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Advances in Social Science Research Using R / SpringerLink (Online service) ; Vinod, Hrishikesh D (2010)
Título : Advances in Social Science Research Using R Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Vinod, Hrishikesh D Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Colección: Lecture Notes in Statistics, ISSN 09300325 num. 196 Número de páginas: XXIII, 205 p Il.: online resource ISBN/ISSN/DL: 9781441917645 Idioma : Inglés (eng) Palabras clave: Statistics Public health Economics, Mathematical Econometrics Social sciences for Business/Economics/Mathematical Finance/Insurance Environmental Monitoring/Analysis Quantitative Finance Health Methodology of the Sciences Clasificación: 51 Matemáticas Resumen: This book covers recent advances for quantitative researchers with practical examples from social sciences. The twelve chapters written by distinguished authors cover a wide range of issuesall providing practical tools using the free R software. McCullough: R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime. Koenker: Additive models provide a clever compromise between parametric and nonparametric components illustrated by risk factors for Indian malnutrition. Gelman: R graphics in the context of voter participation in US elections. Vinod: New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment. Markus and Gu: New R tools for exploratory data analysis including bubble plots. Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and datamining experts; relevant to anyone with an investment portfolio. Foster and Kecojevic: Extends the usual analysis of covariance (ANCOVA) illustrated by growth charts for Saudi children. Imai, Keele, Tingley, and Yamamoto: New R tools for solving the ageold scientific problem of assessing the direction and strength of causation. Their job search illustration is of interest during current times of high unemployment. Haupt, Schnurbus, and Tschernig: Consider the choice of functional form for an unknown, potentially nonlinear relationship, explaining a set of new R tools for model visualization and validation. Rindskopf: R methods to fit a multinomial based multivariate analysis of variance (ANOVA) with examples from psychology, sociology, political science, and medicine. Neath: R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site. Numatsi and Rengifo: Explain persistent discrete jumps in financial series subject to misspecification Nota de contenido: Econometric Computing with #x201C;R#x201D;  Additive Models for Quantile Regression: An Analysis of Risk Factors for Malnutrition in India  Toward Better R Defaults for Graphics: Example of Voter Turnouts in U.S. Elections  Superior Estimation and Inference Avoiding Heteroscedasticity and Flawed Pivots: Rexample of Inflation Unemployment Tradeoff  Bubble Plots as a ModelFree Graphical Tool for Continuous Variables  Combinatorial Fusion for Improving Portfolio Performance  Reference Growth Charts for Saudi Arabian Children and Adolescents  Causal Mediation Analysis Using R  Statistical Validation of Functional Form in Multiple Regression Using R  Fitting Multinomial Models in R: A Program Based on Bock#x2019;s Multinomial Response Relation Model  A Bayesian Analysis of Leukemia Incidence Surrounding an Inactive Hazardous Waste Site  Stochastic Volatility Model with Jumps in Returns and Volatility: An RPackage Implementation En línea: http://dx.doi.org/10.1007/9781441917645 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33610 Advances in Social Science Research Using R [documento electrónico] / SpringerLink (Online service) ; Vinod, Hrishikesh D .  New York, NY : Springer New York, 2010 .  XXIII, 205 p : online resource.  (Lecture Notes in Statistics, ISSN 09300325; 196) .
