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Título : Statistical Learning from a Regression Perspective Tipo de documento: documento electrónico Autores: Richard A. Berk ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVII, 360 p Il.: online resource ISBN/ISSN/DL: 978-0-387-77501-2 Idioma : Inglés (eng) Palabras clave: Mathematics Public health Probabilities Statistics Social sciences Psychology Methodology Psychological measurement Probability Theory and Stochastic Processes for Science, Behavorial Education, Policy, Law Statistical Methods Health Methods/Evaluation of the Sciences Clasificación: 51 Matemáticas Resumen: Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R. Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of applications in the social and natural sciences Nota de contenido: Statistical Learning as a Regression Problem -- Regression Splines and Regression Smoothers -- Classification and Regression Trees (CART) -- Bagging -- Random Forests -- Boosting -- Support Vector Machines -- Broader Implications and a Bit of Craft Lore En línea: http://dx.doi.org/10.1007/978-0-387-77501-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34235 Statistical Learning from a Regression Perspective [documento electrónico] / Richard A. Berk ; SpringerLink (Online service) . - New York, NY : Springer New York, 2008 . - XVII, 360 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-77501-2
Idioma : Inglés (eng)
Palabras clave: Mathematics Public health Probabilities Statistics Social sciences Psychology Methodology Psychological measurement Probability Theory and Stochastic Processes for Science, Behavorial Education, Policy, Law Statistical Methods Health Methods/Evaluation of the Sciences Clasificación: 51 Matemáticas Resumen: Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R. Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of applications in the social and natural sciences Nota de contenido: Statistical Learning as a Regression Problem -- Regression Splines and Regression Smoothers -- Classification and Regression Trees (CART) -- Bagging -- Random Forests -- Boosting -- Support Vector Machines -- Broader Implications and a Bit of Craft Lore En línea: http://dx.doi.org/10.1007/978-0-387-77501-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34235 Ejemplares
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Título : Statistical Decision Theory : Estimation, Testing, and Selection Tipo de documento: documento electrónico Autores: Klaus J. Miescke ; SpringerLink (Online service) ; F. Liese Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVII, 677 p Il.: online resource ISBN/ISSN/DL: 978-0-387-73194-0 Idioma : Inglés (eng) Palabras clave: Statistics Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: This monograph is written for advanced graduate students, Ph.D. students, and researchers in mathematical statistics and decision theory. All major topics are introduced on a fairly elementary level and then developed gradually to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. It can be used as a basis for graduate courses, seminars, Ph.D. programs, self-studies, and as a reference book. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. Highlights are systematic applications to the fields of parameter estimation, testing hypotheses, and selection of populations. With its broad coverage of decision theory that includes results from other more specialized books as well as new material, this book is one of a kind and fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory. One goal is to present a bridge from the classical results of mathematical statistics and decision theory to the modern asymptotic decision theory founded by LeCam. The striking clearness and powerful applicability of LeCam’s theory is demonstrated with its applications to estimation, testing, and selection on an intermediate level that is accessible to graduate students. Another goal is to present a broad coverage of both the frequentist and the Bayes approach in decision theory. Relations between the Bayes and minimax concepts are studied, and fundamental asymptotic results of modern Bayes statistical theory are included. The third goal is to present, for the first time in a book, a well-rounded theory of optimal selections for parametric families. Friedrich Liese, University of Rostock, and Klaus-J. Miescke, University of Illinois at Chicago, are professors of mathematical statistics who have published numerous research papers in mathematical statistics and decision theory over the past three decades Nota de contenido: Statistical Models -- Tests in Models with Monotonicity Properties -- Statistical Decision Theory -- Comparison of Models, Reduction by -- Invariant Statistical Decision Models -- Large Sample Approximations of Models and Decisions -- Estimation -- Testing -- Selection En línea: http://dx.doi.org/10.1007/978-0-387-73194-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34177 Statistical Decision Theory : Estimation, Testing, and Selection [documento electrónico] / Klaus J. Miescke ; SpringerLink (Online service) ; F. Liese . - New York, NY : Springer New York, 2008 . - XVII, 677 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-73194-0
Idioma : Inglés (eng)
