Model choice and model aggregation
By: Frédéric Bertrand, Jean-Jacques Droesbeke, Gilbert Saporta, Christine Thomas-Agnan
Publisher: Editions Technip
Published: 2017
Place of publication: Paris
Language: eng
Number of pages: 355
ISBN-13: 9782710811770
Subjects: Statistics; Methodology; Mathematical statistics
Notes: "Société Française de Statistique."

"This volume originates from the organization of high-level specialists ... who were all speakers at the 16th bienn[i]al workshop on advanced statistics by the French Statistical Society"--Back cover.

Includes bibliographical references and index.
Table of contents: title: 9.3.3.; title: Functional data  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.3.4.; title: Intermediate conclusion  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.4.; title: Canonical models  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.4.1.; title: Parsimonious mixture models  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.4.2.; title: Variable selection through regularization  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.4.3.; title: Variable role modelling  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.4.4.; title: Co-clustering  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.4.5.; title: Intermediate conclusion  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.5.; title: Future methodological challenges  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 10.1.; title: Introduction  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.2.; title: Model-based clustering  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.3.; title: Clustering of microarray data  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.3.1.; title: Microarray data  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.3.2.; title: Gaussian mixture models  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.3.3.; title: Application  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.4.; title: Clustering of RNA-seq data  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.4.1.; title: RNA-seq data  -- Andrea Rau / -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau -- 10.4.2.; title: Poisson mixture models  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.4.3.; title: Applications  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 10.5.; title: Conclusion  -- Marie-Laure Martin-Magniette / -- Cathy Maugis-Rabusseau / -- Andrea Rau -- 11.1.; title: Functional regression models  -- Mathilde Mougeot -- 11.2.; title: Data Mining using sparse approximation of the intra day load curves  -- Mathilde Mougeot -- 11.2.1.; title: Choice of a generic dictionary  -- Mathilde Mougeot -- 11.2.2.; title: Mining and clustering  -- Mathilde Mougeot -- 11.2.3.; title: Patterns of consumption  -- Mathilde Mougeot -- 11.3.; title: Sparse modeling with adaptive dictionaries  -- Mathilde Mougeot -- 11.4.; title: Forecasting  -- Mathilde Mougeot -- 11.4.1.; title: The experts  -- Mathilde Mougeot -- 11.4.2.; title: Aggregation  -- Mathilde Mougeot -- 11.5.; title: Performances & Software  -- Mathilde Mougeot -- 11.6.; title: Conclusion and perspectives  -- Mathilde Mougeot -- 11.7.; title: Annexes -- Mathilde Mougeot.; title: 1.1.; title: Introduction  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.2.; title: Elements of the history of words and ideas  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.3.; title: Modeling in astronomy  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.4.; title: Triangulation in geodesy  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.5.; title: The measurement of meridian arcs  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.6.; title: A model selection tale  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.7.; title: A new model appears  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.8.; title: Expeditions for choosing a good model  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.9.; title: The control of errors  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.10.; title: A final example  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 1.11.; title: Outline of the book  -- Jean-Jacques Droesbeke / -- Gilbert Saporta / -- Christine Thomas-Agnan -- 2.1.; title: Model selection  -- Pascal Massart -- 2.1.1.; title: Empirical risk minimization  -- Pascal Massart -- 2.1.2.; title: The model choice paradigm  -- Pascal Massart -- 2.1.3.; title: Model selection via penalization  -- Pascal Massart -- 2.2.; title: Selection of linear Gaussian models  -- Pascal Massart -- 2.2.1.; title: Examples of Gaussian frameworks  -- Pascal Massart -- 2.2.2.; title: Some model selection problems  -- Pascal Massart -- 2.2.3.; title: The least squares procedure  -- Pascal Massart -- 2.3.; title: Selecting linear models  -- Pascal Massart -- 2.3.1.; title: Mallows' heuristics  -- Pascal Massart -- 2.3.2.; title: Schwarz's heuristics  -- Pascal Massart -- 2.3.3.; title: A first model selection theorem for linear models  -- Pascal Massart -- 2.4.; title: Adaptive estimation in the minimax sense  -- Pascal Massart -- 2.4.1.; title: Minimax lower bounds  -- Pascal Massart -- 2.4.2.; title: Adaptive properties of penalized estimators for Gaussian sequences  -- Pascal Massart -- 2.4.3.; title: Adaptation with respect to ellipsoids  -- Pascal Massart -- 2.4.4.; title: Adaptation with respect to arbitrary lp-bodies  -- Pascal Massart -- 2.5.; title: Appendix  -- Pascal Massart -- 2.5.1.; title: Functional analysis: from function spaces to sequence spaces  -- Pascal Massart -- 2.5.2.; title: Gaussian processes  -- Pascal Massart -- 3.1.; title: A general Theorem  -- Pascal Massart -- 3.2.; title: Selecting ellipsoids and l2 regularization  -- Pascal Massart -- 3.2.1.; title: Adaptation over Besov ellipsoids  -- Pascal Massart -- 3.2.2.; title: A first penalization strategy  -- Pascal Massart -- 3.2.3.; title: l2 regularization  -- Pascal Massart -- 3.3.; title: l1 regularization  -- Pascal Massart -- 3.3.1.; title: Variable selection  -- Pascal Massart -- 3.3.2.; title: Selecting l1 balls and the Lasso  -- Pascal Massart -- 3.4.; title: Appendix  -- Pascal Massart -- 3.4.1.; title: Concentration inequalities  -- Pascal Massart -- 3.4.2.; title: Information inequalities  -- Pascal Massart -- 3.4.3.; title: Birge's Lemma  -- Pascal Massart -- 4.1.; title: The Bayesian paradigm  -- Jean-Michel Marin / -- Christian Robert -- 4.1.1.; title: The posterior distribution  -- Jean-Michel Marin / -- Christian Robert -- 4.1.2.; title: Bayesian estimates  -- Jean-Michel Marin / -- Christian Robert -- 4.1.3.; title: Conjugate prior distributions  -- Jean-Michel Marin / -- Christian Robert -- 4.1.4.; title: Noninformative priors  -- Jean-Michel Marin / -- Christian Robert -- 4.1.5.; title: Bayesian credible sets  -- Jean-Michel Marin / -- Christian Robert -- 4.2.; title: Bayesian discrimination between models  -- Jean-Michel Marin / -- Christian Robert -- 4.2.1.; title: The model index as a parameter  -- Jean-Michel Marin / -- Christian Robert -- 4.2.2.; title: The Bayes Factor  -- Jean-Michel Marin / -- Christian Robert -- 4.2.3.; title: The ban on improper priors  -- Jean-Michel Marin / -- Christian Robert -- 4.2.4.; title: The Bayesian Information Criterium  -- Jean-Michel Marin / -- Christian Robert -- 4.2.5.; title: Bayesian Model Averaging  -- Jean-Michel Marin / -- Christian Robert -- 4.3.; title: The case of linear regression models  -- Jean-Michel Marin / -- Christian Robert -- 4.3.1.; title: Conjugate prior  -- Jean-Michel Marin / -- Christian Robert -- 4.3.2.; title: Zellner's G prior distribution  -- Jean-Michel Marin / -- Christian Robert -- 4.3.3.; title: HPD regions  -- Jean-Michel Marin / -- Christian Robert -- 4.3.4.; title: Calculation of evidences and Bayes factors  -- Jean-Michel Marin / -- Christian Robert -- 4.3.5.; title: Variable Selection  -- Jean-Michel Marin / -- Christian Robert -- 5.1.; title: Some Monte Carlo strategies to approximate the evidence  -- Jean-Michel Marin / -- Christian Robert -- 5.1.1.; title: The basic Monte Carlo solution  -- Jean-Michel Marin / -- Christian Robert -- 5.1.2.; title: Usual importance sampling approximations  -- Jean-Michel Marin / -- Christian Robert -- 5.1.3.; title: The Harmonic mean approximation  -- Christian Robert / -- Jean-Michel Marin -- 5.1.4.; title: The Chib's method  -- Jean-Michel Marin / -- Christian Robert -- 5.2.; title: The bridge sampling methodology to compare embedded models  -- Jean-Michel Marin / -- Christian Robert -- 5.3.; title: A Monte Carlo Markov Chain method for variable selection  -- Christian Robert / -- Jean-Michel Marin -- 5.3.1.; title: The Gibbs sampler  -- Christian Robert / -- Jean-Michel Marin -- 5.3.2.; title: A Stochastic Search for the Most Likely Model  -- Christian Robert / -- Jean-Michel Marin -- 6.1.; title: Motivations  -- Nicolas Vayatis -- 6.2.; title: Randomness, bless our data!  -- Nicolas Vayatis -- 6.2.1.; title: A probabilistic view of classification data  -- Nicolas Vayatis -- 6.2.2.; title: Let the data go: error estimation and model validation  -- Nicolas Vayatis -- 6.3.; title: Power to the masses: aggregation principles  -- Nicolas Vayatis -- 6.3.1.; title: Voting and averaging in binary classification  -- Nicolas Vayatis -- 6.3.2.; title: A lazy way to multi-class classification  -- Nicolas Vayatis -- 6.3.3.; title: Agreement and averaging in the context of scoring  -- Nicolas Vayatis -- 6.3.4.; title: From bipartite ranking to K-partite ranking  -- Nicolas Vayatis -- 6.4.; title: Time for doers: popular aggregation meta-algorithms  -- Nicolas Vayatis -- 6.4.1.; title: Bagging  -- Nicolas Vayatis -- 6.4.2.; title: Boosting  -- Nicolas Vayatis -- 6.4.3.; title: Forests for bipartite ranking and scoring  -- Nicolas Vayatis -- 6.5.; title: Time for thinkers: Theory of aggregated rules  -- Nicolas Vayatis -- 6.5.1.; title: Aggregation of classification rules  -- Nicolas Vayatis -- 6.5.2.; title: Consistency of Forests  -- Nicolas Vayatis -- 6.5.3.; title: From bipartite consistency to K-partite consistency  -- Nicolas Vayatis -- 7.1.; title: Mixture models as a many-purpose tool  -- Christophe Biernacki -- 7.1.1.; title: Starting from applications  -- Christophe Biernacki -- 7.1.2.; title: The mixture model answer  -- Christophe Biernacki -- 7.1.3.; title: Classical mixture models  -- Christophe Biernacki -- 7.1.4.; title: Other models  -- Christophe Biernacki -- 7.2.; title: Estimation  -- Christophe Biernacki -- 7.2.1.; title: Overview  -- Christophe Biernacki -- 7.2.2.; title: Maximum likelihood and variants  -- Christophe Biernacki -- 7.2.3.; title: Theoretical difficulties related to the likelihood  -- Christophe Biernacki -- 7.2.4.; title: Estimation algorithms  -- Christophe Biernacki -- 7.3.; title: Model selection in density estimation  -- Christophe Biernacki -- 7.3.1.; title: Need to select a model  -- Christophe Biernacki -- 7.3.2.; title: Frequentist approach and deviance  -- Christophe Biernacki -- 7.3.3.; title: Bayesian approach and integrated likelihood  -- Christophe Biernacki -- 7.4.; title: Model selection in (semi-)supervised classification  -- Christophe Biernacki -- 7.4.1.; title: Need to select a model  -- Christophe Biernacki -- 7.4.2.; title: Error rates-based criteria  -- Christophe Biernacki -- 7.4.3.; title: A predictive deviance criterion  -- Christophe Biernacki -- 7.5.; title: Model selection in clustering  -- Christophe Biernacki -- 7.5.1.; title: Need to select a model  -- Christophe Biernacki -- 7.5.2.; title: Partition-based criteria  -- Christophe Biernacki -- 7.5.3.; title: The Integrated Completed Likelihood criterion  -- Christophe Biernacki -- 7.6.; title: Experiments on real data sets  -- Christophe Biernacki -- 7.6.1.; title: BIC: extra-solar planets  -- Christophe Biernacki -- 7.6.2.; title: AICcond/BIC/AIC/BEC/ecv: benchmark data sets  -- Christophe Biernacki -- 7.6.3.; title: AICcond/ecvV: textile data set  -- Christophe Biernacki -- 7.6.4.; title: BIC: social comparison theory  -- Christophe Biernacki -- 7.6.5.; title: NEC: marketing data  -- Christophe Biernacki -- 7.6.6.; title: ICL: prostate cancer data  -- Christophe Biernacki -- 7.6.7.; title: BIC: density estimation in the steel industry  -- Christophe Biernacki -- 7.6.8.; title: BIC: partitioning communes of Wallonia  -- Christophe Biernacki -- 7.6.9.; title: ICLbic/BIC: acoustic emission control  -- Christophe Biernacki -- 7.6.10.; title: ICLbic/ICL/BIC/ILbayes: a seabird data set  -- Christophe Biernacki -- 7.7.; title: Future methodological challenges  -- Christophe Biernacki -- 8.1.; title: The concept of minimal penalty  -- Pascal Massart -- 8.1.1.; title: A small number of models  -- Pascal Massart -- 8.1.2.; title: A large number of models  -- Pascal Massart -- 8.2.; title: Data-driven penalties  -- Pascal Massart -- 8.2.1.; title: From theory to practice  -- Pascal Massart -- 8.2.2.; title: The slope heuristics  -- Pascal Massart -- 9.1.; title: Introduction  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.2.; title: HD clustering: Curse or blessing?  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.2.1.; title: HD density estimation: Curse  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.2.2.; title: HD clustering: A mix of curse and blessing  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.2.3.; title: Intermediate conclusion  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.3.; title: Non-canonical models  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.3.1.; title: Gaussian mixture of factor analysers  -- Christophe Biernacki / -- Cathy Maugis-Rabusseau -- 9.3.2.; title: HD Gaussian mixture models -- Christophe Biernacki /-- Cathy Maugis-Rabusseau