Nonparametric regression models offer a way to understand and quantify relationships between variables without having identify an appropriate family of possible functions. Although many estimation methods for these have been proposed in the literature, most them can be highly sensitive presence small proportion atypical observations training set. A review outlier robust nonparametric is provide...

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...

We develop a new method for variable selection in nonparametric functional concurrent regression. The commonly used linear model (FLCM) is far too restrictive assuming linearity of the covariate effects, which not necessarily true many real-world applications. (NPFCM), on other hand, much more flexible and can capture complex dynamic relationships present between response covariates. extend cla...