On Model-Choice in Discrimination with Categorical Variables

An algorithm for model selection in discrimination with categorical variables is presented. It is based on four models applied hierarchically and linked with a build-up procedure of feature-selection. The choice of models and features is ensured by a consequent cross-validation. Results of an application in medical diagnostics are described.     

Randomized Blocks Analysis for the Exponential Distribution

A procedure for analyzing randomized blocks experiments for uncensored exponential random variables is presented. Its small sample behavior is studied in several simulations. Sample size requirements are given.     

Spectral resolution of cardio-circulatory variations in men measured by autorhythmometry over 2 years

An analogue of the periodogram method for unequally spaced data is presented with a view to resolving the frequency structure of the observations. The algorithm is explicitly based on the sequential least squares procedure. In particular, the key concept is that the with-in-plot spectral analysis can be augmented by the between-plot information to make inferences about common characteristics. It is also shown how the between-plot random variations can be incorporated into the multiple harmonic regression model. A detailed spectral analysis investigates the periodic fluctuations in four cardio-circulatory variables, measured by autorhythmometric observation by eight men at rest and extending over a time span of 2 years. The spectral curves show the existence of circadian and circaseptan rhythmicities. The amplitude modulation of the dian rhythm by circaseptan variation is assimilated with the rhythmicity of work during the week. The blood-pressure variables situate their maximum annual peak in the winter period. These quasi-periodic fluctuations appear to be related to the amount of physical activity performed in time by the subjects.     

Analysis of Rates Using a Generalized Poisson Regression Model

In this paper a generalization of the Poisson regression model indexed by a shape parameter is proposed for the analysis of life table and follow-up data with concomitant variables. The model is suitable for analysis of extra-Poisson variation data. The model is used to fit the survival data given in Holford (1980). The model parameters, the hazard and survival functions are estimated by the method of maximum likelihood. The results obtained from this study seem to be comparable to those obtained by Chen (1988). Approximate tests of the dispersion and goodness-of-fit of the data to the model are also discussed.