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Computational statistics handbook with MATLAB:

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Hauptverfasser:	Martinez, Wendy L. 1953- (VerfasserIn), Martinez, Angel R. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Boca Raton [u.a.] Chapman & Hall, CRC 2008
Ausgabe:	2. ed.
Schriftenreihe:	Computer science and data analysis series 8
Schlagworte:	MATLAB (logiciel) Statistique mathématique - Informatique Statistique mathématique MATLAB Statistik
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XXIII, 767 S. graph. Darst.
ISBN:	9781584885665

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Datensatz im Suchindex

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adam_text	Table of Contents Preface to the Second Edition . xvii Preface to the First Edition .xxi Chapter 1 Introduction 1.1 What Is Computational Statistics? .1 1.2 An Overview of the Book .2 Philosophy .2 What Is Covered .3 A Word About Notation .5 1.3 MATLAB® Code .6 Computational Statistics Toolbox .7 Internet Resources .8 1.4 Further Reading .9 Chapter 2 Probability Concepts 2.1 Introduction .11 2.2 Probability .12 Background . 12 Probability .14 Axioms of Probability . 17 2.3 Conditional Probability and Independence .17 Conditional Probability .17 Independence .18 Bayes'Theorem .19 2.4 Expectation .21 Mean and Variance .21 Skewness .23 Kurtosis .23 2.5 Common Distributions .24 Binomial .24 Poisson .26 Uniform .29 Normal .31 vu viii Computational Statistics Handbook with MATLAB®, 2nd Edition Exponential .34 Gamma .36 Chi-Square .37 Weibull .38 Beta .40 Student's t Distribution .41 Multivariate Normal .44 Multivariate t Distribution .47 2.6 MATLAB® Code .48 2.7 Further Reading .49 Exercises .52 Chapter 3 Sampling Concepts 3.1 Introduction .55 3.2 Sampling Terminology and Concepts .55 Sample Mean and Sample Variance .57 Sample Moments .58 Covariance .60 3.3 Sampling Distributions .63 3.4 Parameter Estimation .65 Bias .66 Mean Squared Error .66 Relative Efficiency .67 Standard Error .67 Maximum Likelihood Estimation .68 Method of Moments .71 3.5 Empirical Distribution Function .72 Quantiles .74 3.6 MATLAB® Code .77 3.7 Further Reading .78 Exercises .80 Chapter 4 Generating Random Variables 4.1 Introduction .83 4.2 General Techniques for Generating Random Variables .83 Uniform Random Numbers .83 Inverse Transform Method .86 Acceptance-Rejection Method .89 4.3 Generating Continuous Random Variables .93 Normal Distribution .93 Exponential Distribution .94 Gamma .95 Table of Contents ix Chi-Square.98 Beta .99 Multivariate Normal . 101 Multivariate Student's t Distribution .103 Generating Variâtes on a Sphere . 104 4.4 Generating Discrete Random Variables .107 Binomial .107 Poisson . 108 Discrete Uniform . Ill 4.5 MATLAB® Code .112 4.6 Further Reading .113 Exercises .115 Chapter 5 Exploratory Data Analysis 5.1 Introduction .117 5.2 Exploring Univariate Data .119 Histograms . 119 Stem-and-Leaf .122 Quantile-Based Plots - Continuous Distributions .124 Quantile Plots - Discrete Distributions .132 Box Plots .138 5.3 Exploring Bivariate and Trivariate Data .145 Scatterplots .145 Surface Plots .146 Contour Plots . 148 Bivariate Histogram . 149 3-D Scatterplot .155 5.4 Exploring Multi-Dimensional Data .158 Scatterplot Matrix .158 Slices and Isosurfaces . 160 Glyphs .166 Andrews Curves .168 Parallel Coordinates .172 5.5 MATLAB® Code .179 5.6 Further Reading .181 Exercises .183 Chapter 6 Finding Structure 6.1 Introduction .187 6.2 Projecting Data .188 6.3 Principal Component Analysis .190 6.4 Projection Pursuit EDA .195 x Computational Statistics Handbook with MATLAB®, 2nd Edition Projection Pursuit Index . 