ISBN : 9781441917645
Idioma : Inglés (eng)
Palabras clave: Statistics Public health Economics, Mathematical Econometrics Social sciences for Business/Economics/Mathematical Finance/Insurance Environmental Monitoring/Analysis Quantitative Finance Health Methodology of the Sciences Clasificación: 51 Matemáticas Resumen: This book covers recent advances for quantitative researchers with practical examples from social sciences. The twelve chapters written by distinguished authors cover a wide range of issuesall providing practical tools using the free R software. McCullough: R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime. Koenker: Additive models provide a clever compromise between parametric and nonparametric components illustrated by risk factors for Indian malnutrition. Gelman: R graphics in the context of voter participation in US elections. Vinod: New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment. Markus and Gu: New R tools for exploratory data analysis including bubble plots. Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and datamining experts; relevant to anyone with an investment portfolio. Foster and Kecojevic: Extends the usual analysis of covariance (ANCOVA) illustrated by growth charts for Saudi children. Imai, Keele, Tingley, and Yamamoto: New R tools for solving the ageold scientific problem of assessing the direction and strength of causation. Their job search illustration is of interest during current times of high unemployment. Haupt, Schnurbus, and Tschernig: Consider the choice of functional form for an unknown, potentially nonlinear relationship, explaining a set of new R tools for model visualization and validation. Rindskopf: R methods to fit a multinomial based multivariate analysis of variance (ANOVA) with examples from psychology, sociology, political science, and medicine. Neath: R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site. Numatsi and Rengifo: Explain persistent discrete jumps in financial series subject to misspecification Nota de contenido: Econometric Computing with #x201C;R#x201D;  Additive Models for Quantile Regression: An Analysis of Risk Factors for Malnutrition in India  Toward Better R Defaults for Graphics: Example of Voter Turnouts in U.S. Elections  Superior Estimation and Inference Avoiding Heteroscedasticity and Flawed Pivots: Rexample of Inflation Unemployment Tradeoff  Bubble Plots as a ModelFree Graphical Tool for Continuous Variables  Combinatorial Fusion for Improving Portfolio Performance  Reference Growth Charts for Saudi Arabian Children and Adolescents  Causal Mediation Analysis Using R  Statistical Validation of Functional Form in Multiple Regression Using R  Fitting Multinomial Models in R: A Program Based on Bock#x2019;s Multinomial Response Relation Model  A Bayesian Analysis of Leukemia Incidence Surrounding an Inactive Hazardous Waste Site  Stochastic Volatility Model with Jumps in Returns and Volatility: An RPackage Implementation En línea: http://dx.doi.org/10.1007/9781441917645 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33610 Ejemplares
Signatura Medio Ubicación Sublocalización Sección Estado ningún ejemplar Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series / Estela Bee Dagum (2006)
Título : Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series Tipo de documento: documento electrónico Autores: Estela Bee Dagum ; SpringerLink (Online service) ; Cholette, Pierre A Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Colección: Lecture Notes in Statistics, ISSN 09300325 num. 186 Número de páginas: XIV, 410 p. 101 illus Il.: online resource ISBN/ISSN/DL: 9780387354392 Idioma : Inglés (eng) Palabras clave: Statistics Econometrics Economics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: In modern economies, time series play a crucial role at all levels of activity. They are used by decision makers to plan for a better future, by governments to promote prosperity, by central banks to control inflation, by unions to bargain for higher wages, by hospital, school boards, manufacturers, builders, transportation companies, and by consumers in general. A common misconception is that time series data originate from the direct and straightforward compilations of survey data, censuses, and administrative records. On the contrary, before publication time series are subject to statistical adjustments intended to facilitate analysis, increase efficiency, reduce bias, replace missing values, correct errors, and satisfy crosssectional additivity constraints. Some of the most common adjustments are benchmarking, interpolation, temporal distribution, calendarization, and reconciliation. This book discusses the statistical methods most often applied for such adjustments, ranging from ad hoc procedures to regressionbased models. The latter are emphasized, because of their clarity, ease of application, and superior results. Each topic is illustrated with many real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed, a real data example, the Canada