Weighted Poisson and Semiparametric Kernel Models Applied to Parasite Growth

This work deals with some parametric and semiparametric modeling approaches for count data distributions related to development of spiraling whitefly which is an insect pest collected in Brazzaville, Republic of Congo. In this study, the count data distributions are assumed to be modified Poisson probability mass functions. For the discrete semiparametric associated kernel estimator investigated, its almost sure consistency and asymptotic normality are shown under some asumptions. Some weighted Poisson models (WPD) are applied in comparison with the semiparametric approach for finite samples characterizing the growth of spiraling whitefly. Finally, the discrete semiparametric estimation is simple and effective for estimating any count distribution while WPD are practically more meaningful.