William J. Kennedy

Statistical Computing (eBook, PDF)

• Format: PDF

Produktbeschreibung

The reader who intends to take a hand in designing his own regression and multivariate packages will find a storehouse of information and a valuable resource in the field of statistical computing. The text is highly readable and well illustrated with examples.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 608
• Erscheinungstermin: 23. Juni 2021
• Englisch
• ISBN-13: 9781351414593
• Artikelnr.: 54914516

Autorenporträt

William J. Kennedy, James E. Gentle

Inhaltsangabe

Introduction
Orientation Purpose
Prerequisites Presentation of Algorithms Computer Organization
Introduction
Components of the Digital Computer System
Representation of Numeric Values Floating and Fixed-Point Arithmetic Operations
Error in Floating-Point Computation
Introduction
Types of Error Error Due to Approximation
Imposed by the Compute Analyzing Error in a Finite Process
Rounding Error in Floating-Point Operations
Rounding Error in Two Common Floating-Point Calculations
Condition and Numerical Stability
Other Methods of Assessing Error in Computations
Summary Programming and Statistical Software
Programming Languages: Introduction
Components of Programming Languages
Program Development Statistical Software
Approximating Probabilities and Percentage Points in Selected Probability Distributions
Notation and General Considerations
General Methods in Approximation
The Normal Distribution
Student's t Distribution
The Beta Distribution
F Distribution
Chi-Square Distribution
Random Numbers: Generation
Tests
and Applications
Introduction
Generation of Uniform Random Numbers
Tests of Random Number Generators
General Techniques for Generation of Nonuniform Random Variates
Generation of Variates from Specific Distributions
Applications Selected Computational Methods in Linear Algebra
Introduction
Methods Based on Orthogonal Transformations
Gaussian Elimination and the Sweep Operator
Cholesky Decomposition and Rank-One Update
Summary
Computational Methods for Multiple Linear Regression
Analysis
Basic Computational Methods
Regression
Model Building Multiple Regression Under Linear Restrictions
Computational Methods for Classification Models
Introduction
The Special Case of Balance and Completeness for Fixed-Effects Models
The General Problem for Fixed-Effects Models
Computing Expected Mean Squares and Estimates of Variance Components
Unconstrained Optimization and Nonlinear Regression Preliminaries Methods for Unconstrained Minimization Nonlinear Regression
Computational Methods Test Problems
Model Fitting Based on Criteria
Other Than Least Squares
Introduction
Minimum Lp Norm Estimators
Other Robust Estimators
Biased Estimation Robust Nonlinear Regression Exercises
Selected Multivariate Methods
Introduction Canonical Correlations
Principal Components
Factor Analysis
Multivariate
Analysis of Variance.