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Título : Compstat 2006 - Proceedings in Computational Statistics : 17th Symposium Held in Rome, Italy, 2006 Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Alfredo Rizzi ; Maurizio Vichi Editorial: Heidelberg : Physica-Verlag HD Fecha de publicación: 2006 Número de páginas: XXV, 537 p Il.: online resource ISBN/ISSN/DL: 978-3-7908-1709-6 Idioma : Inglés (eng) Palabras clave: Mathematics Mathematical statistics Database management Information storage and retrieval Computer software Probabilities Statistics Probability Theory Stochastic Processes Software Management Computing/Statistics Programs Storage Retrieval in Science Clasificación: 51 Matemáticas Resumen: International Association for Statistical Computing The International Association for Statistical Computing (IASC) is a Section of the International Statistical Institute. The objectives of the Association are to foster world-wide interest in e?ective statistical computing and to - change technical knowledge through international contacts and meetings - tween statisticians, computing professionals, organizations, institutions, g- ernments and the general public. The IASC organises its own Conferences, IASC World Conferences, and COMPSTAT in Europe. The 17th Conference of ERS-IASC, the biennial meeting of European - gional Section of the IASC was held in Rome August 28 - September 1, 2006. This conference took place in Rome exactly 20 years after the 7th COMP- STAT symposium which was held in Rome, in 1986. Previous COMPSTAT conferences were held in: Vienna (Austria, 1974); West-Berlin (Germany, 1976); Leiden (The Netherlands, 1978); Edimbourgh (UK, 1980); Toulouse (France, 1982); Prague (Czechoslovakia, 1984); Rome (Italy, 1986); Copenhagen (Denmark, 1988); Dubrovnik (Yugoslavia, 1990); Neuch atel (Switzerland, 1992); Vienna (Austria,1994); Barcelona (Spain, 1996);Bristol(UK,1998);Utrecht(TheNetherlands,2000);Berlin(Germany, 2002); Prague (Czech Republic, 2004) Nota de contenido: Classification and Clustering -- Issues of robustness and high dimensionality in cluster analysis -- Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories -- Bootstrap methods for measuring classification uncertainty in latent class analysis -- A robust linear grouping algorithm -- Computing and using the deviance with classification trees -- Estimation procedures for the false discovery rate: a systematic comparison for microarray data -- A unifying model for biclustering -- Image Analysis and Signal Processing -- Non-rigid image registration using mutual information -- Musical audio analysis using sparse representations -- Robust correspondence recognition for computer vision -- Blind superresolution -- Analysis of Music Time Series -- Data Visualization -- Tying up the loose ends in simple, multiple, joint correspondence analysis -- 3 dimensional parallel coordinates plot and its use for variable selection -- Geospatial distribution of alcohol-related violence in Northern Virginia -- Visualization in comparative music research -- Exploratory modelling analysis: visualizing the value of variables -- Density estimation from streaming data using wavelets -- Multivariate Analysis -- Reducing conservatism of exact small-sample methods of inference for discrete data -- Symbolic data analysis: what is it? -- A dimensional reduction method for ordinal three-way contingency table -- Operator related to a data matrix: a survey -- Factor interval data analysis and its application -- Identifying excessively rounded or truncated data -- Statistical inference and data mining: false discoveries control -- Is ‘Which model . . .?’ the right question? -- Use of latent class regression models with a random intercept to remove the effects of the overall response rating level -- Discrete functional data analysis -- Self organizing MAPS: understanding, measuring and reducing variability -- Parameterization and estimation of path models for categorical data -- Latent class model with two latent variables for analysis of count data -- Web Based Teaching -- Challenges concerning web data mining -- e-Learning statistics — a selective review -- Quality assurance of web based e-Learning for statistical education -- Algorithms -- Genetic algorithms for building double threshold generalized autoregressive conditional heteroscedastic models of time series -- Nonparametric evaluation of matching noise -- Subset selection algorithm based on mutual information -- Visiting near-optimal solutions using local search algorithms -- The convergence of optimization based GARCH estimators: theory and application -- The stochastics of threshold accepting: analysis of an application to the uniform design problem -- Robustness -- Robust classification with categorical variables -- Multiple group linear discriminant analysis: robustness and error rate En línea: http://dx.doi.org/10.1007/978-3-7908-1709-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35039 Compstat 2006 - Proceedings in Computational Statistics : 17th Symposium Held in Rome, Italy, 2006 [documento electrónico] / SpringerLink (Online service) ; Alfredo Rizzi ; Maurizio Vichi . - Heidelberg : Physica-Verlag HD, 2006 . - XXV, 537 p : online resource.