197 Finding the Structure . 198 Structure Removal . 199 6.5 Independent Component Analysis .204 6.6 Grand Tour .211 6.7 Nonlinear Dimensionality Reduction .216 Multidimensional Scaling .216 Isometric Feature Mapping - ISOMAP.220 6.8 MATLAB® Code .224 6.9 Further Reading .227 Exercises .230 Chapter 7 Monte Carlo Methods for Inferential Statistics 7.1 Introduction .233 7.2 Classical Inferential Statistics .234 Hypothesis Testing .234 Confidence Intervals .243 7.3 Monte Carlo Methods for Inferential Statistics .246 Basic Monte Carlo Procedure .246 Monte Carlo Hypothesis Testing .247 Monte Carlo Assessment of Hypothesis Testing .252 7.4 Bootstrap Methods .256 General Bootstrap Methodology .256 Bootstrap Estimate of Standard Error .258 Bootstrap Estimate of Bias .260 Bootstrap Confidence Intervals .262 7.5 MATLAB® Code .268 7.6 Further Reading .269 Exercises .271 Chapter 8 Data Partitioning 8.1 Introduction .273 8.2 Cross-Validation .274 8.3 Jackknife .281 8.4 Better Bootstrap Confidence Intervals .289 8.5 Jackknife-After-Bootstrap .293 8.6 MATLAB® Code .295 8.7 Further Reading .296 Exercises .298 Table of Contents xi Chapter 9 Probability Density Estimation 9.1 Introduction .301 9.2 Histograms .303 1-D Histograms . 303 Multivariate Histograms .309 Frequency Polygons .311 Averaged Shifted Histograms .316 9.3 Kernel Density Estimation .322 Univariate Kernel Estimators .322 Multivariate Kernel Estimators .327 9.4 Finite Mixtures .329 Univariate Finite Mixtures .331 Visualizing Finite Mixtures .333 Multivariate Finite Mixtures .335 EM Algorithm for Estimating the Parameters .338 Adaptive Mixtures .343 9.5 Generating Random Variables .348 9.6 MATLAB® Code .356 9.7 Further Reading .357 Exercises .359 Chapter 10 Supervised Learning 10.1 Introduction .363 10.2 Bayes Decision Theory .365 Estimating Class-Conditional Probabilities: Parametric Method .367 Estimating Class-Conditional Probabilities: Nonparametric .369 Bayes Decision Rule .370 Likelihood Ratio Approach .377 10.3 Evaluating the Classifier .380 Independent Test Sample .380 Cross-Validatíon .382 Receiver Operating Characteristic (ROC) Curve .385 10.4 Classification Trees .390 Growing the Tree .394 Pruning the Tree .399 Choosing the Best Tree .403 Other Tree Methods .412 10.5 Combining Classifiers .414 Bagging .415 Boosting .417 Arcing Classifiers .420 Random Forests .422 10.6 MATLAB® Code .423 xii Computational Statistics Handbook with MATLAB®, 2nd Edition 10.7 Further Reading .424 Exercises .428 Chapter 11 Unsupervised Learning 11.1 Introduction .431 11.2 Measures of Distance .432 11.3 Hierarchical Clustering .434 11.4 K-Means Clustering .442 11.5 Model-Based Clustering .445 Finite Mixture Models and the EM Algorithm .446 Model-Based Agglomerative Clustering .450 Bayesian Information Criterion .453 Model-Based Clustering Procedure .453 11.6 Assessing Cluster Results .458 Mojena - Upper Tail Rule .458 Silhouette Statistic .459 Other Methods for Evaluating Clusters .462 11.7 MATLAB® Code .465 11.8 Further Reading .466 Exercises .469 Chapter 12 Parametric Models 12.1 Introduction .471 12.2 Spline Regression Models .477 12.3 Logistic Regression .482 Creating the Model .482 Interpreting the Model Parameters .487 12.4 Generalized Linear Models .488 Exponential Family Form .489 Generalized Linear Model .494 Model Checking .498 12.5 MATLAB® Code .508 12.6 Further Reading .509 Exercises .511 Chapter 13 Nonparametric Models 13.1 Introduction .513 13.2 Some Smoothing Methods .514 Bin Smoothing .515 Running Mean .517 Table of Contents хщ Running Line .518 Local Polynomial Regression - Loess .519 Robust Loess .525 13.3 Kernel Methods .528 Nadaraya-Watson Estimator .531 Local Linear Kernel Estimator .532 13.4 Smoothing Splines .534 Natural Cubic Splines .536 Reinsch Method for Finding Smoothing Splines .537 Values for a Cubic Smoothing Spline .540 Weighted Smoothing Spline .540 13.5 Nonparametric Regression - Other Details .542 Choosing the Smoothing Parameter .542 Estimation of the Residual Variance .547 Variability of Smooths .548 13.6 Regression Trees .551 Growing a Regression Tree .553 Pruning a Regression Tree .557 Selecting a Tree .557 13.7 Additive Models .563 13.8 MATLAB® Code .567 13.9 Further Reading .570 Exercises .573 Chapter 14 Markov Chain Monte Carlo Methods 14.1 Introduction .575 14.2 Background .576 Bayesian Inference .576 Monte Carlo Integration .577 Markov Chains .579 Analyzing the Output .580 14.3 Metropolis-Hastings Algorithms .580 Metropolis-Hastings Sampler .581 Metropolis Sampler .584 Independence Sampler .587 Autoregressive Generating Density .589 14.4 The Gibbs Sampler .592 14.5 Convergence Monitoring .602 Gelman and Rubin Method .604 Raftery and Lewis Method .607 14.6 MATLAB® Code .609 14.7 Further Reading .610 Exercises .,.612 xiv Computational Statistics Handbook with MATLAB®, 2nd Edition Chapter 15 Spatial Statistics 15.1 Introduction .617 What Is Spatial Statistics? . 617 Types of Spatial Data .618 Spatial Point Patterns .619 Complete Spatial Randomness . 621 15.2 Visualizing Spatial Point Processes .623 15.3 Exploring First-order and Second-order Properties .627 Estimating the Intensity . 627 Estimating the Spatial Dependence . 630 15.4 Modeling Spatial Point Processes .638 Nearest Neighbor Distances .638 K-Function . 643 15.5 Simulating Spatial Point Processes .646 Homogeneous Poisson Process .647 Binomial Process .650 Poisson Cluster Process .651 Inhibition Process .654 Strauss Process . 656 15.6 MATLAB® Code .658 15.7 Further Reading .659 Exercises .661 Appendix A Introduction to MATLAB® A.1 What Is MATLAB®? .663 A.2 Getting Help in MATLAB® .664 A.3 File and Workspace Management .664 A.4 Punctuation in MATLAB® .666 A.5 Arithmetic Operators .666 A.6 Data Constructs in MATLAB® .668 Basic Data Constructs .668 Building Arrays .668 Cell Arrays .669 A.7 Script Files and Functions .670 A.8 Control Flow .672 For Loop .672 While Loop .672 If-Else Statements .673 Switch Statement .673 A.9 Simple Plotting .673 A.10 Contact Information .676 Table of Contents xv Appendix В Projection Pursuit Indexes B.l Indexes .677 Friedman-Tukey Index.677 Entropy Index.678 Moment Index.678 L2 Distances .679 Б.2 MATLAB® Source Code .680 Appendix С MATLAB® Statistics Toolbox File I/O .687 Dataset Arrays .687 Grouped Data .687 Descriptive Statistics .688 Statistical Visualization .688 Probability Density Functions .689 Cumulative Distribution Functions .690 Inverse Cumulative Distribution Functions .691 Distribution Statistics Functions .691 Distribution Fitting Functions .692 Negative Log-Likelihood Functions .692 Random Number Generators .693 Hypothesis Tests .694 Analysis of Variance .694 Regression Analysis .694 Multivariate Methods .695 Cluster Analysis .696 Classification .696 Markov Models .696 Design of Experiments .697 Statistical Process Control .697 Graphical User Interfaces .697 Appendix D Computational Statistics Toolbox Probability Distributions .699 Statistics .699 Random Number Generation .700 Exploratory Data Analysis .700 Bootstrap and Jackknife .701 Probability Density Estimation .701 Supervised Learning .701 Unsupervised Learning .701 xvi Computational Statistics Handbook with MATLAB®, 2m Edition Parametric and Nonparametric Models .702 Markov Chain Monte Carlo .702 Spatial Statistics .702 Appendix E Exploratory Data Analysis Toolboxes E.I Introduction .703 E.2 Exploratory Data Analysis Toolbox .704 E.3 EDA GUI Toolbox .705 Appendix F Data Sets Introduction .719 Appendix G Notation Overview .727 Observed Data .727 Greek Letters .728 Functions and Distributions .728 Matrix Notation .729 Statistics .729 References .731 Author Index .751 Subject Index .757
adam_txt	Table of Contents Preface to the Second Edition . xvii Preface to the First Edition .xxi Chapter 1 Introduction 1.1 What Is Computational Statistics? .1 1.2 An Overview of the Book .2 Philosophy .2 What Is Covered .3 A Word About Notation .5 1.3 MATLAB® Code .6 Computational Statistics Toolbox .7 Internet Resources .8 1.4 Further Reading .9 Chapter 2 Probability Concepts 2.1 Introduction .11 2.2 Probability .12 Background . 12 Probability .14 Axioms of Probability . 17 2.3 Conditional Probability and Independence .17 Conditional Probability .17 Independence .18 Bayes'Theorem .19 2.4 Expectation .21 Mean and Variance .21 Skewness .23 Kurtosis .23 2.5 Common Distributions .24 Binomial .24 Poisson .26 Uniform .29 Normal .31 vu viii Computational Statistics Handbook with MATLAB®, 2nd Edition Exponential .34 Gamma .36 Chi-Square .37 Weibull .38 Beta .40 Student's t Distribution .41 Multivariate Normal .44 Multivariate t Distribution .47 2.6 MATLAB® Code .48 2.7 Further Reading .49 Exercises .52 Chapter 3 Sampling Concepts 3.1 Introduction .55 3.2 Sampling Terminology and Concepts .55 Sample Mean and Sample Variance .57 Sample Moments .58 Covariance .60 3.3 Sampling Distributions .63 3.4 Parameter Estimation .65 Bias .66 Mean Squared Error .66 Relative Efficiency .67 Standard Error .67 Maximum Likelihood Estimation .68 Method of Moments .71 3.5 Empirical Distribution Function .72 Quantiles .74 3.6 MATLAB® Code .77 3.7 Further Reading .78 Exercises .80 Chapter 4 Generating Random Variables 4.1 Introduction .83 4.2 General Techniques for Generating Random Variables .83 Uniform Random Numbers .83 Inverse Transform Method .86 Acceptance-Rejection Method .89 4.3 Generating Continuous Random Variables .93 Normal Distribution .93 Exponential Distribution .94 Gamma .95 Table of Contents ix Chi-Square.98 Beta .99 Multivariate Normal . 101 Multivariate Student's t Distribution .103 Generating Variâtes on a Sphere . 104 4.4 Generating Discrete Random Variables .107 Binomial .107 Poisson . 108 Discrete Uniform . Ill 4.5 MATLAB® Code .112 4.6 Further Reading .113 Exercises .115 Chapter 5 Exploratory Data Analysis 5.1 Introduction .117 5.2 Exploring Univariate Data .119 Histograms . 119 Stem-and-Leaf .122 Quantile-Based Plots - Continuous Distributions .124 Quantile Plots - Discrete Distributions .132 Box Plots .138 5.3 Exploring Bivariate and Trivariate Data .145 Scatterplots .145 Surface Plots .146 Contour Plots . 148 Bivariate Histogram . 149 3-D Scatterplot .155 5.4 Exploring Multi-Dimensional Data .158 Scatterplot Matrix .158 Slices and Isosurfaces . 160 Glyphs .166 Andrews Curves .168 Parallel Coordinates .172 5.5 MATLAB® Code .179 5.6 Further Reading .181 Exercises .183 Chapter 6 Finding Structure 6.1 Introduction .187 6.2 Projecting Data .188 6.3 Principal Component Analysis .190 6.4 Projection Pursuit EDA .195 x Computational Statistics Handbook with MATLAB®, 2nd Edition Projection Pursuit Index . 