Total Retail Trade Series, is followed throughout the book. This book brings together the scattered literature on these topics and presents them using a consistent notation and a unifying view. The book will promote better procedures by large producers of time series, e.g. statistical agencies and central banks. Furthermore, knowing what adjustments are made to the data and what technique is used and how they affect the trend, the business cycles and seasonality of the series, will enable users to perform better modeling, prediction, analysis and planning. This book will prove useful to graduate students and final year undergraduate students of time series and econometrics, as well as researchers and practitioners in government institutions and business. Estela Bee Dagum is Professor at the Faculty of Statistical Science of the University of Bologna, Italy, and former Director of the Time Series Research and Analysis division of Statistics Canada, Ottawa, Canada. Dr. Dagum was awarded an Honorary Doctoral Degree from the University of Naples "Parthenope", is a Fellow of the American Statistical Association (ASA) and Honorary Fellow of the International Institute of Forecasters (IIF), the first recipient of the ASA Julius Shiskin Award, the IIF Crystal Globe Award, Elected Member of the International Statistical Institute (ISI), Elected Member of the Academy of Science of the Institute of Bologna, and former President of the Interamerican Statistical Institute (IASI) and the International Institute of Forecasters. Dr. Dagum is the author of the X11ARIMA seasonal adjustment method widely applied by statistical agencies and central banks. Pierre A. Cholette is a Senior Methodologist of the Time Series Research Centre of the Business Survey Methodology Division at Statistics Canada, Ottawa, Canada. He is the author of BENCH, a benchmarking software widely applied by statistical agencies, Central Banks and other government institutions Nota de contenido: The Components of Time Series  The CholetteDagum RegressionBased Benchmarking Method — The Additive Model  Covariance Matrices for Benchmarking and Reconciliation Methods  The CholetteDagum RegressionBased Benchmarking Method  The Multiplicative Model  The Denton Method and its Variants  Temporal Distribution, Interpolation and Extrapolation  Signal Extraction and Benchmarking  Calendarization  A Unified RegressionBased Framework for Signal Extraction, Benchmarking and Interpolation  Reconciliation and Balancing Systems of Time Series  Reconciling OneWay Classified Systems of Time Series  Reconciling the Marginal Totals of TwoWay Classified Systems of Series  Reconciling TwoWay Classifed Systems of Series En línea: http://dx.doi.org/10.1007/0387354395 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34820 Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series [documento electrónico] / Estela Bee Dagum ; SpringerLink (Online service) ; Cholette, Pierre A .  New York, NY : Springer New York, 2006 .  XIV, 410 p. 101 illus : online resource.  (Lecture Notes in Statistics, ISSN 09300325; 186) .
ISBN : 9780387354392
Idioma : Inglés (eng)
Palabras clave: Statistics Econometrics Economics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: In modern economies, time series play a crucial role at all levels of activity. They are used by decision makers to plan for a better future, by governments to promote prosperity, by central banks to control inflation, by unions to bargain for higher wages, by hospital, school boards, manufacturers, builders, transportation companies, and by consumers in general. A common misconception is that time series data originate from the direct and straightforward compilations of survey data, censuses, and administrative records. On the contrary, before publication time series are subject to statistical adjustments intended to facilitate analysis, increase efficiency, reduce bias, replace missing values, correct errors, and satisfy crosssectional additivity constraints. Some of the most common adjustments are benchmarking, interpolation, temporal distribution, calendarization, and reconciliation. This book discusses the statistical methods most often applied for such adjustments, ranging from ad hoc procedures to regressionbased models. The latter are emphasized, because of their clarity, ease of application, and superior results. Each topic is illustrated with many real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed, a real data example, the Canada Total Retail Trade Series, is followed throughout the book. This book brings together the scattered literature on these topics and presents them using a consistent notation and a unifying view. The book will promote better procedures by large producers of time series, e.g. statistical agencies and central banks. Furthermore, knowing what adjustments are made to the data and what technique is used and how they affect the trend, the business cycles and seasonality of the series, will enable users to perform better