Table Of Contentc
PITAT
I'I~
Proceedings in Computational Statistics
6th Symposium held at Prague 1984
Edited by
T. Havranek
v ,
Z. Sidak
M. Novak
nr
Springer-Verlag Berlin Heidelberg GmbH 1984
CIP-Kurztitelaufnahme der Deutschen Bibliothek
COMPSTAT: 06, 1984, Prag
COMPSTAT: proceedings in computational statistics;
6. symposium.
held at Prague 1984/ed by T. Havranek; Z. srdâk;
M. Novâk.
ISBN 978-3-7051-0007-7 ISBN 978-3-642-51883-6 (eBook)
DOI 10.1007/978-3-642-51883-6
This book, or parts thereof, may not be translated or reproduced in any form
without written permission of the publisher
© Springer-Verlag Berlin Heidelberg 1984
Originally published by Physica-Verlag Ges.m.b. H., Vienna/ Austria in 1984
for IASC (International Association for Statistical Computing) 1984
ISBN 978-3-7051-0007-7
3
Preface
Looking into the recent history of computational statistics,
major changes in this field as well as in attitudes of workers
to it may be noticed over the past decade. The preface of the
COMPSTAT 1974 conference volume started by the following words:
"Perhaps neither mathematicians specialized in probability theory
or statistics nor experts in electronic data processing will look
on computational statistics as a serious scientific subject." We
feel now, ten years later, that this field has changed to be consider
ed a serious scientific subject. This leads, on the one hand, to
an increasing number of scientific papers devoted to it, but, on
the other hand, it is much more complicated task to recognize sub
stantial scientific contributions. In the present volume we have
collected papers that we believe to give some new insight into
the problems of computational statistics.
It was a difficult task to select 65 contributed papers,
presented here, from 301 submitted abstracts. The main criterion,
as in preceding COMPSTAT conferences, was that the papers should
show some novelty of their contents both in statistics and in
computing. The selection was done by the Scientific Programme
Committee consisting of J. Andel, A. Bartkowiak, J. Chambers, A.
Czurgay, A. de Falguerolles, T. Havr~nek (Secretary), N. Lauro,
J. de Leeuw, G. Melard, S. Mustonen, G.J.S. Ross, Z. ~idak (Chair
man), o.v. Staroverov, R. Struck, and N. Victor.
The meeting was arranged by the Central Computing Center of
the Czechoslovak Academy of Sciences in cooperation with the
Department of Biomathematics of the Institute of Physiology of the
Czecjoslovak Academy of Sciences, Mathematical Institute of the
Czechoslovak Academy of Sciences, and the Faculty of Mathematics
and Physics of the Charles University, Prague.
4
The conference was organized by the Local Organizing Committee
consisting of M.K. Chytil, Z. Fabian, T. Havr'nek (Secretary),
D. Hdsek, P. Jirkd, O. Kufudaki, B. Louvar, M. Nov'k (Chairman)
P. Pa~es, M. Pravdov', S. ft!zek, G. V!tkov', and Z. Zavorkov'.
We wish to express our gratitude to the members of both committees
and to other colleagues of ours from the above mentioned institut
ions whose efforts enabled that the conference could be held in
Prague.
Editors
5
Contents
Sint, P.: Roots of computational statistics (invited paper). . . . . . . . . . . . . . . . 9
Computational statistics in random processes
Ansley, CF., R. Kohn: New algorithmic developments for estimation problems
in time series (invited paper) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Melard, G.: On fast algorithms for several problems in time series models. . . . . . 41
Krzyfko, M., Smoczylzski: Parameter estimation and order determination of a
multivariate autoregressive process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Computational aspects of robustness
Michlzlek, Jilt, Vajda, L, Vf!ek: New topic in robust statistics with applica-
tions (invited paper). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Antoch, 1., Collomb, G., Hassani. S.: Robustness in parametric and non-para-
metric regression estimation: an investigation by computer simulations. . . . . 49
Glasbey. CA.: CEVOPE - a program for fmding conservative estimates of the
variances of regression parameter estimators when errors are serially correl-
ated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Gomes, ML: Robustness of Gumbel statistic for distribution functions in the
domain of attraction of a type I distribution oflargest values. . . . . . . . . . . . 61
White, A.P., Still, A. W. : Monte-Carlo analysis of variance . . . . . . . . . . . . . . . . 90
Muller, H.-G.: Boundary effects in nonparametric curve estimation models. . . . . 83
Ltdar. V.: Asymptotic distribution of the roots of a correlation matrix. . . . . . . 67
Computational graphics in statistical data analysis
Gordesch, 1. : Graphical methods for the manipulation and solution of systems
of linear equations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Puranen, 1.: DISTEP - dynamic interactive statistical teaching package. . . . . . . 105
Various statistical and data analytic methods
Zagorujko, N.: On discriminant analysis (invited paper) . . . . . . . . . . . . . . . . . 172
w.:
Van der Sluis, D.M, Schaafsma, POSCON - a decision-support system in
diagnosis and prognosis based on a statistical approach. . . . . . .. . . . . . . . . 160
Tracy. D.S., Tajuddin, IH.: Simulated powers under two different approxima-
tions to the exact distribution of MRPP statistics. . . . . . . . . . . . . . . . . . . . 166
