Simulations Study Combined Estimator Fourier Series and Spline Truncated in Multivariable Nonparametric Regression

Robust nonparametric kernel regression estimator

In robust nonparametric kernel regression context,weprescribemethod to select trimming parameter and bandwidth. Through solving estimating equations, we control outlier effect through combining weighting and trimming. We show asymptotic consistency, establish bias, variance properties and derive asymptotics. © 2016 Elsevier B.V. All rights reserved.

Nonparametric Regression Using Kernel and Spline Methods

When applying nonparametric regression methods, the researcher is interested in estimating the relationship between one dependent variable, Y , and one or several covariates, X1, . . . , Xq. We discuss here the situation with one covariate, X (the case with multiple covariates is addressed in the references provided below). The relationship between X and Y can be expressed as the conditional ex...

Confidence Bands in Nonparametric Time Series Regression

We consider nonparametric estimation of mean regression and conditional variance (or volatility) functions in nonlinear stochastic regression models. Simultaneous confidence bands are constructed and the coverage probabilities are shown to be asymptotically correct. The imposed dependence structure allows applications in many linear and nonlinear auto-regressive processes. The results are appli...

Difference-based variance estimator for nonparametric regression in complex surveys

Strong convergence in nonparametric regression with truncated dependent data

AMS 2000 subject classifications: primary 62G07 secondary 62G20 a b s t r a c t In this paper we derive rates of uniform strong convergence for the kernel estimator of the regression function in a left-truncation model. It is assumed that the lifetime observations with multivariate covariates form a stationary α-mixing sequence. The estimation of the covariate's density is considered as well. U...