197 Finding the Structure . 198 Structure Removal . 199 6.5 Independent Component Analysis .204 6.6 Grand Tour .211 6.7 Nonlinear Dimensionality Reduction .216 Multidimensional Scaling .216 Isometric Feature Mapping - ISOMAP.220 6.8 MATLAB® Code .224 6.9 Further Reading .227 Exercises .230 Chapter 7 Monte Carlo Methods for Inferential Statistics 7.1 Introduction .233 7.2 Classical Inferential Statistics .234 Hypothesis Testing .234 Confidence Intervals .243 7.3 Monte Carlo Methods for Inferential Statistics .246 Basic Monte Carlo Procedure .246 Monte Carlo Hypothesis Testing .247 Monte Carlo Assessment of Hypothesis Testing .252 7.4 Bootstrap Methods .256 General Bootstrap Methodology .256 Bootstrap Estimate of Standard Error .258 Bootstrap Estimate of Bias .260 Bootstrap Confidence Intervals .262 7.5 MATLAB® Code .268 7.6 Further Reading .269 Exercises .271 Chapter 8 Data Partitioning 8.1 Introduction .273 8.2 Cross-Validation .274 8.3 Jackknife .281 8.4 Better Bootstrap Confidence Intervals .289 8.5 Jackknife-After-Bootstrap .293 8.6 MATLAB® Code .295 8.7 Further Reading .296 Exercises .298 Table of Contents xi Chapter 9 Probability Density Estimation 9.1 Introduction .301 9.2 Histograms .303 1-D Histograms . 303 Multivariate Histograms .309 Frequency Polygons .311 Averaged Shifted Histograms .316 9.3 Kernel Density Estimation .322 Univariate Kernel Estimators .322 Multivariate Kernel Estimators .327 9.4 Finite Mixtures .329 Univariate Finite Mixtures .331 Visualizing Finite Mixtures .333 Multivariate Finite Mixtures .335 EM Algorithm for Estimating the Parameters .338 Adaptive Mixtures .343 9.5 Generating Random Variables .348 9.6 MATLAB® Code .356 9.7 Further Reading .357 Exercises .359 Chapter 10 Supervised Learning 10.1 Introduction .363 10.2 Bayes Decision Theory .365 Estimating Class-Conditional Probabilities: Parametric Method .367 Estimating Class-Conditional Probabilities: Nonparametric .369 Bayes Decision Rule .370 Likelihood Ratio Approach .377 10.3 Evaluating the Classifier .380 Independent Test Sample .380 Cross-Validatíon .382 Receiver Operating Characteristic (ROC) Curve .385 10.4 Classification Trees .390 Growing the Tree .394 Pruning the Tree .399 Choosing the Best Tree .403 Other Tree Methods .412 10.5 Combining Classifiers .414 Bagging .415 Boosting .417 Arcing Classifiers .420 Random Forests .422 10.6 MATLAB® Code .423 xii Computational Statistics Handbook with MATLAB®, 2nd Edition 10.7 Further Reading .424 Exercises .428 Chapter 11 Unsupervised Learning 11.1 Introduction .431 11.2 Measures of Distance .432 11.3 Hierarchical Clustering .434 11.4 K-Means Clustering .442 11.5 Model-Based Clustering .445 Finite Mixture Models and the EM Algorithm .446 Model-Based Agglomerative Clustering .450 Bayesian Information Criterion .453 Model-Based Clustering Procedure .453 11.6 Assessing Cluster Results .458 Mojena - Upper Tail Rule .458 Silhouette Statistic .459 Other Methods for Evaluating Clusters .462 11.7 MATLAB® Code .465 11.8 Further Reading .466 Exercises .469 Chapter 12 Parametric Models 12.1 Introduction .471 12.2 Spline Regression Models .477 12.3 Logistic Regression .482 Creating the Model .482 Interpreting the Model Parameters .487 12.4 Generalized Linear Models .488 Exponential Family Form .489 Generalized Linear Model .494 Model Checking .498 12.5 MATLAB® Code .508 12.6 Further Reading .509 Exercises .511 Chapter 13 Nonparametric Models 13.1 Introduction .513 13.2 Some Smoothing Methods .514 Bin Smoothing .515 Running Mean .517 Table of Contents