2013, ISBN 1461468485, xiii, 600 pages

Book specializes in data analysis with focus on practice of predictive modeling Useful as a guide for practitioners Reader can reproduce all results using R.

Prediction theory | Mathematical models | R (Computer program language) | Mathematical statistics | Statistics | Statistics and Computing/Statistics Programs | Statistics, general | Statistics for Life Sciences, Medicine, Health Sciences

Prediction theory | Mathematical models | R (Computer program language) | Mathematical statistics | Statistics | Statistics and Computing/Statistics Programs | Statistics, general | Statistics for Life Sciences, Medicine, Health Sciences

Book

2003, ISBN 0387916245, xix, 632

This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate...

Time-series analysis | Mathematical models | Econometric models | S-Plus | Finance

Time-series analysis | Mathematical models | Econometric models | S-Plus | Finance

Book

Statistics and computing, ISSN 1573-1375, 2013, Volume 24, Issue 6, pp. 997 - 1016

We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected...

Statistics and Computing/Statistics Programs | Cross-validation | Prediction | Bayes | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | AIC | WAIC | Statistics | Probability and Statistics in Computer Science | DIC | ASYMPTOTIC EQUIVALENCE | CHOICE | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | SELECTION | Models | Biomedical engineering

Statistics and Computing/Statistics Programs | Cross-validation | Prediction | Bayes | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | AIC | WAIC | Statistics | Probability and Statistics in Computer Science | DIC | ASYMPTOTIC EQUIVALENCE | CHOICE | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | SELECTION | Models | Biomedical engineering

Journal Article

2013, Springer texts in statistics, ISBN 9781461471370, Volume 103, xvi, 426 pages

"An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the...

Mathematical models | R (Computer program language) | Mathematical statistics | Statistics

Mathematical models | R (Computer program language) | Mathematical statistics | Statistics

Book

Statistics and computing, ISSN 1573-1375, 2007, Volume 17, Issue 4, pp. 395 - 416

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by...

Statistics and Computing/Statistics Programs | Graph Laplacian | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | Spectral clustering | Statistics, general | Statistics | SPARSE MATRICES | REDUCTION | CONNECTIVITY | RESISTANCE DISTANCE | EIGENVECTORS | ALGORITHM | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | graph Laplacian | EDGE | spectral clustering | GRAPHS | Algorithms

Statistics and Computing/Statistics Programs | Graph Laplacian | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | Spectral clustering | Statistics, general | Statistics | SPARSE MATRICES | REDUCTION | CONNECTIVITY | RESISTANCE DISTANCE | EIGENVECTORS | ALGORITHM | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | graph Laplacian | EDGE | spectral clustering | GRAPHS | Algorithms

Journal Article

Statistics and computing, ISSN 1573-1375, 2011, Volume 22, Issue 6, pp. 1167 - 1180

Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach...

Statistics and Computing/Statistics Programs | Bayesian model choice | Bayesian statistics | ABC methodology | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | DIYABC | Statistics, general | Statistics | Likelihood-free methods | ABC | PARAMETER-ESTIMATION | PHYLOGEOGRAPHY | SEQUENTIAL MONTE-CARLO | STATISTICS & PROBABILITY | INFERENCE | CHOICE | MODEL SELECTION | COMPUTER SCIENCE, THEORY & METHODS | Mathematics

Statistics and Computing/Statistics Programs | Bayesian model choice | Bayesian statistics | ABC methodology | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | DIYABC | Statistics, general | Statistics | Likelihood-free methods | ABC | PARAMETER-ESTIMATION | PHYLOGEOGRAPHY | SEQUENTIAL MONTE-CARLO | STATISTICS & PROBABILITY | INFERENCE | CHOICE | MODEL SELECTION | COMPUTER SCIENCE, THEORY & METHODS | Mathematics

Journal Article

2015, 2015, Environmental Earth Sciences., ISBN 3319139789, XXXIII, 903 p. 289 illus., 120 illus. in color.

This book provides new insights on the study of global environmental changes using the ecoinformatics tools and the adaptive-evolutionary technology of...

environmental assessment | environmental monitoring | ecosystem management | Environmental sciences | Earth sciences | remote sensing | information systems | Statistics | information services | modelling | climatic changes | computation | Computer programs | Statistics and Computing/Statistics Programs | Math. Appl. in Environmental Science | Earth Sciences | Environmental Science and Engineering

environmental assessment | environmental monitoring | ecosystem management | Environmental sciences | Earth sciences | remote sensing | information systems | Statistics | information services | modelling | climatic changes | computation | Computer programs | Statistics and Computing/Statistics Programs | Math. Appl. in Environmental Science | Earth Sciences | Environmental Science and Engineering

eBook

Statistics and computing, ISSN 1573-1375, 2016, Volume 27, Issue 3, pp. 659 - 678

This paper is about variable selection with the random forests algorithm in presence of correlated predictors. In high-dimensional regression or classification...

Statistics and Computing/Statistics Programs | Supervised learning | Variable importance | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Statistics | Random forests | Probability and Statistics in Computer Science | Variable selection | STABILITY | GENE SELECTION | CLASSIFICATION | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | FEATURES | Earth resources technology satellites | Forests and forestry | Algorithms | Analysis | Remote sensing | Machine learning | Statistics - Methodology | Methodology | Applications | Machine Learning

Statistics and Computing/Statistics Programs | Supervised learning | Variable importance | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Statistics | Random forests | Probability and Statistics in Computer Science | Variable selection | STABILITY | GENE SELECTION | CLASSIFICATION | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | FEATURES | Earth resources technology satellites | Forests and forestry | Algorithms | Analysis | Remote sensing | Machine learning | Statistics - Methodology | Methodology | Applications | Machine Learning

Journal Article

Statistics and computing, ISSN 1573-1375, 2011, Volume 22, Issue 5, pp. 1009 - 1020

Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to...

