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Proceedings of COMPSTAT'2010 : 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers / edited by Yves Lechevallier, Gilbert Saporta
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Heidelberg : Springer-Verlag Berlin Heidelberg, 2010Description: Online-Ressource (XXX, 621p. 105 illus, digital)ISBN:- 9783790826043
- 519.5
- 519.50285
- QA276-280
- QA276
Contents:
Summary: Part I: Keynote -- Part II: ABC Methods for Genetic Data -- Part III: Algorithms for Robust Statistics -- Part IV: Brain Imaging -- Part V: Computational Econometrics -- Part VI: Computer-Intensive Actuarial Methods -- Part VII: Data Stream Mining -- Part VIII: Functional Data Analysis -- Part IX: Kernel Methods.-Part X: Monte Carlo Methods in System Safety, Reliability and Risk Analysis -- Part XI: Optimization Heuristics in Statistical Modelling -- Part XII: Spatial Statistics / Spatial Epidemiology -- Part XIII: ARS Session (Financial) Time Series -- Part XIV: KDD Session: Topological Learning -- Part XV: IFCS Session: New Developments in Two or Highermode Clustering; Model Based Clustering and Reduction for High Dimensional Data -- Part XVI: Selected Contributed Papers -- Part XVII: E-supplementary material: Contributed Papers.Summary: Proceedings of the 19th international symposium on computational statistics, held in Paris august 22-27, 2010.Together with 3 keynote talks, there were 14 invited sessions and more than 100 peer-reviewed contributed communications.PPN: PPN: 1650524773Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXMS | ZDB-2-SMA
Preface; Acknowledgements; Sponsors; Contents; Part I Keynote; Complexity Questions in Non-Uniform Random Variate Generation; Computational Statistics Solutions for Molecular Biomedical Research: A Challenge and Chance for Both; The Laws of Coincidence; Part II ABC Methods for Genetic Data; Choosing the Summary Statistics and the Acceptance Rate in Approximate Bayesian Computation; Integrating Approximate Bayesian Computation with Complex Agent-Based Models for Cancer Research; Part III Algorithms for Robust Statistics; Robust Model Selection with LARS Based on S-estimators
Robust Methods for Compositional DataDetecting Multivariate Outliers Using Projection Pursuit with Particle Swarm Optimization; Part IV Brain Imaging; Imaging Genetics: Bio-Informatics and Bio-Statistics Challenges; The NPAIRS Computational Statistics Framework for Data Analysis in Neuroimaging; Part V Computational Econometrics; Bootstrap Prediction in Unobserved Component Models; Part VI Computer-Intensive Actuarial Methods; A Numerical Approach to Ruin Models with Excess of Loss Reinsurance and Reinstatements; Computation of the Aggregate Claim Amount Distribution Using R and Actuar
Applications of Multilevel Structured Additive Regression Models to Insurance DataPart VII Data Stream Mining; Temporally-Adaptive Linear Classification for Handling Population Drift in Credit Scoring; Large-Scale Machine Learning with Stochastic Gradient Descent; Part VIII Functional Data Analysis; Anticipated and Adaptive Prediction in Functional Discriminant Analysis; Bootstrap Calibration in Functional Linear Regression Models with Applications; Empirical Dynamics and Functional Data Analysis; Part IX Kernel Methods; Indefinite Kernel Discriminant Analysis
Data Dependent Priors in PAC-Bayes BoundsPart X Monte Carlo Methods in System Safety, Reliability and Risk Analysis; Some Algorithms to Fit some Reliability Mixture Models under Censoring; Computational and Monte-Carlo Aspects of Systems for Monitoring Reliability Data; Part XI Optimization Heuristics in Statistical Modelling; Evolutionary Computation for Modelling and Optimization in Finance; Part XII Spatial Statistics / Spatial Epidemiology; Examining the Association between Deprivation Profiles and Air Pollution in Greater London using Bayesian Dirichlet Process Mixt
Assessing the Association between Environmental Exposures and Human HealthPart XIII ARS Session (Financial) Time Series; Semiparametric Seasonal Cointegrating Rank Selection; Estimating Factor Models for Multivariate Volatilities: An Innovation Expansion Method; Multivariate Stochastic Volatility Model with Cross Leverage; Part XIV KDD Session: Topological Learning; Bag of Pursuits and Neural Gas for Improved Sparse Coding; On the Role and Impact of the Metaparameters in t-distributed Stochastic Neighbor Embedding; Part XV IFCS Session: New Developments in Two or Highermode Clustering
Model Based Clustering and Reduction for High Dimensional Data
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