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.

In practice, both testable and untestable assumptions are generally required to draw inference about the mean outcome measured at the final scheduled visit in a repeated measures study with drop-out. Scharfstein et al. (2014) proposed a sensitivity analysis methodology to determine the robustness of conclusions within a class of untestable assumptions. In their approach, the untestable and test...

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...

Bayesian synthetic likelihood (BSL) is an established method for performing approximate inference when the function intractable. In methods, approximated parametrically via model simulations, and then standard likelihood-based techniques are used to perform inference. The Gaussian estimator has become ubiquitous in BSL literature, primarily its simplicity ease of implementation. However, it oft...