ISBN : 978-3-7908-1709-6
Idioma : Inglés (eng)
Palabras clave: Mathematics Mathematical statistics Database management Information storage and retrieval Computer software Probabilities Statistics Probability Theory Stochastic Processes Software Management Computing/Statistics Programs Storage Retrieval in Science Clasificación: 51 Matemáticas Resumen: International Association for Statistical Computing The International Association for Statistical Computing (IASC) is a Section of the International Statistical Institute. The objectives of the Association are to foster world-wide interest in e?ective statistical computing and to - change technical knowledge through international contacts and meetings - tween statisticians, computing professionals, organizations, institutions, g- ernments and the general public. The IASC organises its own Conferences, IASC World Conferences, and COMPSTAT in Europe. The 17th Conference of ERS-IASC, the biennial meeting of European - gional Section of the IASC was held in Rome August 28 - September 1, 2006. This conference took place in Rome exactly 20 years after the 7th COMP- STAT symposium which was held in Rome, in 1986. Previous COMPSTAT conferences were held in: Vienna (Austria, 1974); West-Berlin (Germany, 1976); Leiden (The Netherlands, 1978); Edimbourgh (UK, 1980); Toulouse (France, 1982); Prague (Czechoslovakia, 1984); Rome (Italy, 1986); Copenhagen (Denmark, 1988); Dubrovnik (Yugoslavia, 1990); Neuch atel (Switzerland, 1992); Vienna (Austria,1994); Barcelona (Spain, 1996);Bristol(UK,1998);Utrecht(TheNetherlands,2000);Berlin(Germany, 2002); Prague (Czech Republic, 2004) Nota de contenido: Classification and Clustering -- Issues of robustness and high dimensionality in cluster analysis -- Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories -- Bootstrap methods for measuring classification uncertainty in latent class analysis -- A robust linear grouping algorithm -- Computing and using the deviance with classification trees -- Estimation procedures for the false discovery rate: a systematic comparison for microarray data -- A unifying model for biclustering -- Image Analysis and Signal Processing -- Non-rigid image registration using mutual information -- Musical audio analysis using sparse representations -- Robust correspondence recognition for computer vision -- Blind superresolution -- Analysis of Music Time Series -- Data Visualization -- Tying up the loose ends in simple, multiple, joint correspondence analysis -- 3 dimensional parallel coordinates plot and its use for variable selection -- Geospatial distribution of alcohol-related violence in Northern Virginia -- Visualization in comparative music research -- Exploratory modelling analysis: visualizing the value of variables -- Density estimation from streaming data using wavelets -- Multivariate Analysis -- Reducing conservatism of exact small-sample methods of inference for discrete data -- Symbolic data analysis: what is it? -- A dimensional reduction method for ordinal three-way contingency table -- Operator related to a data matrix: a survey -- Factor interval data analysis and its application -- Identifying excessively rounded or truncated data -- Statistical inference and data mining: false discoveries control -- Is ‘Which model . . .?’ the right question? -- Use of latent class regression models with a random intercept to remove the effects of the overall response rating level -- Discrete functional data analysis -- Self organizing MAPS: understanding, measuring and reducing variability -- Parameterization and estimation of path models for categorical data -- Latent class model with two latent variables for analysis of count data -- Web Based Teaching -- Challenges concerning web data mining -- e-Learning statistics — a selective review -- Quality assurance of web based e-Learning for statistical education -- Algorithms -- Genetic algorithms for building double threshold generalized autoregressive conditional heteroscedastic models of time series -- Nonparametric evaluation of matching noise -- Subset selection algorithm based on mutual information -- Visiting near-optimal solutions using local search algorithms -- The convergence of optimization based GARCH estimators: theory and application -- The stochastics of threshold accepting: analysis of an application to the uniform design problem -- Robustness -- Robust classification with categorical variables -- Multiple group linear discriminant analysis: robustness and error rate En línea: http://dx.doi.org/10.1007/978-3-7908-1709-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35039 Ejemplares