modeling, prediction, analysis and planning. This book will prove useful to graduate students and final year undergraduate students of time series and econometrics, as well as researchers and practitioners in government institutions and business. Estela Bee Dagum is Professor at the Faculty of Statistical Science of the University of Bologna, Italy, and former Director of the Time Series Research and Analysis division of Statistics Canada, Ottawa, Canada. Dr. Dagum was awarded an Honorary Doctoral Degree from the University of Naples "Parthenope", is a Fellow of the American Statistical Association (ASA) and Honorary Fellow of the International Institute of Forecasters (IIF), the first recipient of the ASA Julius Shiskin Award, the IIF Crystal Globe Award, Elected Member of the International Statistical Institute (ISI), Elected Member of the Academy of Science of the Institute of Bologna, and former President of the Interamerican Statistical Institute (IASI) and the International Institute of Forecasters. Dr. Dagum is the author of the X11ARIMA seasonal adjustment method widely applied by statistical agencies and central banks. Pierre A. Cholette is a Senior Methodologist of the Time Series Research Centre of the Business Survey Methodology Division at Statistics Canada, Ottawa, Canada. He is the author of BENCH, a benchmarking software widely applied by statistical agencies, Central Banks and other government institutions Nota de contenido: The Components of Time Series  The CholetteDagum RegressionBased Benchmarking Method — The Additive Model  Covariance Matrices for Benchmarking and Reconciliation Methods  The CholetteDagum RegressionBased Benchmarking Method  The Multiplicative Model  The Denton Method and its Variants  Temporal Distribution, Interpolation and Extrapolation  Signal Extraction and Benchmarking  Calendarization  A Unified RegressionBased Framework for Signal Extraction, Benchmarking and Interpolation  Reconciliation and Balancing Systems of Time Series  Reconciling OneWay Classified Systems of Time Series  Reconciling the Marginal Totals of TwoWay Classified Systems of Series  Reconciling TwoWay Classifed Systems of Series En línea: http://dx.doi.org/10.1007/0387354395 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34820 Ejemplares
Signatura Medio Ubicación Sublocalización Sección Estado ningún ejemplar Case Studies in Spatial Point Process Modeling / SpringerLink (Online service) ; Adrian Baddeley ; Gregori, Pablo ; Mateu, Jorge ; Stoica, Radu ; Stoyan, Dietrich (2006)
Título : Case Studies in Spatial Point Process Modeling Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Adrian Baddeley ; Gregori, Pablo ; Mateu, Jorge ; Stoica, Radu ; Stoyan, Dietrich Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Colección: Lecture Notes in Statistics, ISSN 09300325 num. 185 Número de páginas: XVIII, 310 p Il.: online resource ISBN/ISSN/DL: 9780387311449 Idioma : Inglés (eng) Palabras clave: Statistics Earth sciences Probabilities Statistical Theory and Methods Probability Stochastic Processes Sciences, general Clasificación: 51 Matemáticas Resumen: Point process statistics is successfully used in fields such as material science, human epidemiology, social sciences, animal epidemiology, biology, and seismology. Its further application depends greatly on good software and instructive case studies that show the way to successful work. This book satisfies this need by a presentation of the spatstat package and many statistical examples. Researchers, spatial statisticians and scientists from biology, geosciences, materials sciences and other fields will use this book as a helpful guide to the application of point process statistics. No other book presents so many wellfounded point process case studies. Adrian Baddeley is Professor of Statistics at the University of Western Australia (Perth, Australia) and a Fellow of the Australian Academy of Science. His main research interests are in stochastic geometry, stereology, spatial statistics, image analysis and statistical software. Pablo Gregori is senior lecturer of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon. His research fields of interest are spatial statistics, mainly on spatial point processes, and measure theory of functional analysis. Jorge Mateu is Assistant Professor of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon and a Fellow of the Spanish Statistical Society and of Wessex Institute of Great Britain. His main research interests are in stochastic geometry and spatial statistics, mainly spatial point processes and geostatistics. Radu Stoica obtained his Ph.D. in 2001 from the University of Nice Sophia Anitpolis. He works within the biometry group at INRA Avignon. His research interests are related to the study and the simulation of point processes applied to pattern modeling and recognition. The aimed application domains are image processing, astronomy and environmental sciences. Dietrich Stoyan is Professor of Applied Stochastics at TU Bergakademie Freiberg, Germany. Since