Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y|X; T) = G{XT β + α + m1(T1) + … + md(Td)}. Here G is a known (link) function, β is an unknown parameter, and m1, …, md are unknown functions. In particular, we consider additive binary response models...

Eine große Herausforderung der multivariablen Analyse mit bilanziellen Kennzahlen besteht in der Identifikation derjenigen Kennzahlen, die zur besten Modellperformance führen und dabei möglichst leicht interpretierbar und intuitiv bleiben. Die Menge der in Frage kommenden Kennzahlen ist in...

Empirical studies on the earnings effects of tobacco use have found significant wage penalties attached to smoking. We produce evidence that suggests that these estimates are significantly upward biased. The bias arises from a general failure in the literature to control for the past smoking...

State price density (SPD) contains important information concerning market expectations. In existing literature, a constrained estimator of the SPD is found by nonlinear least squares in a suitable Sobolev space...

Let (X1, Y1), . . ., (Xn, Yn) be i.i.d. rvs and let l(x) be the unknownp-quantile regression curve of Y on X. A quantile-smootherln(x) is a localised, nonlinear estimator of l(x). The strong uniformconsistency rate is established under general conditions. In many applicationsit is necessary to...

We consider time series models in which the conditional mean of the response variable given thepast depends on latent covariates. We assume that the covariates can be estimated consistentlyand use an iterative nonparametric kernel smoothing procedure for estimating the conditional meanfunction....

This chapter gives an overview over smooth backfitting-type estimators in additive models. Moreover, it illustrates their wide applicability in models closely related to additive models such as nonparametric regression with dependent error variables where the errors can be transformed to white...