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Título : COMPSTAT 2008 : Proceedings in Computational Statistics Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Paula Brito Editorial: Heidelberg : Physica-Verlag HD Fecha de publicación: 2008 Número de páginas: XVII, 573 p Il.: online resource ISBN/ISSN/DL: 978-3-7908-2084-3 Idioma : Inglés (eng) Palabras clave: Mathematics Computers Mathematical statistics Information storage and retrieval Computer mathematics Probabilities Statistics Probability Theory Stochastic Processes Computational Numerical Analysis of Computation Computing/Statistics Programs Storage Retrieval in Science Clasificación: 51 Matemáticas Nota de contenido: Keynote -- Nonparametric Methods for Estimating Periodic Functions, with Applications in Astronomy -- Advances on Statistical Computing Environments -- Back to the Future: Lisp as a Base for a Statistical Computing System -- Computable Statistical Research and Practice -- Implicit and Explicit Parallel Computing in R -- Classification and Clustering of Complex Data -- Probabilistic Modeling for Symbolic Data -- Monothetic Divisive Clustering with Geographical Constraints -- Comparing Histogram Data Using a Mahalanobis–Wasserstein Distance -- Computation for Graphical Models and Bayes Nets -- Iterative Conditional Fitting for Discrete Chain Graph Models -- Graphical Models for Sparse Data: Graphical Gaussian Models with Vertex and Edge Symmetries -- Parameterization and Fitting of a Class of Discrete Graphical Models -- Computational Econometrics -- Exploring the Bootstrap Discrepancy -- On Diagnostic Checking Time Series Models with Portmanteau Test Statistics Based on Generalized Inverses and -- New Developments in Latent Variable Models: Non-linear and Dynamic Models -- Computational Statistics and Data Mining Methods for Alcohol Studies -- Estimating Spatiotemporal Effects for Ecological Alcohol Systems -- A Directed Graph Model of Ecological Alcohol Systems Incorporating Spatiotemporal Effects -- Spatial and Computational Models of Alcohol Use and Problems -- Finance and Insurance -- Optimal Investment for an Insurer with Multiple Risky Assets Under Mean-Variance Criterion -- Inhomogeneous Jump-GARCH Models with Applications in Financial Time Series Analysis -- The Classical Risk Model with Constant Interest and Threshold Strategy -- Estimation of Structural Parameters in Crossed Classification Credibility Model Using Linear Mixed Models -- Information Retrieval for Text and Images -- A Hybrid Approach for Taxonomy Learning from Text -- Image and Image-Set Modeling Using a Mixture Model -- Strategies in Identifying Issues Addressed in Legal Reports -- Knowledge Extraction by Models -- Sequential Automatic Search of a Subset of Classifiers in Multiclass Learning -- Possibilistic PLS Path Modeling: A New Approach to the Multigroup Comparison -- Models for Understanding Versus Models for Prediction -- Posterior Prediction Modelling of Optimal Trees -- Model Selection Algorithms -- Selecting Models Focussing on the Modeller’s Purpose -- A Regression Subset-Selection Strategy for Fat-Structure Data -- Fast Robust Variable Selection -- Models for Latent Class Detection -- Latent Classes of Objects and Variable Selection -- Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models -- Clustering via Mixture Regression Models with Random Effects -- Multiple Testing Procedures -- Testing Effects in ANOVA Experiments: Direct Combination of All Pair-Wise Comparisons Using Constrained Synchronized Permutations -- Multiple Comparison Procedures in Linear Models -- Inference for the Top-k Rank List Problem -- Random Search Algorithms -- Monitoring Random Start Forward Searches for Multivariate Data -- Generalized Differential Evolution for General Non-Linear Optimization -- Statistical Properties of Differential Evolution and Related Random Search Algorithms -- Robust Statistics -- Robust Estimation of the Vector Autoregressive Model by a Least Trimmed Squares Procedure -- The Choice of the Initial Estimate for Computing MM-Estimates -- Metropolis Versus Simulated Annealing and the Black-Box-Complexity of Optimization Problems -- Signal Extraction and Filtering -- Filters for Short Nonstationary Sequences: The Analysis of the Business Cycle -- Estimation of Common Factors Under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and Its Main Components En línea: http://dx.doi.org/10.1007/978-3-7908-2084-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34427 COMPSTAT 2008 : Proceedings in Computational Statistics [documento electrónico] / SpringerLink (Online service) ; Paula Brito . - Heidelberg : Physica-Verlag HD, 2008 . - XVII, 573 p : online resource.
