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Author:   Paula Brito
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Physica-Verlag GmbH & Co
Edition:   2008
Dimensions:   Width: 15.50cm , Height: 3.60cm , Length: 23.50cm
Weight:   0.887kg
ISBN:  

9783790820836

ISBN 10:   3790820830
Pages:   573
Publication Date:   04 August 2008
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Mixed media product
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Keynote.- Nonparametric Methods for Estimating Periodic Functions, with Applications in Astronomy.- Advances on Statistical Computing Environments.- Back to the Future: Lisp as a Base for a Statistical Computing System.- Computable Statistical Research and Practice.- Implicit and Explicit Parallel Computing in R.- Classification and Clustering of Complex Data.- Probabilistic Modeling for Symbolic Data.- Monothetic Divisive Clustering with Geographical Constraints.- Comparing Histogram Data Using a Mahalanobis-Wasserstein Distance.- Computation for Graphical Models and Bayes Nets.- Iterative Conditional Fitting for Discrete Chain Graph Models.- Graphical Models for Sparse Data: Graphical Gaussian Models with Vertex and Edge Symmetries.- Parameterization and Fitting of a Class of Discrete Graphical Models.- Computational Econometrics.- Exploring the Bootstrap Discrepancy.- On Diagnostic Checking Time Series Models with Portmanteau Test Statistics Based on Generalized Inverses and.- New Developments in Latent Variable Models: Non-linear and Dynamic Models.- Computational Statistics and Data Mining Methods for Alcohol Studies.- Estimating Spatiotemporal Effects for Ecological Alcohol Systems.- A Directed Graph Model of Ecological Alcohol Systems Incorporating Spatiotemporal Effects.- Spatial and Computational Models of Alcohol Use and Problems.- Finance and Insurance.- Optimal Investment for an Insurer with Multiple Risky Assets Under Mean-Variance Criterion.- Inhomogeneous Jump-GARCH Models with Applications in Financial Time Series Analysis.- The Classical Risk Model with Constant Interest and Threshold Strategy.- Estimation of Structural Parameters in Crossed Classification Credibility Model Using Linear Mixed Models.- Information Retrieval for Text and Images.- A Hybrid Approach for Taxonomy Learning from Text.- Image and Image-Set Modeling Using a Mixture Model.- Strategies in Identifying Issues Addressed in Legal Reports.- Knowledge Extraction by Models.- Sequential Automatic Search of a Subset of Classifiers in Multiclass Learning.- Possibilistic PLS Path Modeling: A New Approach to the Multigroup Comparison.- Models for Understanding Versus Models for Prediction.- Posterior Prediction Modelling of Optimal Trees.- Model Selection Algorithms.- Selecting Models Focussing on the Modeller's Purpose.- A Regression Subset-Selection Strategy for Fat-Structure Data.- Fast Robust Variable Selection.- Models for Latent Class Detection.- Latent Classes of Objects and Variable Selection.- Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models.- Clustering via Mixture Regression Models with Random Effects.- Multiple Testing Procedures.- Testing Effects in ANOVA Experiments: Direct Combination of All Pair-Wise Comparisons Using Constrained Synchronized Permutations.- Multiple Comparison Procedures in Linear Models.- Inference for the Top-k Rank List Problem.- Random Search Algorithms.- Monitoring Random Start Forward Searches for Multivariate Data.- Generalized Differential Evolution for General Non-Linear Optimization.- Statistical Properties of Differential Evolution and Related Random Search Algorithms.- Robust Statistics.- Robust Estimation of the Vector Autoregressive Model by a Least Trimmed Squares Procedure.- The Choice of the Initial Estimate for Computing MM-Estimates.- Metropolis Versus Simulated Annealing and the Black-Box-Complexity of Optimization Problems.- Signal Extraction and Filtering.- Filters for Short Nonstationary Sequences: The Analysis of the Business Cycle.- Estimation of Common Factors Under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and Its Main Components.

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