Interpolation and forecasting in Poisson's processes.
Interpolation of random hyperplanes.
Interpretation and optimization of the -means algorithm
The paper gives a new interpretation and a possible optimization of the well-known -means algorithm for searching for a locally optimal partition of the set which consists of disjoint nonempty subsets , . For this purpose, a new divided -means algorithm was constructed as a limit case of the known smoothed -means algorithm. It is shown that the algorithm constructed in this way coincides with the -means algorithm if during the iterative procedure no data points appear in the Voronoi diagram....
Interprétation non-linéaire d'un coefficient d'association entre modalités d'une juxtaposition de tables de contingence
Interpretation of pattern classification results, obtained from a test set
The present paper presents and discusses a methodology for interpreting the results, obtained from the application of a pattern classifier to an independent test set. It addresses the problem of testing the random classification null hypothesis in the multiclass case, by introducing an exact probability technique. The discussion of this technique includes the presentation of an interval estimation technique for the probability of correct classification, which is slightly more accurate than the ones...
Interval estimation for the Poisson distribution parameter with a single observation.
Interval estimation in two way nested unbalanced random model.
The present paper deals with interval estimation of variance components in two way nested unbalanced random model. Employing a suitable transformation a statistic has been developed and its distribution is well approximated by chi-square. The confidence intervals for variance components and the simultaneous confidence interval for their ratios have been derived.
Interval linear regression analysis based on Minkowski difference – a bridge between traditional and interval linear regression models
In this paper, we extend the traditional linear regression methods to the (numerical input)-(interval output) data case assuming both the observation/measurement error and the indeterminacy of the input-output relationship. We propose three different models based on three different assumptions of interval output data. In each model, the errors are defined as intervals by solving the interval equation representing the relationship among the interval output, the interval function and the interval...
Intervalles de confiance pour les estimations des courbes de survie à partir du modèle de Cox
Intractability results in discrete-event simulation
Intrinsic dimensionality and small sample properties of classifiers
Small learning-set properties of the Euclidean distance, the Parzen window, the minimum empirical error and the nonlinear single layer perceptron classifiers depend on an “intrinsic dimensionality” of the data, however the Fisher linear discriminant function is sensitive to all dimensions. There is no unique definition of the “intrinsic dimensionality”. The dimensionality of the subspace where the data points are situated is not a sufficient definition of the “intrinsic dimensionality”. An exact...
Intrinsic linearization of nonlinear regression by principal components method.
Intrinsic priors for hypothesis testing in normal regression models.
Testing that some regression coefficients are equal to zero is an important problem in many applications. Homoscedasticity is not necessarily a realistic condition in this setting and, as a consequence, no frequentist test there exist. Approximate tests have been proposed. In this paper a Bayesian analysis of this problem is carried out, from a default Bayesian model choice perspective. Explicit expressions for intrinsic priors are provided, and it is shown that the corresponding Bayes factor is...
Introduction à la statistique mathématique. I. Modèle d'expérimentation statistique
Introduction à la statistique mathématique. II. Exhaustivité
Introduction à la statistique mathématique. III. Décision statistique
Introduction à la statistique mathématique. IV. Propriétés asymptotiques du modèle statistique
Introduction à la statistique mathématique. V. Tests d'hypothèses
Introduction à l'analyse des correspondances d'après l'analyse du commerce mondial des phosphates
Introduction à l'approche symbolique en analyse des données