the end of the 1970s he has worked in the fields of stochastic geometry and spatial statistics Nota de contenido: Basic Notions and Manipulation of Spatial Point Processes  Fundamentals of Point Process Statistics  Modelling Spatial Point Patterns in R  Theoretical and Methodological Advances in Spatial Point Processes  Strong Markov Property of Poisson Processes and Slivnyak Formula  Bayesian Analysis of Markov Point Processes  Statistics for Locally Scaled Point Processes  Nonparametric Testing of Distribution Functions in Germgrain Models  Principal Component Analysis for Spatial Point Processes — Assessing the Appropriateness of the Approach in an Ecological Context  Practical Applications of Spatial Point Processes  On Modelling of Refractory Castables by Marked Gibbs and Gibbsianlike Processes  Source Detection in an Outbreak of Legionnaire’s Disease  Doctors’ Prescribing Patterns in the MidiPyrénées rRegion of France: Pointprocess Aggregation  Straintyping Transmissible Spongiform Encephalopathies Using Replicated Spatial Data  Modelling the Bivariate Spatial Distribution of Amacrine Cells  Analysis of Spatial Point Patterns in Microscopic and Macroscopic Biological Image Data  Spatial Marked Point Patterns for Herd Dispersion in a Savanna Wildlife Herbivore Community in Kenya  Diagnostic Analysis of SpaceTime Branching Processes for Earthquakes  Assessing Spatial Point Process Models Using Weighted Kfunctions: Analysis of California Earthquakes En línea: http://dx.doi.org/10.1007/0387311440 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34783 Case Studies in Spatial Point Process Modeling [documento electrónico] / SpringerLink (Online service) ; Adrian Baddeley ; Gregori, Pablo ; Mateu, Jorge ; Stoica, Radu ; Stoyan, Dietrich .  New York, NY : Springer New York, 2006 .  XVIII, 310 p : online resource.  (Lecture Notes in Statistics, ISSN 09300325; 185) .
ISBN : 9780387311449
Idioma : Inglés (eng)
Palabras clave: Statistics Earth sciences Probabilities Statistical Theory and Methods Probability Stochastic Processes Sciences, general Clasificación: 51 Matemáticas Resumen: Point process statistics is successfully used in fields such as material science, human epidemiology, social sciences, animal epidemiology, biology, and seismology. Its further application depends greatly on good software and instructive case studies that show the way to successful work. This book satisfies this need by a presentation of the spatstat package and many statistical examples. Researchers, spatial statisticians and scientists from biology, geosciences, materials sciences and other fields will use this book as a helpful guide to the application of point process statistics. No other book presents so many wellfounded point process case studies. Adrian Baddeley is Professor of Statistics at the University of Western Australia (Perth, Australia) and a Fellow of the Australian Academy of Science. His main research interests are in stochastic geometry, stereology, spatial statistics, image analysis and statistical software. Pablo Gregori is senior lecturer of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon. His research fields of interest are spatial statistics, mainly on spatial point processes, and measure theory of functional analysis. Jorge Mateu is Assistant Professor of Statistics and Probability at the Department of Mathematics, University Jaume I of Castellon and a Fellow of the Spanish Statistical Society and of Wessex Institute of Great Britain. His main research interests are in stochastic geometry and spatial statistics, mainly spatial point processes and geostatistics. Radu Stoica obtained his Ph.D. in 2001 from the University of Nice Sophia Anitpolis. He works within the biometry group at INRA Avignon. His research interests are related to the study and the simulation of point processes applied to pattern modeling and recognition. The aimed application domains are image processing, astronomy and environmental sciences. Dietrich Stoyan is Professor of Applied Stochastics at TU Bergakademie Freiberg, Germany. Since the end of the 1970s he has worked in the fields of stochastic geometry and spatial statistics Nota de contenido: Basic Notions and Manipulation of Spatial Point Processes  Fundamentals of Point Process Statistics  Modelling Spatial Point Patterns in R  Theoretical and Methodological Advances in Spatial Point Processes  Strong Markov Property of Poisson Processes and Slivnyak Formula  Bayesian Analysis of Markov Point Processes  Statistics for Locally Scaled Point Processes  Nonparametric Testing of Distribution Functions in Germgrain Models  Principal Component Analysis for Spatial Point Processes — Assessing the Appropriateness of the Approach in an Ecological Context  Practical Applications of Spatial Point Processes  On Modelling of Refractory Castables by Marked Gibbs and Gibbsianlike Processes  Source Detection in an Outbreak of Legionnaire’s Disease  Doctors’ Prescribing Patterns in the MidiPyrénées rRegion of France: Pointprocess Aggregation  Straintyping Transmissible Spongiform Encephalopathies Using Replicated Spatial Data  Modelling the Bivariate Spatial Distribution of Amacrine Cells  Analysis of Spatial Point Patterns in Microscopic and Macroscopic Biological Image Data  Spatial Marked Point Patterns for Herd Dispersion in a Savanna Wildlife Herbivore Community in Kenya  Diagnostic Analysis of SpaceTime Branching Processes for Earthquakes  Assessing Spatial Point Process Models Using Weighted Kfunctions: Analysis of California Earthquakes En línea: http://dx.doi.org/10.1007/0387311440 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34783 Ejemplares