6
Perez, A.: "Barycenter" of a set of probability measures and its application in
statistical decision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Payne, R. W., Dixon, T.J.: A study of selection criteria for constructing identi-
fication keys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
Lauter, J.: Algorithms of discriminant analysis using parameter restrictions for
diminishing the error rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Diday, E., Moreau, J. V.: Hierarchical inference. . . . . . . . . . . . . . . . . . . . . . . 119
Murtagh, F.: A review of fast techniques for nearest neighbour searching. . . . . . 143
Meulman, J., Heiser, w.J.: Constrained multidimensional scaling: more direc-
tions than dimensions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Korhonen, P.J. : An interactive multiple criteria approach to finding subjective
principal components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Aluja Banet, T., Lebart, L.: Local and partial principal component analysis
(pC A) and correspondence analysis (CA) 113
Numerical aspects and complexity of statistical algorithms
Krivanek, M, Moravek, J. : On NP-hardness in hierarchical clustering. . . . . . . .. 189
Jackel, K.-H.: Computational aspects of Monte-Carlo tests. . . . . . . . . . . . . . . 183
Linear Models
Kubacek, L.: Reliability of calculations in the linear estimation and some prob-
lems of the analysis of a multistage regression experiment (invited paper). . . . 214
Gilchrist, R., Scallan, A.: Parametric link functions in generalized linear models. 203
Kleffe, J. : Statistical analysis of mixed linear models with inhomogeneous vari-
ances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Bethlehem, J. G.: Linweight: a program for weithting sample survey data. . . . . . 197
Intelligent software in statistical data analysis
Gale, W.A., Pregibon, D.: Constructing an expert system for data analysis by
working examples (invited paper). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Pregibo.n, D., Gale, W.A.: REX: an expert system for regression analysis. . . . . . 242
Kowalski, A., Schutt, A.: Analysis of suppositions. . . . . . . . . . . . . . . . . . . . . 237
Pukkila, T., Puntanen, S., Stenman, 0.: On the possibilities to automate the
study of statistical dependence between two variables . . . . . . . . . . . . . . . . 249
Integration of statistical data base management and data analysis
Neumann, K.: Metainforrnation as a tool for integration. . . . . . . . . . . . . . . . . 273
Rauch, L.: Interface problems between software packages used in statistical in~
formation system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
7
Borzemski, L., Kosza/lea, L.: Statistical data analysis using a data base for experi-
ment design and systems identification. . . . . . . . . . . . . . . . . . . . . . . . . . 257
w.,
Kurzyfzski, M Lebiediewa, S.: Processing of statistical data for multistage
recognition system using data base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
Rafanelli, M, Ricci, F.L.: Statistical database: an interactive language for logical
schema definition by means of a model based on graphs. . . . . . . . . . . . . . . 279
lomier, G., Kezouit, 0., Ra/ambondrainy, H.: SICLA-PEPIN: A system integra-
ting data analysis and relational data base management system. . . . . . . . . . . 263
Software for parallel processing in statistical data analysis
La[aye de Micheaux, D.: Parallelization of algorithms in the practice of statisti-
cal data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
Ososkov, G.A., Vajterlic, M.: A fast parallel algorithm for the recognition of
ionized particle tracks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
Evaluation and enhancements of statistical software
Anderson, A.J.B.: The enhancement of Fortran for statistical analysis. . . . . . . . 311
Nedoma, P. : DeSign of experimental statistical packages. . . . . . . . . . . . . . . . . 347
De long, V.l.: A multilevel approach to improve the flexibility of statistical
software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
Molenaar, L W., Broersma, H.: Interpretation of statistical software output: some
behavioral studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
Dekker, A.L. : Comparing statistical software packages on tabulation efficiency. . 317
Kuik, D.J.: QUESTOR, a conversational preprocessor to BMDP. . . . . . . . . . . . 329
MicMlek, laros/av, Nbncova, M, Popellnsky, Rebftkova, M.: The system for
multivariate statistical data processing. . . . . . . . . . . . . . . . . . . . . . . . . . . 335
Software in categorical data analysis
Whitakker, J.: Fitting all possible decomposable and graphical models to multi-
way contingency tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 I
Edwards, D. : A computer intensive approach to the analysis of sparse multi-
dimensional contigency tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
Murphy, B.P. :Similarities and differences between linear, logistic and log-linear
models for survival analysis; some practical observations. . . . . . . . . . . . . . . 378
Lane, P. W.: Methods for summarizing the analysis of categorical data. . . . . . . . 366
Rudas, T.: Stepwise discriminant analysis procedure for categorical variable. . . . 389
8
