Classifications with relations. II. Asymptotic testing and estimation
Classifiers for doubly multivariate data
This paper proposes new classifiers under the assumption of multivariate normality for multivariate repeated measures data (doubly multivariate data) with Kronecker product covariance structures. These classifiers are especially useful when the number of observations is not large enough to estimate the covariance matrices, and thus the traditional classifiers fail. The quality of these new classifiers is examined on some real data. Computational schemes for maximum likelihood estimates of required...
Clear graphical representation of the location model in mixed variable discriminant analysis
Clustering objects described by juxtaposition of binary data tables.
Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set
In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors....
Clustering of vaguely defined objects
This paper is concerned with the clustering of objects whose properties cannot be described by exact data. These can only be described by fuzzy sets or by linguistic values of previously defined linguistic variables. To cluster these objects we use a generalization of classic clustering methods in which instead of similarity (dissimilarity) of objects, used fuzzy similarity (fuzzy dissimilarity) to define the clustering of fuzzy objects.
Clustering techniques for adaptive horizontal fragmentation in object oriented databases.
Coalescence floue fondée sur des -regroupements maximaux
Les concepts d'éléments R-ressemblants à un prototype X et de R-regroupement d'objets introduits dans cet article, sont basés sur la notion de relation de S-comparaison R définie au moyen d'un indice scalaire de similarité défini entre sous-ensembles flous. Cette relation tient compte du fait que la similarité et la non-dissimilarité des sous-ensembles flous ne sont pas en général des synonymes. Une technique de coalescence floue basée sur des R-regroupements maximaux est également introduite.
Codage optimal et sur-optimal sur un tableau invariant
Coefficient de corrélation et prise de décision
Coefficients de corrélation d'ordre supérieur
Colinéarité et instabilité numérique dans le modèle linéaire
Colinearité et Instabilité Numérique dans le Modèle Linéaire
In this paper we give the expression of the multiple correlation coefficient in a linear model according to the coefficients of correlation. This expression makes it possible to analyze from a numerical point of view the instability of estimates in the case of collinear explanatory variables in the linear model or in the autoregressive model. This numerical approach, that we show on two examples, thus supplements the usual approach of the quasi colinearity, founded on the statistical properties...
Combination of 3D epoch-wise and permanent geodetic networks observed by GNSS
The local, regional and global geodetic networks are recently almost exclusively observed by satellite radionavigation methods, such as the U.S. Global Positioning System (GPS), and the Russian navigation system GLONASS. The unprecedented accuracy of geodetic satellite positioning allows determination of the geocentric site coordinates at millimetre level. The paper points to complex adjustment model applied for combination of 3D coordinates observed in permanent and epoch-wise satellite networks....
Combined Unbiased Estimators Based on Q-Reduced Model.
Combining adaptive vector quantization and prototype selection techniques to improve nearest neighbour classifiers
Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbour (NN) classification rules both to improve its accuracy (editing) and to alleviate its computational burden (condensing). Methods based on selecting/discarding prototypes and methods based on adapting prototypes have been separately introduced to deal with this problem. Different approaches to this problem are considered in this paper and their main advantages and drawbacks are pointed out along with some...
Combining forecasts using the least trimmed squares
Employing recently derived asymptotic representation of the least trimmed squares estimator, the combinations of the forecasts with constraints are studied. Under assumption of unbiasedness of individual forecasts it is shown that the combination without intercept and with constraint imposed on the estimate of regression coefficients that they sum to one, is better than others. A numerical example is included to support theoretical conclusions.
Combining multivariate estimators of the mean vector
Meta-analysis is a standard statistical method used to combine the conclusions of individual studies that are related and the results of single study alone can not answered to deal with issues. The data are summarized by one or more outcome measure estimates along with their standard errors. The multivariate model and the variations between studies are not considered in most articles. Here we discuss multivariate effects models: a multivariate fixed effects model and a multivariate random effects...
Comment on C. R. Rao's MINQUE for replicated observations
Comment on "On some statistical paradoxes and non-conglomerability" by Bruce Hill.
Those who follow Harold Jeffreys in using improper priors together with likelihoods to determine posteriors have thought of the improper measures as probability measures of a deviant sort. This is a mistake. Probability measures are finite measures. Improper distributions generate σ-finite measures. (...)