COMPSTAT 2008 Proceedings in Computational Statistics /

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Brito, Paula (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Heidelberg : Physica-Verlag HD, 2008.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.