



LEADER 
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140122  eng 
020 


a 9780387217062

100 
1 

a Venables, W.N.

245 
0 
0 
a Modern Applied Statistics with S
h Elektronische Ressource
c by W.N. Venables, B.D. Ripley

250 


a 4th ed. 2002

260 


a New York, NY
b Springer New York
c 2002, 2002

300 


a XII, 498 p
b online resource

505 
0 

a 8.11 Conclusions  9 TreeBased Methods  9.1 Partitioning Methods  9.2 Implementation in rpart  9.3 Implementation in tree  10 Random and Mixed Effects  10.1 Linear Models  10.2 Classic Nested Designs  10.3 NonLinear Mixed Effects Models  10.4 Generalized Linear Mixed Models  10.5 GEE Models  11 Exploratory Multivariate Analysis  11.1 Visualization Methods  11.2 Cluster Analysis  11.3 Factor Analysis  11.4 Discrete Multivariate Analysis  12 Classification  12.1 Discriminant Analysis  12.2 Classification Theory  12.3 NonParametric Rules  12.4 Neural Networks  12.5 Support Vector Machines  12.6 Forensic Glass Example  12.7 Calibration Plots  13 Survival Analysis  13.1 Estimators of Survivor Curves  13.2 Parametric Models  13.3 Cox Proportional Hazards Model  13.4 Further Examples  14 Time Series Analysis  14.1 SecondOrder Summaries  14.2 ARIMA Models  14.3 Seasonality  14.4 Nottingham Temperature Data 

505 
0 

a 6.1 An Analysis of Covariance Example  6.2 Model Formulae and Model Matrices  6.3 Regression Diagnostics  6.4 Safe Prediction  6.5 Robust and Resistant Regression  6.6 Bootstrapping Linear Models  6.7 Factorial Designs and Designed Experiments  6.8 An Unbalanced FourWay Layout  6.9 Predicting Computer Performance  6.10 Multiple Comparisons  7 Generalized Linear Models  7.1 Functions for Generalized Linear Modelling  7.2 Binomial Data  7.3 Poisson and Multinomial Models  7.4 A Negative Binomial Family  7.5 OverDispersion in Binomial and Poisson GLMs  8 NonLinear and Smooth Regression  8.1 An Introductory Example  8.2 Fitting NonLinear Regression Models  8.3 NonLinear Fitted Model Objects and Method Functions  8.4 Confidence Intervals for Parameters  8.5 Profiles  8.6 Constrained NonLinear Regression  8.7 OneDimensional CurveFitting  8.8 Additive Models  8.9 ProjectionPursuit Regression  8.10 Neural Networks 

505 
0 

a 14.5 Regression with Autocorrelated Errors  14.6 Models for Financial Series  15 Spatial Statistics  15.1 Spatial Interpolation and Smoothing  15.2 Kriging  15.3 Point Process Analysis  16 Optimization  16.1 Univariate Functions  16.2 SpecialPurpose Optimization Functions  16.3 General Optimization  Appendices  A ImplementationSpecific Details  A.1 Using SPLUS under Unix / Linux  A.2 Using SPLUS under Windows  A.3 Using R under Unix / Linux  A.4 Using R under Windows  A.5 For Emacs Users  B The SPLUS GUI  C Datasets, Software and Libraries  C.1 Our Software  C.2 Using Libraries  References

505 
0 

a 1 Introduction  1.1 A Quick Overview of S  1.2 Using S  1.3 An Introductory Session  1.4 What Next?  2 Data Manipulation  2.1 Objects  2.2 Connections  2.3 Data Manipulation  2.4 Tables and CrossClassification  3 The S Language  3.1 Language Layout  3.2 More on S Objects  3.3 Arithmetical Expressions  3.4 Character Vector Operations  3.5 Formatting and Printing  3.6 Calling Conventions for Functions  3.7 Model Formulae  3.8 Control Structures  3.9 Array and Matrix Operations  3.10 Introduction to Classes and Methods  4 Graphics  4.1 Graphics Devices  4.2 Basic Plotting Functions  4.3 Enhancing Plots  4.4 Fine Control of Graphics  4.5 Trellis Graphics  5 Univariate Statistics  5.1 Probability Distributions  5.2 Generating Random Data  5.3 Data Summaries  5.4 Classical Univariate Statistics  5.5 Robust Summaries  5.6 Density Estimation  5.7 Bootstrap and Permutation Methods  6 Linear Statistical Models 

653 


a Computational Mathematics and Numerical Analysis

653 


a Probability Theory and Stochastic Processes

653 


a Statistics

653 


a Statistical Theory and Methods

653 


a Mathematical Software

653 


a Computer software

653 


a Computer mathematics

653 


a Probabilities

653 


a Statistics and Computing/Statistics Programs

700 
1 

a Ripley, B.D.
e [author]

710 
2 

a SpringerLink (Online service)

041 
0 
7 
a eng
2 ISO 6392

989 


b SBA
a Springer Book Archives 2004

490 
0 

a Statistics and Computing

856 


u https://doi.org/10.1007/9780387217062?nosfx=y
x Verlag
3 Volltext

082 
0 

a 519.2

520 


a S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods. Implementations of S are available commercially in SPLUS(R) workstations and as the Open Source R for a wide range of computer systems. The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for wouldbe users of SPLUS or R and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are state of the art approaches to topics such as linear, nonlinear and smooth regression models, treebased methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, nonparametric smoothing and bootstrapping are used where appropriate. This fourth edition is intended for users of SPLUS 6.0 or R 1.5.0 or later. A substantial change from the third edition is updating for the current versions of SPLUS and adding coverage of R. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computerintensive methods to be used, and methods such as GLMMs,
