- Exploratory data analysis -- Modeling univariate distributions -- Resampling -- Multivariate statistical models -- Copulas -- Time series models: basics -- Time series models: further topics -- Portfolio theory -- Regression: basics -- Regression: troubleshooting -- Regression: advanced topics -- Cointegration -- The capital asset pricing model -- Factor models and principal components -- GARCH models -- Risk management -- Bayesian data analysis and MCMC -- Nonparametric regression and splines Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115871 Statistics and Data Analysis for Financial Engineering : with R examples [document électronique] / David Ruppert ; Matteson, David S ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2015 . - XXVI, 719 p. 221 illus., 108 illus. in color : online resource. - (Springer Texts in Statistics, ISSN 1431-875X) .ISBN: 978-1-4939-2614-5

Langues : Anglais (eng)

Descripteurs : Échantillonnage , Rééchantillonnage Technique d'inférence statistique basée sur une succession de rééchantillonnagesTags : Statistics Finance Mathematical statistics Economics Statistics for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Statistical Theory and Methods Finance/Investment/Banking Résumé : The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, multivariate volatility and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science at Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Journal of the American Statistical Association-Theory and Methods and former Editor of the Electronic Journal of Statistics and of the Institute of Mathematical Statistics's Lecture Notes?Monographs. Professor Ruppert has published over 125 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction. David S. Matteson is Assistant Professor of Statistical Science at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering. Professor Matteson received his PhD in Statistics at the University of Chicago. He received a CAREER Award from the National Science Foundation and won Best Academic Paper Awards from the annual R/Finance conference. He is an Associate Editor of the Journal of the American Statistical Association-Theory and Methods, Biometrics, and Statistica Sinica. He is also an Officer for the Business and Economic Statistics Section of the American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society Note de contenu : Introduction -- Returns -- Fixed income securities -- Exploratory data analysis -- Modeling univariate distributions -- Resampling -- Multivariate statistical models -- Copulas -- Time series models: basics -- Time series models: further topics -- Portfolio theory -- Regression: basics -- Regression: troubleshooting -- Regression: advanced topics -- Cointegration -- The capital asset pricing model -- Factor models and principal components -- GARCH models -- Risk management -- Bayesian data analysis and MCMC -- Nonparametric regression and splines Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115871 Relations, Bounds and Approximations for Order Statistics / Barry C. Arnold / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (1989)

Titre : Relations, Bounds and Approximations for Order Statistics Type de document : document électronique Auteurs : Barry C. Arnold ; Narayanaswamy Balakrishnan (1956-....) ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 1989 Collection : Lecture Notes in Statistics - LNS, ISSN 0930-0325 num. 53 Importance : IX, 173p Présentation : online resource ISBN/ISSN/EAN : 978-1-4612-3644-3 Langues : Anglais ( eng)Descripteurs : Échantillonnage Tags : Statistics Résumé : Bounds on moments of order statistics have been of interest since Sir Francis Galton (1902) flrst addressed the problem of fairly dividing flrst and second prize money in a competition. The present compendium of results represents our effort to sort the plethora of results into some semblance of order. We have tried to assign priority for results appropriately. We will cheerfully accept corrections. Omissions of interesting results have inevitably occurred. On this too we await (cheerful) corrections. We are grateful to Peggy Franklin (University of California), Janet Leach, Domenica Calabria and Patsy Chan (McMaster University) who shared the responsibility of typing the manuscript. The flnal form of the manuscript owes much to their skill and patience. Barry C. Arnold Riverside, California U. S. A. N. Balakrishnan Hamilton, Ontario Canada November, 1988 Table of Contents Chapter 1: TIlE DISTRIBUTION OF ORDER STATISTICS Exercises 4 Chapter 2: RECURRENCE RELATIONS AND IDENTITIES FOR ORDER STATISTICS 2. 0. Introduction 5 2. 1. Relations for single moments 6 2. 2. Relations for product moments 9 2. 3. Relations for covariances 13 15 2. 4. Results for symmetric populations 2. 5. Results for normal population 17 20 2. 6. Results for two related populations 2. 7. Results for exchangeable variates 23 25 Exercises Chapter 3: BOUNDS ON EXPECTATIONS OF ORDER STATISTICS 3. 0. Introduction 38 3. 1. Universal bounds in the Li. d. case 38 3. 2. Variations on the Samuelson-Scott theme 43 3. 3 Note de contenu : 1: The Distribution of Order Statistics -- Exercises -- 2: Recurrence Relations and Identities for Order Statistics -- 2.0. Introduction -- 2.1. Relations for single moments -- 2.2. Relations for product moments -- 2.3. Relations for covariances -- 2.4. Results for symmetric populations -- 2.5. Results for normal population -- 2.6. Results for two related populations -- 2.7. Results for exchangeable variates -- Exercises -- 3: Bounds on Expectations of Order Statistics -- 3.0. Introduction -- 3.1. Universal bounds in the i.i.d. case -- 3.2. Variations on the Samuelson-Scott theme -- 3.3. Bounds via maximal dependence -- 3.4. Restricted families of parent distributions -- Exercises -- 4: Approximations to Moments of Order Statistics -- 4.0. Introduction -- 4.1. Uniform order statistics and moments -- 4.2. David and Johnson?s approximation -- 4.3. Clark and Williams? approximation -- 4.4. Plackett?s approximation -- 4.5. Saw?s error analysis -- 4.6. Sugiura?s orthogonal inverse expansion -- 4.7. Joshi?s modified bounds and approximations -- 4.8. Joshi and Balakrishnan?s improved bounds for extremes -- Exercises -- 5: Order Statistics From a Sample Containing a Single Outlier -- 5.0. Introduction -- 5.1. Distributions of order statistics -- 5.2. Relations for single moments -- 5.3. Relations for product moments -- 5.4. Relations for covariances -- 5.5. Results for symmetric outlier model -- 5.6 Results for two related outlier models -- 5.7. Functional behaviour of order statistics -- 5.8. Applications in robustness studies -- Exercises -- 6: Record Values -- 6.0. Introduction -- 6.1. Record values -- 6.2. Bounds on mean record values -- 6.3. Record values in dependent sequences -- Exercises -- References -- Author Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115644 Relations, Bounds and Approximations for Order Statistics [document électronique] / Barry C. Arnold ; Narayanaswamy Balakrishnan (1956-....) ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 1989 . - IX, 173p : online resource. - (Lecture Notes in Statistics - LNS, ISSN 0930-0325; 53) .ISBN: 978-1-4612-3644-3

