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Título : The Pleasures of Statistics : The Autobiography of Frederick Mosteller Tipo de documento: documento electrónico Autores: Frederick Mosteller ; SpringerLink (Online service) ; Stephen E. Fienberg ; David C. Hoaglin ; Judith M. Tanur Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Otro editor: Imprint: Springer Número de páginas: XVI, 344 p Il.: online resource ISBN/ISSN/DL: 978-0-387-77956-0 Idioma : Inglés (eng) Palabras clave: Statistics Public health Biostatistics Probabilities Assessment Social sciences Statistical Theory and Methods Probability Stochastic Processes Methodology of the Sciences Assessment, Testing Evaluation Health Clasificación: 51 Matemáticas Resumen: From his unique perspective, renowned statistician and educator Frederick Mosteller describes many of the projects and events in his long career. From humble beginnings in western Pennsylvania to becoming the founding chairman of Harvard University’s Department of Statistics and beyond, he inspired many statisticians, scientists, and students with his unabashed pragmatism, creative thinking, and zest for both learning and teaching. This candid account offers fresh insights into the qualities that made Mosteller a superb teacher, a prolific scholar, a respected leader, and a valued advisor. A special feature of the book is its chapter-length insider accounts of work on the pre-election polls of 1948, statistical aspects of the Kinsey report on sexual behavior in the human male, mathematical learning theory, authorship of the disputed Federalist papers, safety of anesthetics, and a wide-ranging examination of the Coleman report on equality of educational opportunity. This volume is a companion to Selected Papers of Frederick Mosteller (Springer, 2006) and A Statistical Model: Frederick Mosteller’s Contributions to Statistics, Science, and Public Policy (Springer-Verlag, 1990). Frederick Mosteller (1916–2006) was Roger I. Lee Professor of Mathematical Statistics at Harvard University. His manuscript was unfinished at his death and has been updated Nota de contenido: Examples of Quantitative Studies -- Why Did Dewey Beat Truman in the Pre-election Polls of 1948? -- Sexual Behavior in the United States: The Kinsey Report -- Learning Theory: Founding Mathematical Psychology -- Who Wrote the Disputed Federalist Papers, Hamilton or Madison? -- The Safety of Anesthetics: The National Halothane Study -- Equality of Educational Opportunity: The Coleman Report -- Early Life and Education -- Childhood -- Secondary School -- Carnegie Institute of Technology -- Graduate Schools: Carnegie and Princeton -- Magic -- Beginning Research -- Completing the Doctorate -- Coming to Harvard University -- Organizing Statistics -- Continuing Activities -- Evaluation -- Teaching -- Group Writing -- The Cape -- Biostatistics -- Health Policy and Management -- Health Science Policy -- Editors#x2019; Epilogue En línea: http://dx.doi.org/10.1007/978-0-387-77956-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33507 The Pleasures of Statistics : The Autobiography of Frederick Mosteller [documento electrónico] / Frederick Mosteller ; SpringerLink (Online service) ; Stephen E. Fienberg ; David C. Hoaglin ; Judith M. Tanur . - New York, NY : Springer New York : Imprint: Springer, 2010 . - XVI, 344 p : online resource.
