This book contains a up-to-date overview of nonlinear time series models and their
application to modelling economic relationships. It considers nonlinear models in stationary …

The paper introduces a n-consistent estimator of the probability density function of the
response variable in a nonparametric regression model. The proposed estimator is shown to …

This paper considers linear and nonlinear regression with a response variable that is
allowed to be “missing at random”. The only structural assumptions on the distribution of the …

The authors propose an estimator for the density of the response variable in the parametric
mean regression model where the error density is left unspecified. With the application of …

This paper details implementation of the recently proposed root-n kernel density estimator of
(Escanciano, JC, and DT Jacho-Chávez. 2012.“n-uniformly consistent density estimation in …

This article proposes a simulation-based density estimation technique for time series that
exploits information found in covariate data. The method can be paired with a large range of …

We consider a nonparametric regression model Y=r(X)+ε with a random covariate X that is
independent of the error ε. Then the density of the response Y is a convolution of the …

Densities of functions of two or more independent random variables can be estimated by
local U-statistics. Frees gave conditions under which they converge pointwise at the …

We consider finite-order moving average and nonlinear autoregressive processes with no
parametric assumption on the error distribution, and present a kernel density estimator of a …

Consider a nonparametric regression model Y= r (X)+ ε with a random covariate X that is
independent of the error ε. Then the density of the response Y is a convolution of the …