Statistics and Computing/Statistics Programs | Approximate Bayesian computation | Markov chain Monte Carlo | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | Sequential Monte Carlo | Statistics, general | Statistics | TUBERCULOSIS | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | PARAMETERS | INFERENCE | Markov processes | Monte Carlo method | Models | Algorithms | Analysis | Methods

Statistics and Computing/Statistics Programs | Approximate Bayesian computation | Markov chain Monte Carlo | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | Sequential Monte Carlo | Statistics, general | Statistics | TUBERCULOSIS | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | PARAMETERS | INFERENCE | Markov processes | Monte Carlo method | Models | Algorithms | Analysis | Methods

Journal Article

Statistics and Computing, ISSN 0960-3174, 11/2019, Volume 29, Issue 6, pp. 1181 - 1183

Journal Article

Statistics and computing, ISSN 1573-1375, 2010, Volume 22, Issue 3, pp. 713 - 722

Most surrogate models for computer experiments are interpolators, and the most common interpolator is a Gaussian process (GP) that deliberately omits a...

Statistics and Computing/Statistics Programs | Computer simulator | Interpolation | Gaussian process | Surrogate model | Smoothing | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | Statistics, general | Statistics | CALIBRATION | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | SIMULATION | EMULATION | Analysis | Physicians (General practice) | Models

Statistics and Computing/Statistics Programs | Computer simulator | Interpolation | Gaussian process | Surrogate model | Smoothing | Numeric Computing | Artificial Intelligence (incl. Robotics) | Mathematics, general | Statistics, general | Statistics | CALIBRATION | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | SIMULATION | EMULATION | Analysis | Physicians (General practice) | Models

Journal Article

Statistics and computing, ISSN 1573-1375, 2013, Volume 24, Issue 3, pp. 461 - 479

Dependent data arise in many studies. Frequently adopted sampling designs, such as cluster, multilevel, spatial, and repeated measures, may induce this...

Statistics and Computing/Statistics Programs | Best linear predictor | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Statistics | Hierarchical models | Gaussian quadrature | Clarkeâ€™s derivative | Probability and Statistics in Computer Science | Clarke's derivative | REGRESSION | MATRIX | APPROXIMATION | STATISTICS & PROBABILITY | LONGITUDINAL DATA | LAPLACE DISTRIBUTION | INFERENCE | BOOTSTRAP | COMPUTER SCIENCE, THEORY & METHODS | Models | Algorithms | Mathematical optimization | Analysis | Quantiles | Maximization | Computer simulation | Computation | Regression | Mathematical models | Optimization

Statistics and Computing/Statistics Programs | Best linear predictor | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Statistics | Hierarchical models | Gaussian quadrature | Clarkeâ€™s derivative | Probability and Statistics in Computer Science | Clarke's derivative | REGRESSION | MATRIX | APPROXIMATION | STATISTICS & PROBABILITY | LONGITUDINAL DATA | LAPLACE DISTRIBUTION | INFERENCE | BOOTSTRAP | COMPUTER SCIENCE, THEORY & METHODS | Models | Algorithms | Mathematical optimization | Analysis | Quantiles | Maximization | Computer simulation | Computation | Regression | Mathematical models | Optimization

Journal Article

Statistics and computing, ISSN 1573-1375, 2012, Volume 24, Issue 2, pp. 181 - 202

Finite mixtures of multivariate skew t (MST) distributions have proven to be useful in modelling heterogeneous data with asymmetric and heavy tail behaviour....

Statistics and Computing/Statistics Programs | Skew normal distributions | Mixture models | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Skew t component distributions | Statistics | EM algorithm | Probability and Statistics in Computer Science | MAXIMUM-LIKELIHOOD | MODELS | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | BAYESIAN-INFERENCE | Algorithms | Computation | Mathematical analysis | Mathematical models | Models | Modelling | Proposals | Estimates

Statistics and Computing/Statistics Programs | Skew normal distributions | Mixture models | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Skew t component distributions | Statistics | EM algorithm | Probability and Statistics in Computer Science | MAXIMUM-LIKELIHOOD | MODELS | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | BAYESIAN-INFERENCE | Algorithms | Computation | Mathematical analysis | Mathematical models | Models | Modelling | Proposals | Estimates

Journal Article

Statistics and Computing, ISSN 0960-3174, 3/2015, Volume 25, Issue 2, pp. 173 - 187

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection...

Statistics and Computing/Statistics Programs | Group lasso | Descent algorithms | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Penalized regression | Statistics | Optimization | Probability and Statistics in Computer Science | SHRINKAGE | GENE-EXPRESSION | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | VARIABLE SELECTION | LIKELIHOOD | Analysis | Algorithms | Fittings | Computer simulation | Scad | Regression | Mathematical models | Logistics

Statistics and Computing/Statistics Programs | Group lasso | Descent algorithms | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Penalized regression | Statistics | Optimization | Probability and Statistics in Computer Science | SHRINKAGE | GENE-EXPRESSION | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | VARIABLE SELECTION | LIKELIHOOD | Analysis | Algorithms | Fittings | Computer simulation | Scad | Regression | Mathematical models | Logistics

Journal Article