ISBN : 978-0-387-77956-0
Idioma : Inglés (eng)
Palabras clave: Statistics Public health Biostatistics Probabilities Assessment Social sciences Statistical Theory and Methods Probability Stochastic Processes Methodology of the Sciences Assessment, Testing Evaluation Health Clasificación: 51 Matemáticas Resumen: From his unique perspective, renowned statistician and educator Frederick Mosteller describes many of the projects and events in his long career. From humble beginnings in western Pennsylvania to becoming the founding chairman of Harvard University’s Department of Statistics and beyond, he inspired many statisticians, scientists, and students with his unabashed pragmatism, creative thinking, and zest for both learning and teaching. This candid account offers fresh insights into the qualities that made Mosteller a superb teacher, a prolific scholar, a respected leader, and a valued advisor. A special feature of the book is its chapter-length insider accounts of work on the pre-election polls of 1948, statistical aspects of the Kinsey report on sexual behavior in the human male, mathematical learning theory, authorship of the disputed Federalist papers, safety of anesthetics, and a wide-ranging examination of the Coleman report on equality of educational opportunity. This volume is a companion to Selected Papers of Frederick Mosteller (Springer, 2006) and A Statistical Model: Frederick Mosteller’s Contributions to Statistics, Science, and Public Policy (Springer-Verlag, 1990). Frederick Mosteller (1916–2006) was Roger I. Lee Professor of Mathematical Statistics at Harvard University. His manuscript was unfinished at his death and has been updated Nota de contenido: Examples of Quantitative Studies -- Why Did Dewey Beat Truman in the Pre-election Polls of 1948? -- Sexual Behavior in the United States: The Kinsey Report -- Learning Theory: Founding Mathematical Psychology -- Who Wrote the Disputed Federalist Papers, Hamilton or Madison? -- The Safety of Anesthetics: The National Halothane Study -- Equality of Educational Opportunity: The Coleman Report -- Early Life and Education -- Childhood -- Secondary School -- Carnegie Institute of Technology -- Graduate Schools: Carnegie and Princeton -- Magic -- Beginning Research -- Completing the Doctorate -- Coming to Harvard University -- Organizing Statistics -- Continuing Activities -- Evaluation -- Teaching -- Group Writing -- The Cape -- Biostatistics -- Health Policy and Management -- Health Science Policy -- Editors#x2019; Epilogue En línea: http://dx.doi.org/10.1007/978-0-387-77956-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33507 Ejemplares
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Título : Comparing Distributions Tipo de documento: documento electrónico Autores: Olivier Thas ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Otro editor: Imprint: Springer Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVI, 354 p Il.: online resource ISBN/ISSN/DL: 978-0-387-92710-7 Idioma : Inglés (eng) Palabras clave: Statistics Operations research Decision making Data mining Biostatistics Probabilities Social sciences Statistics, general Probability Theory and Stochastic Processes Methodology of the Sciences Mining Knowledge Discovery Operation Research/Decision Clasificación: 51 Matemáticas Resumen: Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing. Whereas the latter includes only formal statistical hypothesis tests for the one-sample and the K-sample problems, this book presents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the null hypothesis. Despite the historically seemingly different development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone. This book consists of two parts. In the first part statistical methods for the one-sample problem are discussed. The second part of the book treats the K-sample problem. Many sections of this second part of the book may be of interest to every statistician who is involved in comparative studies. The book gives a self-contained theoretical treatment of a wide range of goodness-of-fit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric, semiparametric and nonparametric theory, which is kept at an intermediate level; the intuition and heuristics behind the methods are usually provided as well. The book contains many data examples that are analysed with the cd R-package that is written by the author. All examples include the R-code. Because many methods described in this book belong to the basic toolbox of almost every statistician, the book should be of interest to a wide audience. In particular, the book may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied statisticians may also be interested because of the many examples, the R-code and the stress on the informative nature of the procedures. Olivier Thas is Associate Professor of Biostatistics at Ghent University. He has published methodological papers on goodness-of-fit testing, but he has also published more applied work in the areas of environmental statistics and genomics Nota de contenido: One-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two-Sample and K-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Some Important Two-Sample Tests -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two Final Methods and Some Final Thoughts En línea: http://dx.doi.org/10.1007/978-0-387-92710-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33525 Comparing Distributions [documento electrónico] / Olivier Thas ; SpringerLink (Online service) . - New York, NY : Springer New York : Imprint: Springer, 2010 . - XVI, 354 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-92710-7
Idioma : Inglés (eng)
Palabras clave: Statistics Operations research Decision making Data mining Biostatistics Probabilities Social sciences Statistics, general Probability Theory and Stochastic Processes Methodology of the Sciences Mining Knowledge Discovery Operation Research/Decision Clasificación: 51 Matemáticas Resumen: Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing. Whereas the latter includes only formal statistical hypothesis tests for the one-sample and the K-sample problems, this book presents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the null hypothesis. Despite the historically seemingly different development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone. This book consists of two parts. In the first part statistical methods for the one-sample problem are discussed. The second part of the book treats the K-sample problem. Many sections of this second part of the book may be of interest to every statistician who is involved in comparative studies. The book gives a self-contained theoretical treatment of a wide range of goodness-of-fit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric, semiparametric