ISBN : 978-3-7908-2084-3
Idioma : Inglés (eng)
Palabras clave: Mathematics Computers Mathematical statistics Information storage and retrieval Computer mathematics Probabilities Statistics Probability Theory Stochastic Processes Computational Numerical Analysis of Computation Computing/Statistics Programs Storage Retrieval in Science Clasificación: 51 Matemáticas Nota de contenido: Keynote -- Nonparametric Methods for Estimating Periodic Functions, with Applications in Astronomy -- Advances on Statistical Computing Environments -- Back to the Future: Lisp as a Base for a Statistical Computing System -- Computable Statistical Research and Practice -- Implicit and Explicit Parallel Computing in R -- Classification and Clustering of Complex Data -- Probabilistic Modeling for Symbolic Data -- Monothetic Divisive Clustering with Geographical Constraints -- Comparing Histogram Data Using a Mahalanobis–Wasserstein Distance -- Computation for Graphical Models and Bayes Nets -- Iterative Conditional Fitting for Discrete Chain Graph Models -- Graphical Models for Sparse Data: Graphical Gaussian Models with Vertex and Edge Symmetries -- Parameterization and Fitting of a Class of Discrete Graphical Models -- Computational Econometrics -- Exploring the Bootstrap Discrepancy -- On Diagnostic Checking Time Series Models with Portmanteau Test Statistics Based on Generalized Inverses and -- New Developments in Latent Variable Models: Non-linear and Dynamic Models -- Computational Statistics and Data Mining Methods for Alcohol Studies -- Estimating Spatiotemporal Effects for Ecological Alcohol Systems -- A Directed Graph Model of Ecological Alcohol Systems Incorporating Spatiotemporal Effects -- Spatial and Computational Models of Alcohol Use and Problems -- Finance and Insurance -- Optimal Investment for an Insurer with Multiple Risky Assets Under Mean-Variance Criterion -- Inhomogeneous Jump-GARCH Models with Applications in Financial Time Series Analysis -- The Classical Risk Model with Constant Interest and Threshold Strategy -- Estimation of Structural Parameters in Crossed Classification Credibility Model Using Linear Mixed Models -- Information Retrieval for Text and Images -- A Hybrid Approach for Taxonomy Learning from Text -- Image and Image-Set Modeling Using a Mixture Model -- Strategies in Identifying Issues Addressed in Legal Reports -- Knowledge Extraction by Models -- Sequential Automatic Search of a Subset of Classifiers in Multiclass Learning -- Possibilistic PLS Path Modeling: A New Approach to the Multigroup Comparison -- Models for Understanding Versus Models for Prediction -- Posterior Prediction Modelling of Optimal Trees -- Model Selection Algorithms -- Selecting Models Focussing on the Modeller’s Purpose -- A Regression Subset-Selection Strategy for Fat-Structure Data -- Fast Robust Variable Selection -- Models for Latent Class Detection -- Latent Classes of Objects and Variable Selection -- Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models -- Clustering via Mixture Regression Models with Random Effects -- Multiple Testing Procedures -- Testing Effects in ANOVA Experiments: Direct Combination of All Pair-Wise Comparisons Using Constrained Synchronized Permutations -- Multiple Comparison Procedures in Linear Models -- Inference for the Top-k Rank List Problem -- Random Search Algorithms -- Monitoring Random Start Forward Searches for Multivariate Data -- Generalized Differential Evolution for General Non-Linear Optimization -- Statistical Properties of Differential Evolution and Related Random Search Algorithms -- Robust Statistics -- Robust Estimation of the Vector Autoregressive Model by a Least Trimmed Squares Procedure -- The Choice of the Initial Estimate for Computing MM-Estimates -- Metropolis Versus Simulated Annealing and the Black-Box-Complexity of Optimization Problems -- Signal Extraction and Filtering -- Filters for Short Nonstationary Sequences: The Analysis of the Business Cycle -- Estimation of Common Factors Under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and Its Main Components En línea: http://dx.doi.org/10.1007/978-3-7908-2084-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34427 Ejemplares
