## COMPSTAT: proceedings in computational statistics, 12th symposium held in Barcelona, Spain, 1996COMPSTAT symposia have been held regularly since 1974 when they started in Vienna. This tradition has made COMPSTAT a major forum for the interplay of statistics and computer sciences with contributions from many well known scientists all over the world. The scientific programme of COMPSTAT '96 covers all aspects of this interplay, from user-experiences and evaluation of software through the development and implementation of new statistical ideas. All papers presented belong to one of the three following categories: - Statistical methods (preferable new ones) that require a substantial use of computing; - Computer environments, tools and software useful in statistics; - Applications of computational statistics in areas of substantial interest (environment, health, industry, biometrics, etc.). |

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### Contents

Scientific Statistics Teaching Learning and the Computer | 3 |

Robust Procedures for Regression Models with ARIMA Errors | 27 |

Automatic Modelling of Daily Series of Economic Activity | 51 |

Copyright | |

24 other sections not shown

### Other editions - View all

COMPSTAT 2006 - Proceedings in Computational Statistics: 17th Symposium Held ... Alfredo Rizzi,Maurizio Vichi No preview available - 2006 |

COMPSTAT 2006 - Proceedings in Computational Statistics: 17th Symposium Held ... Alfredo Rizzi,Maurizio Vichi No preview available - 2006 |

COMPSTAT 2006 - Proceedings in Computational Statistics: 17th Symposium Held ... Alfredo Rizzi,Maurizio Vichi No preview available - 2006 |

### Common terms and phrases

additive models algorithm applications approach approximation assume asymptotic B-spline Bayesian bivariate blur bootstrap calculated classification coefficients computation consider convergence convex correlation covariance covariance matrix data depth data set defined denotes density described distribution efficient equation estimate example exponential exponential distribution exponential family failure forecasting function genetic algorithm Gibbs sampling given graphical input iterative Keywords likelihood function linear model linear regression logistic regression Markov chain matrix maximum likelihood means measurement error method Metropolis-Hastings algorithm minimal module Monte Carlo multivariate neural networks node nonlinear nonlinear regression nonparametric number of knots observations obtained optimal outliers parameters points posterior prediction problem procedure projection projection pursuit random regression model residual robust S-PLUS sample Section selection sequence simulation splines squares standard statistical step stochastic structure subset Table taxonomies techniques Tibshirani transformations updating values variables variance vector wavelet weights zero