Célestin C. Kokonendji

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This work deals with a semiparametric estimation of a count regression function m that can be represented as a product of an unknown discrete parametric function r and an unknown discrete “smooth” function ω. We propose an estimation procedure in two steps: first, we construct an approximation r̂ of r, then we use a discrete associated kernel method to(More)
It has been shown that the uniformly minimum variance unbiased (UMVU) estimator of the generalized variance always exists for any natural exponential family. In practice, however, this estimator is often di¢ cult to obtain. This paper explicitly identi…es the results in complete bivariate and symmetric multivariate gamma models, which are diagonal quadratic(More)
Discrete kernel estimation of a probability mass function (p.m.f.) often mentioned in the literature has been far less investigated in comparison with continuous kernel estimation of a probability density function (p.d.f.). In this paper, we are concerned with a general methodology of discrete kernels for smoothing a p.m.f. f . We give a basic of(More)
Transorthogonality for a sequence of polynomials on Rd has been recently introduced in order to characterize the reference probability measures, which are multivariate distributions of the natural exponential families (NEFs) having a simple cubic variance function. The present paper pursues this characterization of three various manners through exponential(More)
In this paper we introduce the Hinde-Demétrio (HD) regression models for analyzing overdispersed count data and, mainly, investigate the e¤ect of dispersion parameter. The HD distributions are discrete additive exponential dispersion models (depending on canonical and dispersion parameters) with a third real index parameter p and have been characterized by(More)
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