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Título : Computational Statistics Tipo de documento: documento electrónico Autores: James E. Gentle ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2009 Colección: Statistics and Computing, ISSN 1431-8784 Número de páginas: XXII, 728 p Il.: online resource ISBN/ISSN/DL: 978-0-387-98144-4 Idioma : Inglés (eng) Palabras clave: Mathematics Computer science Numerical analysis Data mining mathematics Probabilities Statistics Probability Theory and Stochastic Processes Computational Analysis of Computing Computing/Statistics Programs Numeric Mining Knowledge Discovery Clasificación: 51 Matemáticas Resumen: Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra Nota de contenido: Preliminaries -- Mathematical and Statistical Preliminaries -- Statistical Computing -- Computer Storage and Arithmetic -- Algorithms and Programming -- Approximation of Functions and Numerical Quadrature -- Numerical Linear Algebra -- Solution of Nonlinear Equations and Optimization -- Generation of Random Numbers -- Methods of Computational Statistics -- Graphical Methods in Computational Statistics -- Tools for Identification of Structure in Data -- Estimation of Functions -- Monte Carlo Methods for Statistical Inference -- Data Randomization, Partitioning, and Augmentation -- Bootstrap Methods -- Exploring Data Density and Relationships -- Estimation of Probability Density Functions Using Parametric Models -- Nonparametric Estimation of Probability Density Functions -- Statistical Learning and Data Mining -- Statistical Models of Dependencies En línea: http://dx.doi.org/10.1007/978-0-387-98144-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33931 Computational Statistics [documento electrónico] / James E. Gentle ; SpringerLink (Online service) . - New York, NY : Springer New York, 2009 . - XXII, 728 p : online resource. - (Statistics and Computing, ISSN 1431-8784) .
ISBN : 978-0-387-98144-4
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer science Numerical analysis Data mining mathematics Probabilities Statistics Probability Theory and Stochastic Processes Computational Analysis of Computing Computing/Statistics Programs Numeric Mining Knowledge Discovery Clasificación: 51 Matemáticas Resumen: Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra Nota de contenido: Preliminaries -- Mathematical and Statistical Preliminaries -- Statistical Computing -- Computer Storage and Arithmetic -- Algorithms and Programming -- Approximation of Functions and Numerical Quadrature -- Numerical Linear Algebra -- Solution of Nonlinear Equations and Optimization -- Generation of Random Numbers -- Methods of Computational Statistics -- Graphical Methods in Computational Statistics -- Tools for Identification of Structure in Data -- Estimation of Functions -- Monte Carlo Methods for Statistical Inference -- Data Randomization, Partitioning, and Augmentation -- Bootstrap Methods -- Exploring Data Density and Relationships -- Estimation of Probability Density Functions Using Parametric Models -- Nonparametric Estimation of Probability Density Functions -- Statistical Learning and Data Mining -- Statistical Models of Dependencies En línea: http://dx.doi.org/10.1007/978-0-387-98144-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33931 Ejemplares
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Título : Handbook of Computational Statistics : Concepts and Methods Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; James E. Gentle ; Wolfgang Karl Härdle ; Yuichi Mori Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: Springer Handbooks of Computational Statistics Número de páginas: XII, 1192 p. 297 illus., 96 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-642-21551-3 Idioma : Inglés (eng) Palabras clave: Statistics and Computing/Statistics Programs Statistics, general Statistical Theory Methods Clasificación: 51 Matemáticas Resumen: The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications Nota de contenido: Part I Computational Statistics -- Part II Statistical Computing -- Part III Statistical Methodology -- Part IV Selected Applications En línea: http://dx.doi.org/10.1007/978-3-642-21551-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32916 Handbook of Computational Statistics : Concepts and Methods [documento electrónico] / SpringerLink (Online service) ; James E. Gentle ; Wolfgang Karl Härdle ; Yuichi Mori . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012 . - XII, 1192 p. 297 illus., 96 illus. in color : online resource. - (Springer Handbooks of Computational Statistics) .