Palabras clave: Statistics Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: This monograph is written for advanced graduate students, Ph.D. students, and researchers in mathematical statistics and decision theory. All major topics are introduced on a fairly elementary level and then developed gradually to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. It can be used as a basis for graduate courses, seminars, Ph.D. programs, self-studies, and as a reference book. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. Highlights are systematic applications to the fields of parameter estimation, testing hypotheses, and selection of populations. With its broad coverage of decision theory that includes results from other more specialized books as well as new material, this book is one of a kind and fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory. One goal is to present a bridge from the classical results of mathematical statistics and decision theory to the modern asymptotic decision theory founded by LeCam. The striking clearness and powerful applicability of LeCam’s theory is demonstrated with its applications to estimation, testing, and selection on an intermediate level that is accessible to graduate students. Another goal is to present a broad coverage of both the frequentist and the Bayes approach in decision theory. Relations between the Bayes and minimax concepts are studied, and fundamental asymptotic results of modern Bayes statistical theory are included. The third goal is to present, for the first time in a book, a well-rounded theory of optimal selections for parametric families. Friedrich Liese, University of Rostock, and Klaus-J. Miescke, University of Illinois at Chicago, are professors of mathematical statistics who have published numerous research papers in mathematical statistics and decision theory over the past three decades Nota de contenido: Statistical Models -- Tests in Models with Monotonicity Properties -- Statistical Decision Theory -- Comparison of Models, Reduction by -- Invariant Statistical Decision Models -- Large Sample Approximations of Models and Decisions -- Estimation -- Testing -- Selection En línea: http://dx.doi.org/10.1007/978-0-387-73194-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34177 Ejemplares
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Título : Statistical Analysis of Management Data Tipo de documento: documento electrónico Autores: Hubert Gatignon ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Número de páginas: XVII, 388 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-1270-1 Idioma : Inglés (eng) Palabras clave: Statistics Econometrics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Social Science, Behavorial Education, Public Policy, Law Clasificación: 51 Matemáticas Resumen: Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software Nota de contenido: Multivariate Normal Distribution -- Reliability Alpha, Principle Component Analysis, and Exploratory Factor Analysis -- Confirmatory Factor Analysis -- Multiple Regression with a Single Dependent Variable -- System of Equations -- Canonical Correlation Analysis -- Categorical Dependent Variables -- Rank-Ordered Data -- Error in Variables – Analysis of Covariance Structure -- Cluster Analysis -- Analysis of Similarity and Preference Data -- Appendices En línea: http://dx.doi.org/10.1007/978-1-4419-1270-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33586 Statistical Analysis of Management Data [documento electrónico] / Hubert Gatignon ; SpringerLink (Online service) . - New York, NY : Springer New York, 2010 . - XVII, 388 p : online resource.
ISBN : 978-1-4419-1270-1
Idioma : Inglés (eng)
Palabras clave: Statistics Econometrics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Social Science, Behavorial Education, Public Policy, Law Clasificación: 51 Matemáticas Resumen: Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software Nota de contenido: Multivariate Normal Distribution -- Reliability Alpha, Principle Component Analysis, and Exploratory Factor Analysis -- Confirmatory Factor Analysis -- Multiple Regression with a Single Dependent Variable -- System of Equations -- Canonical Correlation Analysis -- Categorical Dependent Variables -- Rank-Ordered Data -- Error in Variables – Analysis of Covariance Structure -- Cluster Analysis -- Analysis of Similarity and Preference Data -- Appendices En línea: http://dx.doi.org/10.1007/978-1-4419-1270-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33586 Ejemplares
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Título : Statistical Modeling and Analysis for Complex Data Problems Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Pierre Duchesne ; Rémillard, Bruno Editorial: Boston, MA : Springer US Fecha de publicación: 2005 Número de páginas: XIV, 324 p Il.: online resource ISBN/ISSN/DL: 978-0-387-24555-3 Idioma : Inglés (eng) Palabras clave: Mathematics Medicine Computers Mathematical optimization Probabilities Statistics Optimization Theory of Computation Medicine/Public Health, general Probability and Stochastic Processes Statistical Methods Computing/Statistics Programs Clasificación: 51 Matemáticas Resumen: STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area Nota de contenido: Dependence Properties of Meta-Elliptical Distributions -- The Statistical Significance of Palm Beach County -- Bayesian Functional Estimation of Hazard Rates for Randomly Right Censored Data Using Fourier Series Methods -- Conditions for the Validity of F-Ratio Tests for Treatment and Carryover Effects in Crossover Designs -- Bias in Estimating the Variance of K-Fold Cross-Validation -- Effective Construction of Modified Histograms in Higher Dimensions -- On Robust Diagnostics at Individual Lags Using RA-ARX Estimators -- Bootstrap Confidence Intervals for Periodic Preventive Replacement Policies -- Statistics for Comparison of Two Independent cDNA Filter Microarrays -- Large Deviations for Interacting Processes in the Strong Topology -- Asymptotic Distribution of a Simple Linear Estimator for Varma Models in Echelon Form -- Recent Results for Linear Time Series Models with Non Independent Innovations -- Filtering of Images for Detecting Multiple Targets Trajectories -- Optimal Detection of Periodicities in Vector Autoregressive Models -- The Wilcoxon Signed-Rank Test for Cluster Correlated Data En línea: http://dx.doi.org/10.1007/b105993 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35090 Statistical Modeling and Analysis for Complex Data Problems [documento electrónico] / SpringerLink (Online service) ; Pierre Duchesne ; Rémillard, Bruno . - Boston, MA : Springer US, 2005 . - XIV, 324 p : online resource.
