Contents
Part I Classification and Clustering
Issues of robustness and high dimensionality in cluster
analysis
Kaye Basford, Geoff McLachlan, Richard Bean . . . . . . . . . . . . . . . . . . . . . .
3
Fuzzy K -medoids clustering models for fuzzy multivariate
time trajectories
Renato Coppi, Pierpaolo D’Urso, Paolo Giordani . . . . . . . . . . . . . . . . . . . . 17
Bootstrap methods for measuring classification uncertainty in
latent class analysis
José G. Dias, Jeroen K. Vermunt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
A robust linear grouping algorithm
Greet Pison, Stefan Van Aelst, Ruben H. Zamar . . . . . . . . . . . . . . . . . . . . . 43
Computing and using the deviance with classification trees
Gilbert Ritschard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Estimation procedures for the false discovery rate: a
systematic comparison for microarray data
Michael G. Schimek, Tomáš Pavlı́k . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
A unifying model for biclustering
Iven Van Mechelen, Jan Schepers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Part II Image Analysis and Signal Processing
Non-rigid image registration using mutual information
Frederik Maes, Emiliano D’Agostino, Dirk Loeckx, Jeroen Wouters,
Dirk Vandermeulen, Paul Suetens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
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Musical audio analysis using sparse representations
Mark D. Plumbley, Samer A. Abdallah, Thomas Blumensath, Maria G.
Jafari, Andrew Nesbit, Emmanuel Vincent, Beiming Wang . . . . . . . . . . . . 105
Robust correspondence recognition for computer vision
Radim Šára . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Blind superresolution
Filip Šroubek, Gabriel Cristóbal, Jan Flusser . . . . . . . . . . . . . . . . . . . . . . . . 133
Analysis of Music Time Series
Claus Weihs, Uwe Ligges, Katrin Sommer . . . . . . . . . . . . . . . . . . . . . . . . . . 147
Part III Data Visualization
Tying up the loose ends in simple, multiple, joint
correspondence analysis
Michael Greenacre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
3 dimensional parallel coordinates plot and its use for variable
selection
Keisuke Honda, Junji Nakano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Geospatial distribution of alcohol-related violence in Northern
Virginia
Yasmin H. Said, Edward J. Wegman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Visualization in comparative music research
Petri Toiviainen, Tuomas Eerola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Exploratory modelling analysis: visualizing the value of
variables
Antony Unwin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
Density estimation from streaming data using wavelets
Edward J. Wegman, Kyle A. Caudle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Part IV Multivariate Analysis
Reducing conservatism of exact small-sample methods of
inference for discrete data
Alan Agresti, Anna Gottard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
Symbolic data analysis: what is it?
Lynne Billard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
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XI
A dimensional reduction method for ordinal three-way
contingency table
Luigi D’Ambra, Biagio Simonetti and Eric J. Beh . . . . . . . . . . . . . . . . . . . 271
Operator related to a data matrix: a survey
Yves Escoufier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
Factor interval data analysis and its application
Wang Huiwen, Henry M.K. Mok, Li Dapeng . . . . . . . . . . . . . . . . . . . . . . . . 299
Identifying excessively rounded or truncated data
Kevin H. Knuth, J. Patrick Castle, Kevin R. Wheeler . . . . . . . . . . . . . . . . 313
Statistical inference and data mining: false discoveries control
Stéphane Lallich, Olivier Teytaud and Elie Prudhomme . . . . . . . . . . . . . . . 325
Is ‘Which model . . .?’ the right question?
