COMPSTAT: Proceedings in Computational Statistics ; 14th Symposium Held in Utrecht, The Netherlands, 2000 ; with 96 TablesJelke G. Bethlehem, Peter G.M. van der Heijden Springer Science & Business Media, 2000 M08 4 - 540 pages This Volume contains the Keynote, Invited and Full Contributed papers presented at COMPSTAT 2000. A companion volume (Jansen & Bethlehem, 2000) contains papers describing the Short Communications and Posters. COMPST AT is a one week conference held every two years under the auspices of the International Association of Statistical Computing, a section of the International Statistical Institute. COMPST AT 2000 is jointly organised by the Department of Methodology and Statistics of the Faculty of Social Sciences of Utrecht University, and Statistics Netherlands. It is taking place from 21-25 August 2000 at Utrecht University. Previous COMPSTATs (from 1974-1998) were in Vienna, Berlin, Leiden, Edinburgh, Toulouse, Prague, Rome, Copenhagen, Dubrovnik, Neuchatel, Vienna, Barcelona and Bristol. The conference is the main European forum for developments at the interface between statistics and computing. This was encapsulated as follows on the COMPST A T 2000 homepage http://neon. vb.cbs.nlIrsml compstat. Statistical computing provides the link between statistical theory and applied statistics. As at previous COMPSTATs, the scientific programme will range over all aspects of this link, from the development and implementation of new statistical ideas through to user experiences and software evaluation. The programme should appeal to anyone working in statistics and using computers, whether in universities, industrial companies, research institutes or as software developers. At COMPST AT 2000 there is a special interest in the interplay with official statistics. This is evident from papers in the area of computerised data collection, survey methodology, treatment of missing data, and the like. |
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Contents
I | 3 |
II | 15 |
III | 29 |
IV | 41 |
V | 53 |
VI | 65 |
VII | 77 |
VIII | 87 |
XL | 319 |
XLI | 325 |
XLII | 331 |
XLIII | 337 |
XLIV | 343 |
XLV | 349 |
XLVI | 355 |
XLVII | 361 |
IX | 97 |
X | 109 |
XI | 121 |
XII | 131 |
XIII | 139 |
XIV | 151 |
XV | 161 |
XVI | 175 |
XVII | 181 |
XVIII | 187 |
XIX | 193 |
XX | 199 |
XXI | 205 |
XXII | 211 |
XXIII | 217 |
XXIV | 223 |
XXV | 229 |
XXVI | 235 |
XXVII | 241 |
XXVIII | 247 |
XXIX | 253 |
XXX | 259 |
XXXI | 265 |
XXXII | 271 |
XXXIII | 277 |
XXXIV | 283 |
XXXV | 289 |
XXXVI | 295 |
XXXVII | 301 |
XXXVIII | 307 |
XXXIX | 313 |
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Common terms and phrases
aggregate aggregate series algorithm analysis applets application approach approximation asymptotic autoregressive Bayes factor Bayesian bias cells classification cluster coefficients component COMPSTAT computational conditional independence considered correlation corresponding covariance matrix data mining data set database defined denote density dimension distribution EM algorithm error estimation example forecasting function Gibbs sampler given implemented impulse responses interaction intervals item response iterative Jasp Journal Keywords linear models Markov chain maximum likelihood MCMC mean measure methods missing data missing values mixture multiple imputation multivariate nonresponse normal observations obtained optimal outliers paper parameters performance plot points population posterior posterior probability prediction predictor probability problem procedure proposed random regression residuals robust Rousseeuw Rubin rules S-Plus score seasonal adjustment Section selection simulation spatial spline spreadsheet standard Statistics Netherlands step stochastic structure survey Table techniques tree Van Driessen variables variance vector weights