Signatura Medio Ubicación Sublocalización Sección Estado ningún ejemplar Dependence in Probability and Statistics / SpringerLink (Online service) ; Patrice Bertail ; Soulier, Philippe ; Doukhan, Paul (2006)
Título : Dependence in Probability and Statistics Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Patrice Bertail ; Soulier, Philippe ; Doukhan, Paul Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Colección: Lecture Notes in Statistics, ISSN 09300325 num. 187 Número de páginas: VIII, 490 p. 40 illus Il.: online resource ISBN/ISSN/DL: 9780387360621 Idioma : Inglés (eng) Palabras clave: Statistics Probabilities Statistical Theory and Methods Probability Stochastic Processes Clasificación: 51 Matemáticas Resumen: This book gives a detailed account of some recent developments in the field of probability and statistics for dependent data. The book covers a wide range of topics from Markov chain theory and weak dependence with an emphasis on some recent developments on dynamical systems, to strong dependence in times series and random fields. A special section is devoted to statistical estimation problems and specific applications. The book is written as a succession of papers by some specialists of the field, alternating general surveys, mostly at a level accessible to graduate students in probability and statistics, and more general research papers mainly suitable to researchers in the field. The first part of the book considers some recent developments on weak dependent time series, including some new results for Markov chains as well as some developments on new notions of weak dependence. This part also intends to fill a gap between the probability and statistical literature and the dynamical system literature. The second part presents some new results on strong dependence with a special emphasis on nonlinear processes and random fields currently encountered in applications. Finally, in the last part, some general estimation problems are investigated, ranging from rate of convergence of maximum likelihood estimators to efficient estimation in parametric or nonparametric time series models, with an emphasis on applications with nonstationary data. Patrice Bertail is researcher in statistics at CRESTENSAE, Malakoff and Professor of Statistics at the UniversityParis X. Paul Doukhan is researcher in statistics at CRESTENSAE, Malakoff and Professor of Statistics at the University of CergyPontoise. Philippe Soulier is Professor of Statistics at the UniversityParis X Nota de contenido: Weak dependence and related concepts  Regenerationbased statistics for Harris recurrent Markov chains  Subgeometric ergodicity of Markov chains  Limit Theorems for Dependent Ustatistics  Recent results on weak dependence for causal sequences. Statistical applications to dynamical systems.  Parametrized KantorovichRubinštein theorem and application to the coupling of random variables  Exponential inequalities and estimation of conditional probabilities  Martingale approximation of non adapted stochastic processes with nonlinear growth of variance  Strong dependence  Almost periodically correlated processes with long memory  Long memory random fields  Long Memory in Nonlinear Processes  A LARCH(?) Vector Valued Process  On a Szegö type limit theorem and the asymptotic theory of random sums, integrals and quadratic forms  Aggregation of Doubly Stochastic Interactive Gaussian Processes and Toeplitz forms of UStatistics  Statistical Estimation and Applications  On Efficient Inference in GARCH Processes  Almost sure rate of convergence of maximum likelihood estimators for multidimensional diffusions  Convergence rates for density estimators of weakly dependent time series  Variograms for spatial maxstable random fields  A nonstationary paradigm for the dynamics of multivariate financial returns  Multivariate NonLinear Regression with Applications  Nonparametric estimator of a quantile function for the probability of event with repeated data En línea: http://dx.doi.org/10.1007/038736062X Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34825 Dependence in Probability and Statistics [documento electrónico] / SpringerLink (Online service) ; Patrice Bertail ; Soulier, Philippe ; Doukhan, Paul .  New York, NY : Springer New York, 2006 .  VIII, 490 p. 40 illus : online resource.  (Lecture Notes in Statistics, ISSN 09300325; 187) .