хщ Running Line .518 Local Polynomial Regression - Loess .519 Robust Loess .525 13.3 Kernel Methods .528 Nadaraya-Watson Estimator .531 Local Linear Kernel Estimator .532 13.4 Smoothing Splines .534 Natural Cubic Splines .536 Reinsch Method for Finding Smoothing Splines .537 Values for a Cubic Smoothing Spline .540 Weighted Smoothing Spline .540 13.5 Nonparametric Regression - Other Details .542 Choosing the Smoothing Parameter .542 Estimation of the Residual Variance .547 Variability of Smooths .548 13.6 Regression Trees .551 Growing a Regression Tree .553 Pruning a Regression Tree .557 Selecting a Tree .557 13.7 Additive Models .563 13.8 MATLAB® Code .567 13.9 Further Reading .570 Exercises .573 Chapter 14 Markov Chain Monte Carlo Methods 14.1 Introduction .575 14.2 Background .576 Bayesian Inference .576 Monte Carlo Integration .577 Markov Chains .579 Analyzing the Output .580 14.3 Metropolis-Hastings Algorithms .580 Metropolis-Hastings Sampler .581 Metropolis Sampler .584 Independence Sampler .587 Autoregressive Generating Density .589 14.4 The Gibbs Sampler .592 14.5 Convergence Monitoring .602 Gelman and Rubin Method .604 Raftery and Lewis Method .607 14.6 MATLAB® Code .609 14.7 Further Reading .610 Exercises .,.612 xiv Computational Statistics Handbook with MATLAB®, 2nd Edition Chapter 15 Spatial Statistics 15.1 Introduction .617 What Is Spatial Statistics? . 617 Types of Spatial Data .618 Spatial Point Patterns .619 Complete Spatial Randomness . 621 15.2 Visualizing Spatial Point Processes .623 15.3 Exploring First-order and Second-order Properties .627 Estimating the Intensity . 627 Estimating the Spatial Dependence . 630 15.4 Modeling Spatial Point Processes .638 Nearest Neighbor Distances .638 K-Function . 643 15.5 Simulating Spatial Point Processes .646 Homogeneous Poisson Process .647 Binomial Process .650 Poisson Cluster Process .651 Inhibition Process .654 Strauss Process . 656 15.6 MATLAB® Code .658 15.7 Further Reading .659 Exercises .661 Appendix A Introduction to MATLAB® A.1 What Is MATLAB®? .663 A.2 Getting Help in MATLAB® .664 A.3 File and Workspace Management .664 A.4 Punctuation in MATLAB® .666 A.5 Arithmetic Operators .666 A.6 Data Constructs in MATLAB® .668 Basic Data Constructs .668 Building Arrays .668 Cell Arrays .669 A.7 Script Files and Functions .670 A.8 Control Flow .672 For Loop .672 While Loop .672 If-Else Statements .673 Switch Statement .673 A.9 Simple Plotting .673 A.10 Contact Information .676 Table of Contents xv Appendix В Projection Pursuit Indexes B.l Indexes .677 Friedman-Tukey Index.677 Entropy Index.678 Moment Index.678 L2 Distances .679 Б.2 MATLAB® Source Code .680 Appendix С MATLAB® Statistics Toolbox File I/O .687 Dataset Arrays .687 Grouped Data .687 Descriptive Statistics .688 Statistical Visualization .688 Probability Density Functions .689 Cumulative Distribution Functions .690 Inverse Cumulative Distribution Functions .691 Distribution Statistics Functions .691 Distribution Fitting Functions .692 Negative Log-Likelihood Functions .692 Random Number Generators .693 Hypothesis Tests .694 Analysis of Variance .694 Regression Analysis .694 Multivariate Methods .695 Cluster Analysis .696 Classification .696 Markov Models .696 Design of Experiments .697 Statistical Process Control .697 Graphical User Interfaces .697 Appendix D Computational Statistics Toolbox Probability Distributions .699 Statistics .699 Random Number Generation .700 Exploratory Data Analysis .700 Bootstrap and Jackknife .701 Probability Density Estimation .701 Supervised Learning .701 