: Alfredo Rizzi, Maurizio Vichi
: Alfredo Rizzi, Maurizio Vichi
: COMPSTAT 2006 - Proceedings in Computational Statistics 17th Symposium Held in Rome, Italy, 2006
: Physica-Verlag
: 9783790817096
: 1
: CHF 115.60
:
: Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
: English
: 537
: Wasserzeichen
: PC/MAC/eReader/Tablet
: PDF
International Association for Statistical Computing The International Association for Statistical Computing (IASC) is a Section of the International Statistical Institute. The objectives of the Association are to foster world-wide interest in e?ective statistical computing and to - change technical knowledge through international contacts and meetings - tween statisticians, computing professionals, organizations, institutions, g- ernments and the general public. The IASC organises its own Conferences, IASC World Conferences, and COMPSTAT in Europe. The 17th Conference of ERS-IASC, the biennial meeting of European - gional Section of the IASC was held in Rome August 28 - September 1, 2006. This conference took place in Rome exactly 20 years after the 7th COMP- STAT symposium which was held in Rome, in 1986. Previous COMPSTAT conferences were held in: Vienna (Austria, 1974); West-Berlin (Germany, 1976); Leiden (The Netherlands, 1978); Edimbourgh (UK, 1980); Toulouse (France, 1982); Prague (Czechoslovakia, 1984); Rome (Italy, 1986); Copenhagen (Denmark, 1988); Dubrovnik (Yugoslavia, 1990); Neuchˆ atel (Switzerland, 1992); Vienna (Austria,1994); Barcelona (Spain, 1996);Bristol(UK,1998);Utrech (TheNetherlands,2000);Berlin( ermany, 2002); Prague (Czech Republic, 2004).
Preface5
Contents8
Part I Classification and Clustering25
Issues of robustness and high dimensionality in cluster analysis26
1 Introduction26
2 Multivariate t Distribution29
3 ML Estimation of Mixtures of t Components30
4 Factor Analysis Model for Dimension Reduction31
5 Mixtures of Normal Factor Analyzers32
6 Mixtures of t Factor Analyzers34
7 Discussion36
References36
Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories39
1 Introduction39
2 Fuzzy data time arrays, fuzzy multivariate time trajectories and dissimilarity measures40
3 Fuzzy K-means clustering models for fuzzy multivariate time trajectories [ CD03]43
4 Fuzzy K-medoids clustering for fuzzy multivariate time trajectories45
5 Application47
References50
Bootstrap methods for measuring classification uncertainty in latent class analysis52
1 Introduction52
2 Measures of classification uncertainty54
3 The bootstrap method55
4 Bootstrapping LC models56
5 Applications57
6 Discussion60
References61
A robust linear grouping algorithm63
1 Introduction63
2 Linear Grouping Algorithm64
3 Robust Linear Grouping Algorithm65
4 Examples67
5 Discussion70
References72
Computing and using the deviance with classification trees74
1 Introduction74
2 Tree induction principle: an illustrative example75
3 Validating the tree descriptive ability77
4 Computational aspects82
5 Conclusion84
References84
Estimation procedures for the false discovery rate: a systematic comparison for microarray data86
1 Introduction86
2 The testing problem87
3 The false discovery rate88
4 Estimation procedures89
5 The data sets92
6 Outline of the comparative study95
7 Results and conclusions96
Acknowledgment98
References98
A unifying model for biclustering*99
1 Illustrative Example99
2 Biclustering100
3 A Unifying Biclustering Model101
4 Data Analysis103
5 Concluding Remarks104
References105
Part II Image Analysis and Signal Processing107
Non-rigid image registration using mutual information108
1 Introduction108
2 Non-rigid registration109
3 The mutual information criterion112
4 Non-rigid registration using mutual information113
5 Validation116
References117
Musical audio analysis using sparse representations121
1 Introduction121
2 Finding Sparse Representations122
3 Sparse Representations for Music Transcription125
4 Source Separation128
5 Conclusions130
Acknowledgements130
References131
Robust correspondence recognition for computer vision134
1 Introduction134
2 Stability and Digraph Kernels138
3 Properties of Strict Sub-Kernels142
4 A Simple Algorithm for Interval Orientations144
5 Discussion144
References145
Blind superresolution147
1 Introduction147
2 Mathematical Model150
3 Blind Superresolution152
4 Experiments155
5 Conclusions156
Acknowledgment157
References157
Analysis of Music Time Series160
1 Introduction160
2 Model building161
3 Applied models164
4 Studies166
5 Conclusion171
References172
Part III Data Visualization173
Tying up the loose ends in simple, multiple, joint correspondence analysis174
1 Introduction174
2 Basic CA theory175
3 Multiple and joint correspondence analysis177
4 Data sets used as illustrations177
5 Measuring variance and comparing different tables178
6 The myth of the influential outlier179
7 The scaling problem in CA180
8 To rotate or not to rotate186
9 Statistical significance of results189
10 Loose ends in MCA and JCA191
Acknowledgments194
References194
3 dimensional parallel coordinates plot and its use for variable selection197
1 Introduction197
2 Parallel coordinates plot and interactive operations198
3 3 dimensional parallel coordinates plot199
4 Implementation of 3D