ISBN : 9780387360621
Idioma : Inglés (eng)
Palabras clave: Statistics Probabilities Statistical Theory and Methods Probability Stochastic Processes Clasificación: 51 Matemáticas Resumen: This book gives a detailed account of some recent developments in the field of probability and statistics for dependent data. The book covers a wide range of topics from Markov chain theory and weak dependence with an emphasis on some recent developments on dynamical systems, to strong dependence in times series and random fields. A special section is devoted to statistical estimation problems and specific applications. The book is written as a succession of papers by some specialists of the field, alternating general surveys, mostly at a level accessible to graduate students in probability and statistics, and more general research papers mainly suitable to researchers in the field. The first part of the book considers some recent developments on weak dependent time series, including some new results for Markov chains as well as some developments on new notions of weak dependence. This part also intends to fill a gap between the probability and statistical literature and the dynamical system literature. The second part presents some new results on strong dependence with a special emphasis on nonlinear processes and random fields currently encountered in applications. Finally, in the last part, some general estimation problems are investigated, ranging from rate of convergence of maximum likelihood estimators to efficient estimation in parametric or nonparametric time series models, with an emphasis on applications with nonstationary data. Patrice Bertail is researcher in statistics at CRESTENSAE, Malakoff and Professor of Statistics at the UniversityParis X. Paul Doukhan is researcher in statistics at CRESTENSAE, Malakoff and Professor of Statistics at the University of CergyPontoise. Philippe Soulier is Professor of Statistics at the UniversityParis X Nota de contenido: Weak dependence and related concepts  Regenerationbased statistics for Harris recurrent Markov chains  Subgeometric ergodicity of Markov chains  Limit Theorems for Dependent Ustatistics  Recent results on weak dependence for causal sequences. Statistical applications to dynamical systems.  Parametrized KantorovichRubinštein theorem and application to the coupling of random variables  Exponential inequalities and estimation of conditional probabilities  Martingale approximation of non adapted stochastic processes with nonlinear growth of variance  Strong dependence  Almost periodically correlated processes with long memory  Long memory random fields  Long Memory in Nonlinear Processes  A LARCH(?) Vector Valued Process  On a Szegö type limit theorem and the asymptotic theory of random sums, integrals and quadratic forms  Aggregation of Doubly Stochastic Interactive Gaussian Processes and Toeplitz forms of UStatistics  Statistical Estimation and Applications  On Efficient Inference in GARCH Processes  Almost sure rate of convergence of maximum likelihood estimators for multidimensional diffusions  Convergence rates for density estimators of weakly dependent time series  Variograms for spatial maxstable random fields  A nonstationary paradigm for the dynamics of multivariate financial returns  Multivariate NonLinear Regression with Applications  Nonparametric estimator of a quantile function for the probability of event with repeated data En línea: http://dx.doi.org/10.1007/038736062X Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34825 Ejemplares
Signatura Medio Ubicación Sublocalización Sección Estado ningún ejemplar
Título : Design of Experiments in Nonlinear Models : Asymptotic Normality, Optimality Criteria and SmallSample Properties Tipo de documento: documento electrónico Autores: Pronzato, Luc ; SpringerLink (Online service) ; Pázman, Andrej Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Lecture Notes in Statistics, ISSN 09300325 num. 212 Número de páginas: XV, 399 p. 56 illus., 37 illus. in color Il.: online resource ISBN/ISSN/DL: 9781461463634 Idioma : Inglés (eng) Palabras clave: Statistics for Life Sciences, Medicine, Health Sciences Statistics, general Social Science, Behavorial Education, Public Policy, and Law Clasificación: 51 Matemáticas