ISBN : 978-0-387-24555-3
Idioma : Inglés (eng)
Palabras clave: Mathematics Medicine Computers Mathematical optimization Probabilities Statistics Optimization Theory of Computation Medicine/Public Health, general Probability and Stochastic Processes Statistical Methods Computing/Statistics Programs Clasificación: 51 Matemáticas Resumen: STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area Nota de contenido: Dependence Properties of Meta-Elliptical Distributions -- The Statistical Significance of Palm Beach County -- Bayesian Functional Estimation of Hazard Rates for Randomly Right Censored Data Using Fourier Series Methods -- Conditions for the Validity of F-Ratio Tests for Treatment and Carryover Effects in Crossover Designs -- Bias in Estimating the Variance of K-Fold Cross-Validation -- Effective Construction of Modified Histograms in Higher Dimensions -- On Robust Diagnostics at Individual Lags Using RA-ARX Estimators -- Bootstrap Confidence Intervals for Periodic Preventive Replacement Policies -- Statistics for Comparison of Two Independent cDNA Filter Microarrays -- Large Deviations for Interacting Processes in the Strong Topology -- Asymptotic Distribution of a Simple Linear Estimator for Varma Models in Echelon Form -- Recent Results for Linear Time Series Models with Non Independent Innovations -- Filtering of Images for Detecting Multiple Targets Trajectories -- Optimal Detection of Periodicities in Vector Autoregressive Models -- The Wilcoxon Signed-Rank Test for Cluster Correlated Data En línea: http://dx.doi.org/10.1007/b105993 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35090 Ejemplares
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Título : Essentials of Monte Carlo Simulation : Statistical Methods for Building Simulation Models Tipo de documento: documento electrónico Autores: Nick T. Thomopoulos ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Número de páginas: XVIII, 174 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-6022-0 Idioma : Inglés (eng) Palabras clave: Statistics Statistical Theory and Methods Computing/Statistics Programs Statistics, general Clasificación: 51 Matemáticas Resumen: Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run very many times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. More than 100 numerical examples are presented in the chapters to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. With a strong focus in the area of computer Monte Carlo simulation methods, this book will appeal to students and researchers in the fields of Mathematics and Statistics. Nick T. Thomopoulos is a professor emeritus at the Illinois Institute of Technology. He is the author of six books, including Fundamentals of Queuing Systems (2012). He has more than 100 published papers and presentations to his credit, and for many years, he has consulted in a wide variety of industries in the United States, Europe, and Asia. He has been the recipient of numerous honors, such as the Rist Prize in 1972 from the Military Operations Research Society for new developments in queuing theory, the Distinguished Professor Award in Bangkok, Thailand in 2005 from the IIT Asian Alumni Association, and the Professional Achievement Award in 2009 from the IIT Alumni Association En línea: http://dx.doi.org/10.1007/978-1-4614-6022-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32286 Essentials of Monte Carlo Simulation : Statistical Methods for Building Simulation Models [documento electrónico] / Nick T. Thomopoulos ; SpringerLink (Online service) . - New York, NY : Springer New York : Imprint: Springer, 2013 . - XVIII, 174 p : online resource.
ISBN : 978-1-4614-6022-0
Idioma : Inglés (eng)
Palabras clave: Statistics Statistical Theory and Methods Computing/Statistics Programs Statistics, general Clasificación: 51 Matemáticas Resumen: Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run very many times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. More than 100 numerical examples are presented in the chapters to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. With a strong focus in the area of computer Monte Carlo simulation methods, this book will appeal to students and researchers in the fields of Mathematics and Statistics. Nick T. Thomopoulos is a professor emeritus at the Illinois Institute of Technology. He is the author of six books, including Fundamentals of Queuing Systems (2012). He has more than 100 published papers and presentations to his credit, and for many years, he has consulted in a wide variety of industries in the United States, Europe, and Asia. He has been the recipient of numerous honors, such as the Rist Prize in 1972 from the Military Operations Research Society for new developments in queuing theory, the Distinguished Professor Award in Bangkok, Thailand in 2005 from the IIT Asian Alumni Association, and the Professional Achievement Award in 2009 from the IIT Alumni Association En línea: http://dx.doi.org/10.1007/978-1-4614-6022-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32286 Ejemplares
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