2006

Compstat 2006 - Proceedings in Computational Statistics

17th Symposium Held in Rome, Italy, 2006

Editors:

ISBN: 978-3-7908-1708-9 (Print) 978-3-7908-1709-6 (Online)

Table of contents (42 chapters)

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  1. Front Matter

    Pages I-XXV

  2. Classification and Clustering

    1. Front Matter

      Pages 1-1

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      Pages 3-15

      Issues of robustness and high dimensionality in cluster analysis

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      Pages 17-29

      Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories

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      Pages 31-41

      Bootstrap methods for measuring classification uncertainty in latent class analysis

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      Pages 43-53

      A robust linear grouping algorithm

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      Pages 55-66

      Computing and using the deviance with classification trees

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      Pages 67-79

      Estimation procedures for the false discovery rate: a systematic comparison for microarray data

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      Pages 81-88

      A unifying model for biclustering

  3. Image Analysis and Signal Processing

    1. Front Matter

      Pages 89-89

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      Pages 91-103

      Non-rigid image registration using mutual information

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      Pages 105-117

      Musical audio analysis using sparse representations

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      Pages 119-131

      Robust correspondence recognition for computer vision

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      Pages 133-145

      Blind superresolution

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      Pages 147-159

      Analysis of Music Time Series

  4. Data Visualization

    1. Front Matter

      Pages 161-161

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      Pages 163-185

      Tying up the loose ends in simple, multiple, joint correspondence analysis

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      Pages 187-195

      3 dimensional parallel coordinates plot and its use for variable selection

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      Pages 197-207

      Geospatial distribution of alcohol-related violence in Northern Virginia

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      Pages 209-219

      Visualization in comparative music research

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      Pages 221-230

      Exploratory modelling analysis: visualizing the value of variables

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      Pages 231-242

      Density estimation from streaming data using wavelets

  5. Multivariate Analysis

    1. Front Matter

      Pages 243-243

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      Pages 245-260

      Reducing conservatism of exact small-sample methods of inference for discrete data

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      Pages 261-269

      Symbolic data analysis: what is it?

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