Resumen: Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and SmallSample Properties provides a comprehensive coverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments. The first three chapters expose the connections between the asymptotic properties of estimators in parametric models and experimental design, with more emphasis than usual on some particular aspects like the estimation of a nonlinear function of the model parameters, models with heteroscedastic errors, etc. Classical optimality criteria based on those asymptotic properties are then presented thoroughly in a special chapter. Three chapters are dedicated to specific issues raised by nonlinear models. The construction of design criteria derived from nonasymptotic considerations (smallsample situation) is detailed. The connection between design and identifiability/estimability issues is investigated. Several approaches are presented to face the problem caused by the dependence of an optimal design on the value of the parameters to be estimated. A survey of algorithmic methods for the construction of optimal designs is provided Nota de contenido: Introduction  Asymptotic designs and uniform convergence. Asymptotic properties of the LS estimator  Asymptotic properties of M, ML and maximum a posteriori estimators  Local optimality criteria based on asymptotic normality  Criteria based on the smallsample precision of the LS estimator  Identifiability, estimability and extended optimality criteria  Nonlocal optimum design  Algorithms—a survey  Subdifferentials and subgradients  Computation of derivatives through sensitivity functions  Proofs  Symbols and notation  List of labeled assumptions  References En línea: http://dx.doi.org/10.1007/9781461463634 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32301 Design of Experiments in Nonlinear Models : Asymptotic Normality, Optimality Criteria and SmallSample Properties [documento electrónico] / Pronzato, Luc ; SpringerLink (Online service) ; Pázman, Andrej .  New York, NY : Springer New York : Imprint: Springer, 2013 .  XV, 399 p. 56 illus., 37 illus. in color : online resource.  (Lecture Notes in Statistics, ISSN 09300325; 212) .
ISBN : 9781461463634
Idioma : Inglés (eng)
Palabras clave: Statistics for Life Sciences, Medicine, Health Sciences Statistics, general Social Science, Behavorial Education, Public Policy, and Law Clasificación: 51 Matemáticas Resumen: Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and SmallSample Properties provides a comprehensive coverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments. The first three chapters expose the connections between the asymptotic properties of estimators in parametric models and experimental design, with more emphasis than usual on some particular aspects like the estimation of a nonlinear function of the model parameters, models with heteroscedastic errors, etc. Classical optimality criteria based on those asymptotic properties are then presented thoroughly in a special chapter. Three chapters are dedicated to specific issues raised by nonlinear models. The construction of design criteria derived from nonasymptotic considerations (smallsample situation) is detailed. The connection between design and identifiability/estimability issues is investigated. Several approaches are presented to face the problem caused by the dependence of an optimal design on the value of the parameters to be estimated. A survey of algorithmic methods for the construction of optimal designs is provided Nota de contenido: Introduction  Asymptotic designs and uniform convergence. Asymptotic properties of the LS estimator  Asymptotic properties of M, ML and maximum a posteriori estimators  Local optimality criteria based on asymptotic normality  Criteria based on the smallsample precision of the LS estimator  Identifiability, estimability and extended optimality criteria  Nonlocal optimum design  Algorithms—a survey  Subdifferentials and subgradients  Computation of derivatives through sensitivity functions  Proofs  Symbols and notation  List of labeled assumptions  References En línea: http://dx.doi.org/10.1007/9781461463634 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32301 Ejemplares
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