Some Recent Trends in Applied Stochastic Modeling and Multidimensional Data Analysis

This special issue presents a selection of nine papers dealing with various topics of multidimensional data analysis, coming from very different fields. Some papers are proposing new Stochastic Modeling, while others are more concerned with the improvement of exploratory Data Analysis techniques. Most of them combine Stochastic Modeling with techniques of approximation that are either combinatorial or geometrical. Their common feature is the strong relation with applications.

[1]  J. Pagès,et al.  Analyse factorielle multiple appliquée aux variables qualitatives et aux données mixtes , 2002 .

[2]  Dominique Pastor,et al.  A theoretical result for processing signals that have unknown distributions and priors in white Gaussian noise , 2008, Comput. Stat. Data Anal..

[3]  Jean-Jacques Daudin,et al.  Estimation of the conditional risk in classification: The swapping method , 2008, Comput. Stat. Data Anal..

[4]  Claus Weihs,et al.  Detection of chatter vibration in a drilling process using multivariate control charts , 2008, Comput. Stat. Data Anal..

[5]  Jérôme Pagès,et al.  Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency data , 2008, Comput. Stat. Data Anal..

[6]  Ana M. Aguilera,et al.  Functional PLS logit regression model , 2007, Comput. Stat. Data Anal..

[7]  J. E. Jackson.  Multivariate quality control , 1985 .

[8]  J. Gower.  A General Coefficient of Similarity and Some of Its Properties , 1971 .

[9]  Regina Y. Liu.  Control Charts for Multivariate Processes , 1995 .

[10]  Gérard Govaert,et al.  Block clustering with Bernoulli mixture models: Comparison of different approaches , 2008, Comput. Stat. Data Anal..

[11]  Ana M. Aguilera,et al.  Forecasting binary longitudinal data by a functional PC-ARIMA model , 2008, Comput. Stat. Data Anal..

[12]  Wenceslao González-Manteiga,et al.  Statistics for Functional Data , 2007, Comput. Stat. Data Anal..

[13]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[14]  G. Saporta.  Simultaneous Analysis of Qualitative and Quantitative Data , 1990 .

[15]  Michel Tenenhaus,et al.  Analyse en composantes principales d'un ensemble de variables nominales ou numériques , 1977 .

[16]  Pierre Tufféry,et al.  A Hidden Markov Model applied to the protein 3D structure analysis , 2008, Comput. Stat. Data Anal..

[17]  Alexandre Villeminot,et al.  Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set , 2007, Comput. Stat. Data Anal..

[18]  Eric Moulines,et al.  Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Angela Montanari,et al.  Independent factor discriminant analysis , 2008, Comput. Stat. Data Anal..

[20]  Michael Greenacre,et al.  Exploratory data analysis leading towards the most interesting simple association rules , 2008, Comput. Stat. Data Anal..