Martinez, Wendy L. <br/><br/>
Computational statistics handbook with MATLAB /  
Wendy L. Martinez, Angel R. Martinez. 
 - Second edition. 
 - Boca Ratón :   Chapman & Hall/CRC,   2008. 
 - xxiii, 767 páginas :   tablas, gráficas ;   24 cm. 
 - Computer science and data analysis series .
<br/><br/>Incluye referencias bibliográficas (páginas 731-750) e índice. 
<br/><br/>Chapter 1: Introduction: 1.1. What is computational statistics? ; 1.2. An overview of the book ; 1.3. Matlab code ; 1.4. Further reading -- Chapter 2: Probability concepts: 2.1. Introduction ; 2.2. Probability ; 2.3. Conditional probability and independence ; 2.4. Expectation ; 2.5. Common distributions ; 2.6. Matlab code ; 2.7. Further Reading -- Chapter 3: Sampling concepts: 3.1. Introduction ; 3.2. Sampling terminology and concepts ; 3.3. Sampling distributions ; 3.4. Parameter estimation ; 3.5. Empirical distribution function ; 3.6. Matlab code ; 3.7. Further reading -- Chapter 4: Generating random variables: 4.1. Introduction ; 4.2. General techniques for generating random variables ; 4.3. Generating continuous random variables ; 4.4. Generating discrete random variables ; 4.5. Matlab code ; 4.6 Further reading -- Chapter 5: Exploratory data analysis: 5.1. Introduction ; 5.2. Exploring univariate data ; 5.3. Exploring bivariate and trivariate data ; 5.4. Exploring multi-dimensional data ; 5.5. Matlab code ; 5.6. Further reading -- Chapter 6: Finding structure: 6.1. Introduction ; 6.2. Principal component analysis ; 6.3. Projection pursuit EDA ; 6.4. Independent component analysis ; 6.5. Grand tour ; 6.6. Nonlinear dimensionality reduction ; 6.7. Matlab code ; 6.8. Further reading -- Chapter 7: Monte Carlo methods for inferential statistics: 7.1. Introduction ; 7.2. Classical inferential statistics ; 7.3. Monte Carlo methods for inferential statistics ; 7.4. Bootstrap methods ; 7.5. Matlab code ; 7.6. Further reading -- Chapter 8: Data partitioning: 8.1. Introduction ; 8.2. Cross-validation ; 8.3. Jackknife ; 8.4. Better bootstrap confidence intervals ; 8.5. Jackknife-after-bootstrap ; 8.6. Matlab code ; 8.7. Further reading -- Chapter 9 : Probability density estimation: 9.1. Introduction ; 9.2. Histograms ; 9.3. Kernel density estimation ; 9.4. Finite mixtures ; 9.5. Generating random variables ; 9.6. Matlab Code ; 9.7. Further reading -- Chapter 10: Supervised learning: 10.1. Introduction ; 10.2. Bayes decision theory ; 10.3. Evaluating the classifier ; 10.4. Classification trees ; 10.5. Combining classifiers ; 10.6. MATLAB code ; 10.7. Further reading -- Chapter 11: Unsupervised learning ; 11.1. Introduction ; 11.2. Measures of distance ; 11.3. Hierarchical clustering ; 11.4. K-Means clustering ; 11.5. Model-based clustering ; 11.6. Assessing cluster results ; 11.7. Matlab code ; 11.8. Further Reading -- Chapter 12: Parametric models: 12.1. Introduction ; 12.2. Spline regression models ; 12.3. Logistic regression ; 12.4. Generalized linear models ; 12.5. MATLAB code ; 12.6. Further reading -- Chapter 13: Nonparametric regression: 13.1. Introduction ; 13.2. Some smoothing methods ; 13.3. Kernel methods ; 13.4. Smoothing splines ; 13.5. Nonparametric regression - other details ; 13.6. Regression trees ; 13.7. Additive models ; 13.8. Matlab code ; 13.9. Further reading -- Chapter 14: Markov Chain Monte Carlo methods: 14.1. Introduction ; 14.2. Background ; 14.3. Metropolis-hastings algorithms ; 14.4. The Gibbs sampler ; 14.5 Convergence monitoring  ; 14.6. Matlab code ; 14.7. Further reading -- Chapter 15: Spatial statistics: 15.1. Introduction ; 15.2. Visualizing spatial point processes ; 15.3. Exploring first-order and second-order properties ; 15.4. Modeling spatial point processes ; 15.5. Simulating spatial point processes ; 15.6. Matlab code ; 15.7. Further reading. 
<br/><br/><label>ISBN: </label>9781584885665 
<br/><br/><label>Subjects--Topical Terms: </label><br/>MATLAB (Programa para computador)<br/>Método Monte Carlo<br/>Análisis espacial (Estadística)
<br/><br/><label>Dewey Class. No.: </label>519.5   / M17c