We construct a semiparametric estimator in case-control studies where the gene and the environment are assumed to be independent. A discrete or continuous parametric distribution of the genes is assumed in the model. A discrete distribution of the genes can be used to model the mutation or presence of certain group of genes. A continuous distribution allows the distribution of the gene effects ...

The Generalized Poisson Distribution (GPD) adds an extra parameter to the usual Poisson distribution. This parameter induces a loss of homogeneity in the stochastic processes modeled by the distribution. Thus, the generalized distribution becomes an useful model for counting processes where the occurrence of events is not homogeneous. This model creates the need for an inferential procedure, to...

The non-parametric maximum likelihood estimator and semi-parametric regression models are fundamental estimators for interval censored data, along with standard fullyparametric regression models. The R-package icenReg is introduced which contains fast, reliable algorithms for fitting these models. In addition, the package contains functions for imputation of the censored response variables and ...

Global weak continuity of M-functionals in a neighbourhood of the parametric distribution is established. This has implications for robustness of M-estimators vis a vis definitions put forward by Hampel. For instance the Tukey bisquare location estimator is robust on neighbourhoods of the parametric model, but the median is not. 2000 Mathematics subject classification: primary 62G35; secondary ...

We combine the ensembles from two different experiments to study the climate sensitivity of the HadSM3 climate model subject to parametric uncertainty. We use a statistical framework based around linked emulators, where expert judgements are required to quantify the relationship between the two ensembles. Detailed diagnostics are presented. As an application, we perform a sensitivity analysis f...

In this paper, we first propose a finite mixture of exponential distribution model with parametric functions. By using the local constant fitting, the local maximum likelihood estimations of parametric functions are obtained, and their asymptotic biases and asymptotic variances are discussed. Moreover, we propose the EM algorithm to carry out the estimation procedure and give the ascent propert...

The Bayesian approach to analyzing semi-parametric models are gaining popularity in practice. For the Cox proportional hazard model, it has been shown recently that the posterior is consistent and leads to asymptotically accurate confidence intervals under a Lévy process prior on the cumulative hazard rate. The explicit expression of the posterior distribution together with independent incremen...

abstractprecipitation could be regarded as a complex and a very vital element in the earth atmosphere and its spatial-temporal changes can control iran's climate. in the current research, in order to model the spatial-temporal changes of annual precipitation, the data taking from 50 synoptic stations, have been analyzed during the years 1966-2005, with a suitable distribution. the most dat...

in this paper, the formation of club convergence among real per capita gdp of iran’s provinces is tests through use of non-parametric quantile regression and distribution dynamics approach. the results show two convergence clubs are forming among provinces and most of them are converging toward poor club. also, calculation of convergence speed shows that there is a vast difference among economi...