This paper proposes a nonparametric test of the non-convexity of a smooth regression function based on least squares or hybrid splines. By a simple formulation of the convexity hypothesis in the class of all polynomial cubic splines, we build a test which has an asymptotic size equal to the...

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT β + g(T) when the T's are measured with additive error. We derive an estimator of β by modification local-likelihood method. The resulting estimator of β is shown to be asymptotically...

Couples from Western countries tend to delay their pregnancies, which may affect their ability to obtain a live birth. We assessed the association between male age and the risk of spontaneous abortion taking into account woman's age. We performed telephone interviews on a ross-sectional random...

In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show...

This paper establishes the almost. sure consistency of least. squares regression series estimators, in the L2-norm and the sup-norm, under very large assumptions on the underlying model. Three examples are considered in order to illustrate the general results: trigonometric series, Legendre...

The importance of homogeneity as a restriction on functional forms has been well recognized in economic theory. Imposing additive separability is also quite popular since many economics models become easier to interpret and estimate when the explanatory variables are additively separable. In...

Consider the regression y = f(x) + e ' where E (e | x) = 0 and the exact functional form of f is unknown, although we do know that it is homogeneous of known degree r. Using a local linear approach we examine two ways of nonparametrically estimating f: (i) a "direct" or "numeraire" approach, and...

We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is asymptotically equivalent to the infeasible local maximum...

Estimating equations have found wide popularity recently in parametric problems, yielding consistent estimators with asymptotically valid inferences obtained via the sandwich formula. Motivated by a problem in nutritional epidemiology, we use estimating equations to derive nonparametric...