general test of significance for qualitative variables in a nonparametric regression. In the case of a fixed C, our procedure … all alternatives. We study its small sample behavior through Monte-Carlo simulations. -- Nonparametric regression …

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression … likelihood method is developed. It is shown that when missing responses are imputed using the semiparametric regression method …

Additive modelling has been widely used in nonparametric regression to circumvent the "curse of dimensionality", by … reducing the problem of estimating a multivariate regression function to the estimation of its univariate components …

Additive modelling is known to be useful for multivariate nonparametric regression as it reduces the complexity of … problem to the level of univariate regression. This usefulness could be compromised if the data set was contaminated by … procedure for the additive component of the regression function , less sensitive to possible outliers in the sample. Our …

Chaudhuri, Doksum and Samarov (1997) have recently stressed the usefulness of the quantile regression formulation for … survival analysis and for transformation models, more generally. In this paper, we explore the use of quantile regression in …-specific mortality in a population of roughly 1.2 million Mediterranean fruit flies (Ceratitis Capitata). The quantile regression …

Applying nonparametric variable selection criteria in nonlinear regression models generally requires a substantial … without problems. -- nonlinear regression ; Autoregression ; nonlinear time series ; nonparametric variable selection ; time …

bonds ; forward curve ; Hilbert space ; local linear ; nonparametric regression ; Yield curve …