Effects of associated kernels in nonparametric multiple regressions

A Comparative Study Of Parametric And 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 ...

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 ...

Multicollinearity: A tale of two nonparametric regressions

The most popular form of arti cial neural network, feedforward networks with sigmoidal activation functions, and a new statistical technique, multivariate adaptive regression splines (MARS) can both be classi ed as nonlinear, nonparametric function estimation techniques, and both show great promise for tting general nonlinear multivariate functions. In comparing the two methods on a variety of ...

An equality test across nonparametric regressions

A procedure for testing equality across nonparametric regressions is proposed The proce dure allows for any dimension of the explanatory variables and for any number of subsamples We consider the case of random explanatory variables and allow the designs of the regressors and the number of observations to di er across subsamples The division into subsamples is de ned through a variable C which ...