ISBN : 978-3-642-21551-3
Idioma : Inglés (eng)
Palabras clave: Statistics and Computing/Statistics Programs Statistics, general Statistical Theory Methods Clasificación: 51 Matemáticas Resumen: The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications Nota de contenido: Part I Computational Statistics -- Part II Statistical Computing -- Part III Statistical Methodology -- Part IV Selected Applications En línea: http://dx.doi.org/10.1007/978-3-642-21551-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32916 Ejemplares
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Título : Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing Tipo de documento: documento electrónico Autores: Daniela Calvetti ; SpringerLink (Online service) ; Erkki Somersalo Editorial: New York, NY : Springer New York Fecha de publicación: 2007 Colección: Surveys and Tutorials in the Applied Mathematical Sciences num. 2 Número de páginas: XIV, 202 p Il.: online resource ISBN/ISSN/DL: 978-0-387-73394-4 Idioma : Inglés (eng) Palabras clave: Computer science Computers mathematics Probabilities Statistics Science Theory of Computation Computational and Engineering Computing/Statistics Programs Mathematics Numerical Analysis Probability Stochastic Processes Clasificación: 51 Matemáticas Resumen: A combination of the concepts subjective – or Bayesian – statistics and scientific computing, the book provides an integrated view across numerical linear algebra and computational statistics. Inverse problems act as the bridge between these two fields where the goal is to estimate an unknown parameter that is not directly observable by using measured data and a mathematical model linking the observed and the unknown. Inverse problems are closely related to statistical inference problems, where the observations are used to infer on an underlying probability distribution. This connection between statistical inference and inverse problems is a central topic of the book. Inverse problems are typically ill-posed: small uncertainties in data may propagate in huge uncertainties in the estimates of the unknowns. To cope with such problems, efficient regularization techniques are developed in the framework of numerical analysis. The counterpart of regularization in the framework of statistical inference is the use prior information. This observation opens the door to a fruitful interplay between statistics and numerical analysis: the statistical framework provides a rich source of methods that can be used to improve the quality of solutions in numerical analysis, and vice versa, the efficient numerical methods bring computational efficiency to the statistical inference problems. This book is intended as an easily accessible reader for those who need numerical and statistical methods in applied sciences. Nota de contenido: Inverse problems and subjective computing -- Basic problem of statistical inference -- The praise of ignorance: randomness as lack of information -- Basic problem in numerical linear algebra -- Sampling: first encounter -- Statistically inspired preconditioners -- Conditional Gaussian densities and predictive envelopes -- More applications of the Gaussian conditioning -- Sampling: the real thing -- Wrapping up: hypermodels, dynamic priorconditioners and Bayesian learning En línea: http://dx.doi.org/10.1007/978-0-387-73394-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34537 Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing [documento electrónico] / Daniela Calvetti ; SpringerLink (Online service) ; Erkki Somersalo . - New York, NY : Springer New York, 2007 . - XIV, 202 p : online resource. - (Surveys and Tutorials in the Applied Mathematical Sciences; 2) .
ISBN : 978-0-387-73394-4
Idioma : Inglés (eng)
Palabras clave: Computer science Computers mathematics Probabilities Statistics Science Theory of Computation Computational and Engineering Computing/Statistics Programs Mathematics Numerical Analysis Probability Stochastic Processes Clasificación: 51 Matemáticas Resumen: A combination of the concepts subjective – or Bayesian – statistics and scientific computing, the book provides an integrated view across numerical linear algebra and computational statistics. Inverse problems act as the bridge between these two fields where the goal is to estimate an unknown parameter that is not directly observable by using measured data and a mathematical model linking the observed and the unknown. Inverse problems are closely related to statistical inference problems, where the observations are used to infer on an underlying probability distribution. This connection between statistical inference and inverse problems is a central topic of the book. Inverse problems are typically ill-posed: small uncertainties in data may propagate in huge uncertainties in the estimates of the unknowns. To cope with such problems, efficient regularization techniques are developed in the framework of numerical analysis. The counterpart of regularization in the framework of statistical inference is the use prior information. This observation opens the door to a fruitful interplay between statistics and numerical analysis: the statistical framework provides a rich source of methods that can be used to improve the quality of solutions in numerical analysis, and vice versa, the efficient numerical methods bring computational efficiency to the statistical inference problems. This book is intended as an easily accessible reader for those who need numerical and statistical methods in applied sciences. Nota de contenido: Inverse problems and subjective computing -- Basic problem of statistical inference -- The praise of ignorance: randomness as lack of information -- Basic problem in numerical linear algebra -- Sampling: first encounter -- Statistically inspired preconditioners -- Conditional Gaussian densities and predictive envelopes -- More applications of the Gaussian conditioning -- Sampling: the real thing -- Wrapping up: hypermodels, dynamic priorconditioners and Bayesian learning En línea: http://dx.doi.org/10.1007/978-0-387-73394-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34537 Ejemplares
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