Morineau, A. : Computational and statistical methods for exploratory analysis
of textual data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372
Scherb, H., Welzl, G.: A recursive algorithm for the determination of the uni-
formly most powerful unbiased test in fourfold tables . . . . . . . . . . . . . . . . 395
Pokorny, D. : Optimal collapsing of two-way contingency tables including struc-
tural zeroes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
Hajek, P.: The new version of the GUHA procedure ASSOC (generating hypo-
theses on associations) - mathematical foundations. . . . . . . . . . . . . . . . . . 360
Optimization techniques in statistics
Narula, S.C, Wellington, 1.F.: On special purpose algorithms for the MSAE and
the MMAE regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440
Prekopa, A. : Programming under probabilistic constraints and its applications in
statistics (invited paper) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
Meester, I : Maximum likelihood estimation for mixtures of distributions . . . . . 427
Lejeune, M.: Optimization in nonparametric estimation. . . . . . . . . . . . . . . . . 421
Ross, G.J.S.: Parallel model analysis: fitting non-linear models to several sets of
data .......... '. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458
Messean, A., Nicole, 0., Vila, J.P.: HAUS 82: a new tool for nonlinear fitting
and the study of non-linear models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434
Kutas, T.: A new method for solving nonlinear least squares problem . . . . . . . . 415
Kaishev, V.K.: A computer program package for solving spline-regression prob-
lems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409
Software for data preprocessing (checking, handling of missing values reduction)
Chytil, M.K. : Is data preprocessing a computational process only? . . . . . . . . . . 467
Liski, E.: Missing data problems in growth curves . . . . . . . . . . . . . . . . . . . . . 479
Dickson, J.M : Data capture and validation using portable terminals. . . . . . . . . 473
Error free and fonnal computations in statistics
Collombier, D.: Error-free computation in design of experiments (invited paper). 487
Vuchkov, LN, Damgaliev, D.L., Donev, A.N: Comparison of some algorithms
for discrete D-optimal design generation . . . . . . . . . . . . . . . . . . . . . . . . . 503
Tilt, E.-M: Formal computation of regression parameters. . . . . . . . . . . . . . . . 497
Computational problems in spatial data analysis
Ko'§melj, M : Means for analysis of space-time relationship 509
9
Roots of Computational Statistics
P. Sint, Wien
This paper describes some of the nodes in history where the
traditions of computing with machinery and the develooment of
statistical methods met. It will give also sOlie ideas ~bout the
driving forces which brought these developments about and why there
eKist fundamental connections between them.
The preparation of this paper was p~rtly sponsored by IBM Austria
***
A base for a report of this kind should be a collection of
monographs on the topics concerned and an overwiew of the whole
development which I can't pretend to possess. However as there has
not been too much work gOing on in this direction it may well be
worthwile to point out some factors in the development. The paper as
it stands scratches, however, scarcley the surface, especially
concerning the analysis of the facts. As historical work is
cumulative in the sense that the information on relevant influences,
e.g. on the persons acting, is scattered in varying places, and may
be found sometimes only after long study of the specific topic under
consideration and often only accidentally, it is likely that most
readers will be able to point out factors overlooked or given a
wrong emphasis. This paper should stimulate this kind of interest in
historical developments, especially as we are still living in a
period where many of those who have contributed to a quantum leap in
the development of statistical computation are still alive, and
where many of you can contribute to the preservation of historical
records.
Already most of the developers of mechanical calculating machines
are, sometimes in roundabout ways, connected to statistics:
Least of them Wilhelm Schickard (1592-1635) who built the first
mechanical calculator which could already perform multiplications
and divisions. following an algorithmiC idea of Kepler in 1623.
The builder of the neKt calculating machine was Blaise Pascal
(1623-1662) who constructed a machine for addition and subtraction
for accounting purposes in 1642-44, and studied well known early
problems in the theory of games of chance, which is now considered
to be the beginning of the modern theory of probability. Thus Pascal
combined activities related to two early and important roots of
computational statistics.
With Gottfried Wilhelm Leibnitz (1646-1716) the mathematical
cOllputational aspect, the original "old" statistics and the politi
cal arithmetic - the forerunner of the "modern" statistics - meet
for the first and probably only time:
The word statistics is derived from the italianllatin ragione di
statoldisciplina status meant to describe the knowled~e of states
manship andlor st~te affairs. The "statist" in Shakespears Hamlet
COMPSTAT 1984 e Physica-Verlag, Vienna for IASC (International Association for Statistical Computing), 1984
