Local linear–additive estimation for multiple nonparametric regressions

Multiple Imputations Using Sequential Semi and Nonparametric Regressions

Multiple imputation is a general purpose method for analyzing data with missing values. Under this approach the missing set of values is replaced by several plausible sets of missing values to yield completed data sets. Each completed data set is then analyzed separately and the results (estimates, standard errors, test statistics etc) are combined to form a single inference. It is fairly well ...

A Comparative Study Of Parametric And Nonparametric Regressions

Gini's Multiple Regressions Gini's Multiple Regressions

Two regression methods can be interpreted as based on Gini's mean difference (GMD). One relies on a weighted average of slopes defined between adjacent observations and the other is based on minimization of the GMD of the errors. The properties of the former approach are investigated in a multiple regression framework. These estimators have representations that resemble the OLS estimators, and ...

Estimation of Soil Infiltration in Agricultural and Pasture Lands using Artificial Neural Networks and Multiple Regressions

Common methods to determine the soil infiltration need extensive time and are expensive. However, the existence of non-linear behaviors in soil infiltration makes it difficult to be modeled. With regards to the difficulties of direct measurement of soil infiltration, the use of indirect methods toestimate this parameter has received attention in recent years. Despite the existence of various th...

Near-Linear Time Local Polynomial Nonparametric Estimation

Local polynomial regression (Fan & Gijbels, 1996) is an important class of methods for nonparametric density estimation and regression problems. However, straightforward implementation of local polynomial regression has quadratic time complexity which hinders its applicability in large-scale data analysis. In this paper, we significantly accelerate the computation of local polynomial estimates ...