and nonparametric theory, which is kept at an intermediate level; the intuition and heuristics behind the methods are usually provided as well. The book contains many data examples that are analysed with the cd R-package that is written by the author. All examples include the R-code. Because many methods described in this book belong to the basic toolbox of almost every statistician, the book should be of interest to a wide audience. In particular, the book may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied statisticians may also be interested because of the many examples, the R-code and the stress on the informative nature of the procedures. Olivier Thas is Associate Professor of Biostatistics at Ghent University. He has published methodological papers on goodness-of-fit testing, but he has also published more applied work in the areas of environmental statistics and genomics Nota de contenido: One-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two-Sample and K-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Some Important Two-Sample Tests -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two Final Methods and Some Final Thoughts En línea: http://dx.doi.org/10.1007/978-0-387-92710-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33525 Ejemplares
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Título : Dynamic Mixed Models for Familial Longitudinal Data Tipo de documento: documento electrónico Autores: Brajendra C. Sutradhar ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVIII, 494 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-8342-8 Idioma : Inglés (eng) Palabras clave: Statistics Epidemiology Biometrics (Biology) Biostatistics Econometrics Social sciences Statistical Theory and Methods Methodology of the Sciences Clasificación: 51 Matemáticas Resumen: This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalized estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up. The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents the illustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to the society including his special services for society’s annual meetings Nota de contenido: Introduction -- Overview of Linear Fixed Models for Longitudinal Data -- Overview of Linear Mixed Models for Longitudinal Data -- Familial Models for Count Data -- Familial Models for Binary Data -- Longitudinal Models for Count Data -- Longitudinal Models for Binary Data -- Longitudinal Mixed Models for Count Data -- Longitudinal Mixed Models for Binary Data -- Familial Longitudinal Models for Count Data -- Familial Longitudinal Models for Binary Data En línea: http://dx.doi.org/10.1007/978-1-4419-8342-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33181 Dynamic Mixed Models for Familial Longitudinal Data [documento electrónico] / Brajendra C. Sutradhar ; SpringerLink (Online service) . - New York, NY : Springer New York, 2011 . - XVIII, 494 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-1-4419-8342-8
Idioma : Inglés (eng)
Palabras clave: Statistics Epidemiology Biometrics (Biology) Biostatistics Econometrics Social sciences Statistical Theory and Methods Methodology of the Sciences Clasificación: 51 Matemáticas Resumen: This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalized estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up. The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents the illustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to the society including his special services for society’s annual meetings Nota de contenido: Introduction -- Overview of Linear Fixed Models for Longitudinal Data -- Overview of Linear Mixed Models for Longitudinal Data -- Familial Models for Count Data -- Familial Models for Binary Data -- Longitudinal Models for Count Data -- Longitudinal Models for Binary Data -- Longitudinal Mixed Models for Count Data -- Longitudinal Mixed Models for Binary Data -- Familial Longitudinal Models for Count Data -- Familial Longitudinal Models for Binary Data En línea: http://dx.doi.org/10.1007/978-1-4419-8342-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33181 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R / SpringerLink (Online service) ; Dan Lin ; Shkedy, Ziv ; Daniel Yekutieli ; Dhammika Amaratunga ; Luc Bijnens (2012)
Título : Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order-Restricted Analysis of Microarray Data Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Dan Lin ; Shkedy, Ziv ; Daniel Yekutieli ; Dhammika Amaratunga ; Luc Bijnens Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: Use R! Número de páginas: XV, 282 p. 96 illus., 4 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-642-24007-2 Idioma : Inglés (eng) Palabras clave: Statistics Pharmaceutical technology Bioinformatics Biostatistics Computational biology Statistics, general and Computing/Statistics Programs Sciences/Technology Computer Appl. in Life Sciences Clasificación: 51 Matemáticas Resumen: This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as the likelihood ratio test and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book. Methodological topics discussed include: · Multiplicity adjustment · Test statistics and testing procedures for the analysis of dose-response microarray data · Resampling-based inference and use of the SAM method at the presence of small-variance genes in the data · Identification and classification of dose-response curve shapes · Clustering of order restricted (but not necessarily monotone) dose-response profiles · Hierarchical Bayesian models and non-linear models for dose-response microarray data · Multiple contrast tests All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments Nota de contenido: Introduction -- Part I: Dose-response Modeling: An Introduction -- Estimation Under Order Restrictions -- The Likelihood Ratio Test -- Part II: Dose-response Microarray Experiments -- Functional Genomic Dose-response Experiments -- Adjustment for Multiplicity -- Test for Trend -- Order Restricted Bisclusters -- Classification of Trends in Dose-response Microarray Experiments Using Information Theory Selection Methods -- Multiple Contrast Test -- Confidence Intervals for the Selected Parameters -- Case Study Using GUI in R: Gene Expression Analysis After Acute Treatment With Antipsychotics En línea: http://dx.doi.org/10.1007/978-3-642-24007-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32941 Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order-Restricted Analysis of Microarray Data [documento electrónico] / SpringerLink (Online service) ; Dan Lin ; Shkedy, Ziv ; Daniel Yekutieli ; Dhammika Amaratunga ; Luc Bijnens . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012 . - XV, 282 p. 96 illus., 4 illus. in color : online resource. - (Use R!) .