Nicholas T. Longford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
Use of latent class regression models with a random intercept
to remove the effects of the overall response rating level
Jay Magidson, Jeroen K. Vermunt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
Discrete functional data analysis
Masahiro Mizuta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
Self organizing MAPS: understanding, measuring and
reducing variability
Patrick Rousset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
Parameterization and estimation of path models for
categorical data
Tamás Rudas, Wicher Bergsma, Renáta Németh . . . . . . . . . . . . . . . . . . . . . 383
Latent class model with two latent variables for analysis of
count data
Kazunori Yamaguchi, Naoko Sakurai, Michiko Watanabe . . . . . . . . . . . . . . 395
Part V Web Based Teaching
Challenges concerning web data mining
Wolfgang Gaul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
e-Learning statistics – a selective review
Wolfgang Härdle, Sigbert Klinke, Uwe Ziegenhagen . . . . . . . . . . . . . . . . . . . 417
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Quality assurance of web based e-Learning for statistical
education
Taerim Lee, Jungjin Lee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
Part VI Algorithms
Genetic algorithms for building double threshold generalized
autoregressive conditional heteroscedastic models of time
series
Roberto Baragona, Francesco Battaglia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
Nonparametric evaluation of matching noise
Pier Luigi Conti, Daniela Marella, Mauro Scanu . . . . . . . . . . . . . . . . . . . . . 453
Subset selection algorithm based on mutual information
Moon Y. Huh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461
Visiting near-optimal solutions using local search algorithms
Sheldon H. Jacobson, Shane N. Hall, Laura A. McLay . . . . . . . . . . . . . . . . 471
The convergence of optimization based GARCH estimators:
theory and application
Peter Winker, Dietmar Maringer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483
The stochastics of threshold accepting: analysis of an
application to the uniform design problem
Peter Winker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495
Part VII Robustness
Robust classification with categorical variables
Andrea Cerioli, Marco Riani, Anthony C. Atkinson . . . . . . . . . . . . . . . . . . . 507
Multiple group linear discriminant analysis: robustness and
error rate
Peter Filzmoser, Kristel Joossens, Christophe Croux . . . . . . . . . . . . . . . . . . 521
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533
Part on CD
Part VIII Categorical Data Analysis
Contents
XIII
Measuring compliance of taxpayers using correspondence
analysis: evidence from Turkey
Ali Çelykkaya, Hüseyin Gürbüz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543
Multiple taxicab correspondence analysis
Choulakian, V. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557
A residual from updating based approach for multiple
categorical ordinal responses
Giulio D’Epifanio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565
A method for analysis of categorical data for robust product
or process design
Serkan Erdural, Gülser Köksal, and Özlem İlk . . . . . . . . . . . . . . . . . . . . . . . 573
Implementation of textile plot
Natsuhiko Kumasaka, Ritei Shibata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581
Acceleration of the EM and ECM algorithms for log-linear
models with missing data
Masahiro Kuroda, Michio Sakakihara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591
Modelling multivariate count data
Aristidis K. Nikoloulopoulos, Dimitris Karlis . . . . . . . . . . . . . . . . . . . . . . . . 599
Comparison of some approaches to clustering categorical data
Rezankova H., Husek D., Kudova P., Snasel V. . . . . . . . . . . . . . . . . . . . . . . 607
A comparison of the powers of the Chi-Square test statistic
with the discrete Kolmogorov-Smirnov and Cramér-von Mises
test statistics
Michael Steele, Janet Chaseling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615
Numerical comparison of approximations of the distributions
of statistics for multinomial homogeneity test
Nobuhiro Taneichi, Yuri Sekiya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623
Prediction of solvability dependencies between dichotomous
test items: a local order-theoretic measure of association
Ali Ünlü, Michael D. Kickmeier-Rust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631
Part IX Multivariate Data Analysis II
A new computational procedure for treating ANOVA panel
data models with grouped or missed observations and
log-concave errors
Carmen Anido, Carlos Rivero, Teofilo Valdes . . . . . . . . . . . . . . . . . . . . . . . . 641
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Fitting Archimedean copulas to bivariate geodetic data
Tomáš Bacigál, Magda Komornı́ková . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649
Continuum redundancy P LS regression: a simple continuum
approach. Application to epidemiological data.