Unsupervised Learning .701 xvi Computational Statistics Handbook with MATLAB®, 2m Edition Parametric and Nonparametric Models .702 Markov Chain Monte Carlo .702 Spatial Statistics .702 Appendix E Exploratory Data Analysis Toolboxes E.I Introduction .703 E.2 Exploratory Data Analysis Toolbox .704 E.3 EDA GUI Toolbox .705 Appendix F Data Sets Introduction .719 Appendix G Notation Overview .727 Observed Data .727 Greek Letters .728 Functions and Distributions .728 Matrix Notation .729 Statistics .729 References .731 Author Index .751 Subject Index .757
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id	DE-604.BV022476044
illustrated	Illustrated
index_date	2024-07-02T17:46:31Z
indexdate	2024-09-10T00:56:00Z
institution	BVB
isbn	9781584885665
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-015683460
oclc_num	254942782
open_access_boolean
owner	DE-19 DE-BY-UBM DE-945 DE-91G DE-BY-TUM DE-355 DE-BY-UBR DE-92 DE-M347
owner_facet	DE-19 DE-BY-UBM DE-945 DE-91G DE-BY-TUM DE-355 DE-BY-UBR DE-92 DE-M347
physical	XXIII, 767 S. graph. Darst.
publishDate	2008
publishDateSearch	2008
publishDateSort	2008
publisher	Chapman & Hall, CRC
record_format	marc
series	Computer science and data analysis series
series2	Computer science and data analysis series
spelling	Martinez, Wendy L. 1953- Verfasser (DE-588)1173101632 aut Computational statistics handbook with MATLAB Wendy L. Martinez ; Angel R. Martinez 2. ed. Boca Raton [u.a.] Chapman & Hall, CRC 2008 XXIII, 767 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computer science and data analysis series 8 MATLAB (logiciel) ram Statistique mathématique - Informatique Statistique mathématique ram MATLAB (DE-588)4329066-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s MATLAB (DE-588)4329066-8 s DE-604 Martinez, Angel R. Verfasser aut Computer science and data analysis series 8 (DE-604)BV019994004 8 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015683460&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Martinez, Wendy L. 1953- Martinez, Angel R. Computational statistics handbook with MATLAB Computer science and data analysis series MATLAB (logiciel) ram Statistique mathématique - Informatique Statistique mathématique ram MATLAB (DE-588)4329066-8 gnd Statistik (DE-588)4056995-0 gnd
subject_GND	(DE-588)4329066-8 (DE-588)4056995-0
title	Computational statistics handbook with MATLAB
title_auth	Computational statistics handbook with MATLAB
title_exact_search	Computational statistics handbook with MATLAB
title_exact_search_txtP	Computational statistics handbook with MATLAB
title_full	Computational statistics handbook with MATLAB Wendy L. Martinez ; Angel R. Martinez
title_fullStr	Computational statistics handbook with MATLAB Wendy L. Martinez ; Angel R. Martinez
title_full_unstemmed	Computational statistics handbook with MATLAB Wendy L. Martinez ; Angel R. Martinez
title_short	Computational statistics handbook with MATLAB
title_sort	computational statistics handbook with matlab
topic	MATLAB (logiciel) ram Statistique mathématique - Informatique Statistique mathématique ram MATLAB (DE-588)4329066-8 gnd Statistik (DE-588)4056995-0 gnd
topic_facet	MATLAB (logiciel) Statistique mathématique - Informatique Statistique mathématique MATLAB Statistik
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015683460&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV019994004
work_keys_str_mv	AT martinezwendyl computationalstatisticshandbookwithmatlab AT martinezangelr computationalstatisticshandbookwithmatlab

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