ISBN : 978-3-642-24007-2
Idioma : Inglés (eng)
Palabras clave: Statistics Pharmaceutical technology Bioinformatics Biostatistics Computational biology Statistics, general and Computing/Statistics Programs Sciences/Technology Computer Appl. in Life Sciences Clasificación: 51 Matemáticas Resumen: This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as the likelihood ratio test and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book. Methodological topics discussed include: · Multiplicity adjustment · Test statistics and testing procedures for the analysis of dose-response microarray data · Resampling-based inference and use of the SAM method at the presence of small-variance genes in the data · Identification and classification of dose-response curve shapes · Clustering of order restricted (but not necessarily monotone) dose-response profiles · Hierarchical Bayesian models and non-linear models for dose-response microarray data · Multiple contrast tests All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments Nota de contenido: Introduction -- Part I: Dose-response Modeling: An Introduction -- Estimation Under Order Restrictions -- The Likelihood Ratio Test -- Part II: Dose-response Microarray Experiments -- Functional Genomic Dose-response Experiments -- Adjustment for Multiplicity -- Test for Trend -- Order Restricted Bisclusters -- Classification of Trends in Dose-response Microarray Experiments Using Information Theory Selection Methods -- Multiple Contrast Test -- Confidence Intervals for the Selected Parameters -- Case Study Using GUI in R: Gene Expression Analysis After Acute Treatment With Antipsychotics En línea: http://dx.doi.org/10.1007/978-3-642-24007-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32941 Ejemplares
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Título : Numerical Ecology with R Tipo de documento: documento electrónico Autores: Daniel Borcard ; SpringerLink (Online service) ; Francois Gillet ; Legendre, Pierre Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Use R Número de páginas: XII, 306 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-7976-6 Idioma : Inglés (eng) Palabras clave: Statistics Epidemiology Biostatistics Ecology Forestry for Life Sciences, Medicine, Health Sciences Environmental Monitoring/Analysis Theoretical Ecology/Statistics Clasificación: 51 Matemáticas Resumen: Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site(http://adn.biol.umontreal.ca/~numericalecology/numecolR/). This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment Nota de contenido: Introduction -- Exploratory data analysis -- Association measures and matrices -- Cluster analysis -- Unconstrained ordination -- Canonical ordination -- Spatial analysis of ecological data. En línea: http://dx.doi.org/10.1007/978-1-4419-7976-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33178 Numerical Ecology with R [documento electrónico] / Daniel Borcard ; SpringerLink (Online service) ; Francois Gillet ; Legendre, Pierre . - New York, NY : Springer New York, 2011 . - XII, 306 p : online resource. - (Use R) .
ISBN : 978-1-4419-7976-6
Idioma : Inglés (eng)
Palabras clave: Statistics Epidemiology Biostatistics Ecology Forestry for Life Sciences, Medicine, Health Sciences Environmental Monitoring/Analysis Theoretical Ecology/Statistics Clasificación: 51 Matemáticas Resumen: Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site(http://adn.biol.umontreal.ca/~numericalecology/numecolR/). This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment Nota de contenido: Introduction -- Exploratory data analysis -- Association measures and matrices -- Cluster analysis -- Unconstrained ordination -- Canonical ordination -- Spatial analysis of ecological data. En línea: http://dx.doi.org/10.1007/978-1-4419-7976-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33178 Ejemplares
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