Stéphanie Bougeard, Mohamed Hanafi, El Mostafa Qannari . . . . . . . . . . . 657
A-optimal chemical balance weighing design with diagonal
variance matrix of errors
Bronislaw Ceranka, Malgorzata Graczyk . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665
Expected convex hull trimming of a data set
Ignacio Cascos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673
Path modeling: partial maximum likelihood approach vs
partial least squares approach
Christian Derquenne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681
An asymptotic two dependent samples test of equality of
means of fuzzy random variables
González-Rodrı́guez, Gil, Colubi, Ana, Gil, Angeles M., D’Urso,
Pierpaolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689
On the estimation of linear models with interval-valued data
González-Rodrı́guez, Gil, Colubi, Ana, Coppi, Renato, Giordani, Paolo . . 697
Ternary designs leading to the optimum chemical balance
weighing designs with correlated errors
Bronislaw Ceranka, Malgorzata Graczyk . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705
Updating on the kernel density estimation for compositional
data
Martı́n-Fernández, J. A., Chacón-Durán, J. E., Mateu-Figueras, G. . . . . 713
Understanding PLS path modeling parameters estimates:
a study based on Monte Carlo simulation and customer
satisfaction surveys
Emmanuel Jakobowicz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721
Asymptotic standard errors in independent factor analysis
Angela Montanari, Cinzia Viroli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729
Approximate cumulants of the distribution of sample
generalized measure of skewness
Shigekazu Nakagawa, Naoto Niki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737
Using growth curve model in anthropometric data analysis
Anu Roos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743
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Algebraic rank analysis of tensor data through Gröbner Basis
Toshio Sakata, Ryuei Nishii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751
Bootstrapping Spearman’s multivariate rho
Friedrich Schmid, Rafael Schmidt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759
Using structural equation modeling to discover the hidden
structure of ck data
Ene-Margit Tiit, Mare Vähi, Kai Saks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767
Instrumental weighted variables - algorithm
Jan Ámos Vı́šek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777
Sensitivity analysis in kernel principal component analysis
Yoshihiro Yamanishi, Yutaka Tanaka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787
Proximity graphs for image retrieval
Djamel Abdelkader Zighed, Hakim Hacid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795
Part X Classification and Clustering II
Visualizing some multi-class erosion data using kernel
methods
Anna Bartkowiak, Niki Evelpidou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805
High dimensional data clustering
Charles Bouveyron, Stéphane Girard, Cordelia Schmid . . . . . . . . . . . . . . . . 813
Anticipated prediction in discriminant analysis on functional
data for binary response
G. Damiana Costanzo, Cristian Preda, Gilbert Saporta . . . . . . . . . . . . . . . 821
Multidimensional visualisation of time series and the
construction of acceptance regions in a PCA biplot
Sugnet Gardner, Niël J le Roux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829
The profile’s assessment grid as a tool for clinical praxis. An
application to functional disability
K. Gibert, R. Annicchiarico, C. Caltagirone . . . . . . . . . . . . . . . . . . . . . . . . 837
Large-scale kernel discriminant analysis with application to
quasar discovery
Alexander Gray, Ryan Riegel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845
Fitting finite mixtures of linear regression models with
varying & fixed effects in R
Bettina Grün, Friedrich Leisch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853
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Neural network based Boolean factor analysis of parliament
voting
Frolov A.A., Polyakov P.Y., Husek D., Rezankova H. . . . . . . . . . . . . . . . . . 861
Dynamic clustering of histograms using Wasserstein metric
Antonio Irpino, Rosanna Verde, Yves Lechevallier . . . . . . . . . . . . . . . . . . . 869
Generalized discriminant rule for binary data when training
and test populations differ on their descriptive parameters.
Julien Jacques, Christophe Biernacki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 877
Extending standard cluster algorithms to allow for group
constraints
Friedrich Leisch, Bettina Grün . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 885
Discriminant analysis of time series using wavelets
Elizabeth A. Maharaj, Andrés M. Alonso . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893
A randomness test for stable data
Adel Mohammadpour, Ali Mohammad-Djafari, John P. Nolan . . . . . . . . . 901
An algorithm for density estimation in a network space
Schoier Gabriella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 909
V-MDAV: a multivariate microaggregation with variable
group size
Agusti Solanas, Antoni Martı́nez-Ballesté . . . . . . . . . . . . . . . . . . . . . . . . . . . 917
A tree structured classifier for symbolic class description
Suzanne Winsberg, Edwin Diday, M. Mehdi Limam . . . . . . . . . . . . . . . . . . 927
Part XI Data Mining
On the identification of unknown authors: a comparison
between SVM’s and non parametric methods.
Paola Cerchiello . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 941
Angel algorithm: a novel globla estimation level algorithm for
discovering extended association rules between any variable
types
Angelos Chatzigiannakis-Kokkidis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 949
Customer relationship: a survival analysis approach
Silvia Figini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 959
Evaluating modern graphics-new standards or old?