Langues : Anglais (eng)

Descripteurs : Échantillonnage Tags : Statistics Résumé : Bounds on moments of order statistics have been of interest since Sir Francis Galton (1902) flrst addressed the problem of fairly dividing flrst and second prize money in a competition. The present compendium of results represents our effort to sort the plethora of results into some semblance of order. We have tried to assign priority for results appropriately. We will cheerfully accept corrections. Omissions of interesting results have inevitably occurred. On this too we await (cheerful) corrections. We are grateful to Peggy Franklin (University of California), Janet Leach, Domenica Calabria and Patsy Chan (McMaster University) who shared the responsibility of typing the manuscript. The flnal form of the manuscript owes much to their skill and patience. Barry C. Arnold Riverside, California U. S. A. N. Balakrishnan Hamilton, Ontario Canada November, 1988 Table of Contents Chapter 1: TIlE DISTRIBUTION OF ORDER STATISTICS Exercises 4 Chapter 2: RECURRENCE RELATIONS AND IDENTITIES FOR ORDER STATISTICS 2. 0. Introduction 5 2. 1. Relations for single moments 6 2. 2. Relations for product moments 9 2. 3. Relations for covariances 13 15 2. 4. Results for symmetric populations 2. 5. Results for normal population 17 20 2. 6. Results for two related populations 2. 7. Results for exchangeable variates 23 25 Exercises Chapter 3: BOUNDS ON EXPECTATIONS OF ORDER STATISTICS 3. 0. Introduction 38 3. 1. Universal bounds in the Li. d. case 38 3. 2. Variations on the Samuelson-Scott theme 43 3. 3 Note de contenu : 1: The Distribution of Order Statistics -- Exercises -- 2: Recurrence Relations and Identities for Order Statistics -- 2.0. Introduction -- 2.1. Relations for single moments -- 2.2. Relations for product moments -- 2.3. Relations for covariances -- 2.4. Results for symmetric populations -- 2.5. Results for normal population -- 2.6. Results for two related populations -- 2.7. Results for exchangeable variates -- Exercises -- 3: Bounds on Expectations of Order Statistics -- 3.0. Introduction -- 3.1. Universal bounds in the i.i.d. case -- 3.2. Variations on the Samuelson-Scott theme -- 3.3. Bounds via maximal dependence -- 3.4. Restricted families of parent distributions -- Exercises -- 4: Approximations to Moments of Order Statistics -- 4.0. Introduction -- 4.1. Uniform order statistics and moments -- 4.2. David and Johnson?s approximation -- 4.3. Clark and Williams? approximation -- 4.4. Plackett?s approximation -- 4.5. Saw?s error analysis -- 4.6. Sugiura?s orthogonal inverse expansion -- 4.7. Joshi?s modified bounds and approximations -- 4.8. Joshi and Balakrishnan?s improved bounds for extremes -- Exercises -- 5: Order Statistics From a Sample Containing a Single Outlier -- 5.0. Introduction -- 5.1. Distributions of order statistics -- 5.2. Relations for single moments -- 5.3. Relations for product moments -- 5.4. Relations for covariances -- 5.5. Results for symmetric outlier model -- 5.6 Results for two related outlier models -- 5.7. Functional behaviour of order statistics -- 5.8. Applications in robustness studies -- Exercises -- 6: Record Values -- 6.0. Introduction -- 6.1. Record values -- 6.2. Bounds on mean record values -- 6.3. Record values in dependent sequences -- Exercises -- References -- Author Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115644 Statistics and Finance / David Ruppert / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2004)

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