There are three common types of regression models: parametric, semiparametric and nonparametric regression. The model should be used to fit the real data depends on how much information is available about form relationship between response variable explanatory variables, random error distribution that assumed. Researchers need familiar with each modeling approach requirements. In this paper, di...

In this paper, we establish a proof for  a  necessary condition for  multiple objective fractional programming. In order to derive the set of necessary conditions, we employ an equivalent parametric problem. Also, we  present the related semi parametric model.

We review gene mapping, or inference for quantitative trait loci, in the context of recent research in semi-parametric and non-parametric inference for mixture models. Gene mapping studies the relationship between a phenotypic trait and inherited genotype. Semi-parametric gene mapping using the exponential tilt covers most standard exponential families and improves estimation of genetic effects...

Eliciting all the components of prospect theory – curvature utility function, weighting function and loss aversion remains an open empirical challenge. We develop a semi-parametric method that keeps tractability parametric methods while providing more precise estimates. Applying new to datasets Tversky Kahneman (1992) Bruhin et al. (2010), we reject convexity in domain show probability does not...

An accurate estimate of the uncertainty associated with a parameter estimate is important if we want to avoid misleading inference. The bootstrap technique (Efron, 1979; Efron and Tisbshirani, 1993) is a very general way of measuring the accuracy of estimators, and was originally developed for parameter estimation given independent identically distributed (i.i.d.) data. However, random effects ...