Hilary Green . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 967
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XVII
Detection of breast cancer using an asymmetric entropy
measure
Simon Marcellin, Djamel A. Zighed, Gilbert Ritschard . . . . . . . . . . . . . . . . 975
Part XII Biostatistics
Analyzing associations in multivariate binary time series
Roland Fried, Silvia Kuhls, Isabel Molina . . . . . . . . . . . . . . . . . . . . . . . . . . . . 985
Stochastic Gompertz diffusion process with threshold
parameter
R. Gutiérrez, R. Gutiérrez-Sánchez, A. Nafidi, E. Ramos-Ábalos . . . . . . . 993
A unifying approach to non-inferiority, equivalence and
superiority tests via multiple decision processes
Chihiro Hirotsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 001
A comparison of parameter estimates in standard logistic
regression using WinBugs MCMC and MLE methods in R
for different sample sizes
Masoud Karimlou, Gholamraza Jandaghi, Kazem Mohammad, Rory
Wolfe, Kmal Azam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 007
The stochastic QT–clust algorithm: evaluation of stability and
variance on time–course microarray data
Theresa Scharl, Friedrich Leisch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 015
Bootstrap estimation of disease incidence proportion with
measurement errors
Masataka Taguri, Hisayuki Tsukuma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 023
A threshold of disequilibrium parameter using cumulative
relative frequency of Haplotypes on Multiallelic model
Makoto Tomita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 031
Parametric robust regression of correlated binary data on
cluster-specific covariates
Tsung-Shan Tsou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 041
Bayesian generalized linear models using marginal likelihoods
Jinfang Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 049
Part XIII Resampling Methods
XVIII Contents
Goodness-of-fit tests based on the empirical characteristic
function
V. Alba-Fernández, M.D. Jiménez-Gamero, J. Muñoz Garcı́a . . . . . . . . . 1. 059
The bootstrap methodology in time series forecasting
C. Cordeiro, M. Neves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 067
Continuous bootstrapping
Naoto Niki, Yoko Ono . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 075
A measure of performance of self compacting concrete
mixtures
Sandra Nunes, Helena Figueiras, Paula Milheiro-Oliveira, Joana
Sousa-Coutinho, Joaquim Figueiras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 079
Test of mean difference in longitudinal data based on block
resampling
Hirohito Sakurai, Masaaki Taguri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 087
Part XIV Functional Data Analysis
A model selection criterion for functional PLS logit regression
Aguilera, A.M., Escabias, M., Valderrama, M.J. . . . . . . . . . . . . . . . . . . . . 1. 097
Functional supervised and unsupervised classification of gene
expression data
Yuko Araki, Sadanori Konishi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 105
An application of relative projection pursuit for functional
data to human growth
Shintaro Hiro, Yuriko Komiya, Hiroyuki Minami, Masahiro Mizuta . . . . 1. 113
Boosting for functional data
Nicole Krämer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 121
Stochastic model for PSA system
Hassan Naseri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 129
Part XV Time Series Analysis and Spatial Analysis
Introducing interval time series: accuracy measures
Javier Arroyo, Carlos Maté . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 139
Estimation of frequency in SCLM models
Muguel Artiach, Josu Arteche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 147
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Out-of-sample decomposition of a Granger causality measure
Sarah Gelper, Christophe Croux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 155
Tourism, openness and growth triangle in a small island: the
case of North Cyprus
Salih Turan Katircioglu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 163
Switching by aggregation operators in regime-switching
models
Radko Mesiar, Jozef Komornı́k, Magda Komornı́ková, Danuša Szökeová 1. 171
Residuals in time series models
José Alberto Mauricio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 179
Testing the adequacy of regime-switching time series models
based on aggregation operators
Jozef Komornı́k, Magda Komornı́ková, Danuša Szökeová . . . . . . . . . . . . . 1. 187
Simulation of spatial dependence structures
Rosa Marı́a Crujeiras, Rubén Fernández-Casal . . . . . . . . . . . . . . . . . . . . . . 1. 193
Semiparametric estimation of spatiotemporal anisotropic
long-range dependence
M.P. Frı́as, M.D. Ruiz-Medina, J.M. Angulo, F.J. Alonso . . . . . . . . . . . . 1. 201
Spatial structure for multidimensional spatial lattice data
Fumio Ishioka, Koji Kurihara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 209
Spectral estimation in a random effect model
Luengo I., Hernández, C. N., Saavedra P. . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 217
Hidden Markov Random Field and FRAME modelling for
TCA image analysis
Katy Streso, Francesco Lagona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 225
Part XVI Nonparametric Statistics and Smoothing
A cross-validation method for choosing the pilot bandwidth
in kernel density estimation
J.E. Chacón, J. Montanero, A.G. Nogales, P. Pérez . . . . . . . . . . . . . . . . . 1. 235
A bootstrap approach to the nonparametric estimation of a
regression function from backward recurrence times
J.A. Cristóbal, P. Olave, J. T. Alcalá . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 243
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Contents
The fitting of multifunctions: an approach to nonparametric
multimodal regression
Jochen Einbeck, Gerhard Tutz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 251
Using nonparametric regression to find local departures from
a parametric model
Mario Francisco-Fernández, Jean Opsomer . . . . . . . . . . . . . . . . . . . . . . . . . 1. 259
Smoothing with curvature constraints based on boosting
techniques
Florian Leitenstorfer, Gerhard Tutz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 267
Bandwidth selectors performance through SiZer Map
Martı́nez-Miranda, M.D., Raya-Miranda, R., González-Manteiga, W.,
González-Carmona, A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 277
Computing confidence bounds for the mean of a Lévy-stable
distribution
Djamel Meraghni, Abdelhakim Necir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 285
Local polynomial estimator in a regression model with
correlated errors and missing data
Pérez-González, A., Vilar-Fernández, J.M., González-Manteiga, W. . . . 1. 293
Confidence intervals of the tail index
Jan Picek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 301
Part XVII Statistical Software and Optimization Algorithms
A model of optimum tariff in vehicle fleet insurance
K. Boukhetala, F.Belhia, R.Salmi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 311
On some nonresponse correction for the finite population
median estimator
Wojciech Gamrot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 319
An R-package for the surveillance of infectious diseases
Michael Höhle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 327
A basic graphical user interface for R: R-interactive
Angelo M. Mineo, Alfredo Pontillo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 335
mathStatica: symbolic computational statistics
Colin Rose, Murray D. Smith . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 343
Simulator for process reliability with reuse of component in
time bound software projects
Ritu Soni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 349
Contents
XXI
Adaptive population-based algorithm for global optimization
Josef Tvrdı́k, Ivan Křivý, Ladislav Mišı́k . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 363
Part XVIII Computational Bayesian Methods
Bayesian inference on the scalar skew-normal distribution
Stefano Cabras, Walter Racugno, Laura Ventura . . . . . . . . . . . . . . . . . . . . 1. 375
On bayesian design in finite source queues
M. Eugenia Castellanos, Javier Morales, Asunción M. Mayoral, Roland
Fried, Carmen Armero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 383
A discrete kernel sampling algorithm for DBNs
Theodore Charitos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 391
MARS: selecting basis and knots with the empirical Bayes
method
Wataru Sakamoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 399
Gaussian representation of independence models over four
random variables
Petr Šimeček . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 407
Probabilistic approach for statistical learning in administrative
archives
Vincenzo Spinelli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 415
Recovery of the coefficients of the elastodynamics equation
using two statistical estimators
Samih Zein, Nabil Nassif, Jocelyne Erhel, Édouard Canot . . . . . . . . . . . . 1. 423
Part XIX Computational Methods in Official Statistics
A log-linear model to estimate cheating in randomizedresponse
M. Cruyff, A. van der Hout, P. van der Heijden, U. Böckenholt . . . . . . . 1. 433
Maximum likelihood estimation of regression parameters
in statistical matching: a comparison between different
approaches
Marcello D’Orazio, Marco Di Zio, Mauro Scanu . . . . . . . . . . . . . . . . . . . . . 1. 441
Imputation by conditional distribution using Gaussian copula
Ene Käärik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 449
XXII
Contents
Missing value imputation methods for multilevel data
Antonella Plaia, Anna Lisa Bondı̀ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 457
Experiences of variance estimation for relative poverty
measures and inequality indicators in official sample surveys
Claudia Rinaldelli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 467
The R package sampling, a software tool for training in official
statistics and survey sampling
Yves Tillé, Alina Matei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 475
Part XX Computational Statistics in Finance, Industry and
Economics
Modeling mixed spatial processes and spatio-temporal
dynamics in information-theoretic frameworks
Rosa Bernardini Papalia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 487
A realised volatility measurement using quadratic variation
and dealing with microstructure effects
Willie J Conradie, Cornel Du Toit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 495
Nonparametric statistical analysis of ruin probability under
conditions of “small” and “large” claims
Pier Luigi Conti, Esterina Masiello . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 505
New models to compute short-run forecasts of electricity
prices: application to the spanish market case
Carolina Garcı́a Martos, Julio Rodrı́guez, Marı́a Jesús Sánchez . . . . . . . 1. 515
Adaptive modelling of conditional variance function
Juutilainen I., Röning J. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 523
Some remarks on measuring sigma coefficient in six sigma
multidimensional processes
Grzegorz Kończak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 533
A comparison between probabilistic and possibilistic models
for data validation
V. Köppen, H. J. Lenz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 543
Threshold volatility models: forecasting performance
Márquez M.D., Muñoz M.P., Martı́-Recober M., Acosta L.M. . . . . . . . . . 1. 551
Descriptive statistics for boxplot variables
Carlos Maté, Javier Arroyo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 561
Contents XXIII
Conditionally heteroskedastic factorial HMMs for time series
in finance
Mohamed Saidane, Christian Lavergne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 571
Modelling FX new bid prices as a clustered marked point
process
Ritei Shibata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 581
Bayesian inference for regime switching stochastic volatility
model with fat-tails and correlated errors
Tomohiro Ando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 591
The Chi-square test when the expected frequencies are less
than 5
Wai Wan Tsang, Kai Ho Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 601
Part XXI Microarray Data Analysis
Modelling the background correction in microarray data
analysis
Angelo M. Mineo, Calogero Fede, Luigi Augugliaro, Mariantonietta
Ruggieri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 611
LASSO estimators in linear regression for microarray data
Angela Recchia, Ernst Wit, Alessio Pollice . . . . . . . . . . . . . . . . . . . . . . . . . 1. 619
Stochastic oscillations in genetic regulatory networks.
Application to microarray experiments
Simon Rosenfeld . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 627
Part XXII Statistical Education and Web Based Teaching
On the difficulty to design arabic e-learning system in statistic
Taleb Ahmad, Wolfgang Härdle, Julius Mungo . . . . . . . . . . . . . . . . . . . . . . 1. 637
The graphical analysis of the ANOVA and regression models
parameters significance
Irina Arhipova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 643
Data collection and document generation system for
data-oriented approaches
Yuichi Mori, Yoshiro Yamamoto, Hiroshi Yadohisa . . . . . . . . . . . . . . . . . . 1. 651
An eLearning website for the design and analysis of
experiments with application to chemical processes
D.C. Woods, D.M. Grove, I. Liccardi, S.M. Lewis, J.G. Frey . . . . . . . . . 1. 659
XXIV Contents
Part XXIII Posters
Asymptotic properties in a semi-functional partial linear
regression model
Germán Aneiros-Peréz, Philippe Vieu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 669
Finding groups in a diagnostic plot
G. Brys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 677
Model-calibration method in the distribution function’s
estimation
Sergio Martı́nez; Marı́a del Mar Rueda; Helena Martı́nez; Ismael
Sánchez-Borrego, Silvia González, Juan F. Muñoz . . . . . . . . . . . . . . . . . . . 1. 683
Computational aspects of sequential monte carlo approach to
image restoration
Ken Nittono . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 691
Two-term Edgeworth expansion of the distributions of the
maximum likelihood estimators in factor analysis under
nonnormality
Haruhiko Ogasawara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 699
Estimating quantiles under sampling in two occasions with an
effective use of auxiliary information
M. Rueda, J.F. Muñoz, S. González, I. Sánchez, S. Martı́nez and A.
Arcos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 707
Application of new model-based and model-assisted methods
for estimating the finite population mean of the IBEX’35
stock market data
M. Rueda, I. Sánchez-Borrego, S. González, J.F. Muñoz, S. Martı́nez . . 1. 715
Comparison of parametric and non-parametric estimators of
the population spectrum
P. Saavedra, C. N. Hernández, A. Santana, I. Luengo, J. Artiles . . . . . 1. 723
Power comparison of nonparametric test for latent root of
covariance matrix in two populations
Shin-ichi Tsukada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 731
Bootstrap tests for nonparametric comparison of regression
curves with dependent errors
Vilar-Fernández, J.A., Vilar-Fernández, J.M., González-Manteiga, W. . 1. 739