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20000224BuchA Bootstrap Test for Single Index Models Härdle, Wolfgang Karl; Mammen, Enno; Proença, Isabel

20050306BuchA Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics Fengler, Matthias R.; Härdle, Wolfgang Karl; Mammen, EnnoA primary goal in modelling the implied volatility surface (IVS) for pricing and hedging aims at reducing complexity. For this purpose one fits the IVS each day and applies a principal component analysis using a functional ...

20120809BuchAdditive Models Extensions and Related ModelsMammen, Enno; Park, Byeong U.; Schienle, MelanieWe give an overview over smooth backfitting type estimators in additive models. Moreover we illustrate their wide applicability in models closely related to additive models such as nonparametric regression with dependent ...

19970730BuchBootstrap of kernel smoothing in nonlinear time series Franke, Jürgen; Kreiss, JensPeter; Mammen, EnnoKernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time series. In this paper it is shown that the bootstrap can be used for estimating the distribution of kernel smoothers. This ...

19981216BuchEstimating Yield Curves by Kernel Smoothing Methods Linton, Oliver; Mammen, Enno; Nielsen, Jens P.; Tanggaard, C.

19990114BuchEstimation in an additive model when the components are linked parametrically Carroll, Raymond J.; Härdle, Wolfgang Karl; Mammen, EnnoMotivated by a nonparametric GARCH model we consider nonparametric additive regression and autoregression models in the special case that the additive components are linked parametrically. We show that the parameter can ...

20120626BuchGenerated Covariates in Nonparametric Estimation A Short ReviewMammen, Enno; Rothe, Christoph; Schienle, MelanieIn many applications, covariates are not observed but have to be estimated from data. We outline some regressiontype models where such a situation occurs and discuss estimation of the regression function in this context.We ...

20031208BuchImplied Volatility String Dynamics Fengler, Matthias R.; Härdle, Wolfgang Karl; Mammen, EnnoA primary goal in modelling the dynamics of implied volatility surfaces (IVS) aims at reducing complexity. For this purpose one fits the IVS each day and applies a principal component analysis using a functional norm. This ...

20031208Berichte und sonstige TexteImplied Volatility String Dynamics Fengler, Matthias R.; Härdle, Wolfgang Karl; Mammen, EnnoA primary goal in modelling the dynamics of implied volatility surfaces (IVS) aims at reducing complexity. For this purpose one fits the IVS each day and applies a principal component analysis using a functional norm. This ...

20151112BuchNonparametric Estimation in case of Endogenous Selection Breunig, Christoph; Mammen, Enno; Simoni, AnnaThis paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection ...

20051012BuchNonparametric Estimation of an Additive Model with a Link Function Horowitz, Joel L.; Mammen, EnnoThis paper describes an estimator of the additive components of a nonparametric additive model with a known link function. When the additive components are twice continuously differentiable, the estimator is asymptotically ...

20101206BuchNonparametric Regression with Nonparametrically Generated Covariates Mammen, Enno; Rothe, Christoph; Schienle, MelanieWe analyze the properties of non and semiparametric estimation procedures involving nonparametric regression with generated covariates. Such estimators appear in numerous econometric applications, including nonparametric ...

19970801BuchOn Estimation of Monotone and Concave Frontier Functions Gijbels, I.; Mammen, Enno; Park, Byeong U.; Simar, LéopoldWhen analyzing the productivity of firms, one may want to compare how the firms transform a set of inputs x (typically labor, energy or capital) into an output y (typically a quantity of goods produced). The economic ...

20070221BuchPenalized quasilikelihood estimation in partial linear models Mammen, Enno; Geer, Sara van de

19980702BuchProperties of the Nonparametric Autoregressive Bootstrap Franke, Jürgen; Kreiss, JensPeter; Mammen, Enno; Neumann, Michael H.We prove geometric ergodicity and absolute regularity of the nonparametric autoregressive bootstrap process. To this end, we revisit this problem for nonparametric autoregressive processes and give some quantitative ...

19981109BuchSemiparametric additive indices for binary response and generalized additive models Härdle, Wolfgang Karl; Huet, Sylvie; Mammen, Enno; Sperlich, StefanModels are studied where the response Y and covariates X, T are assumed to fulfill E(YX; 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 ...

20111017BuchSemiparametric Estimation with Generated Covariates Mammen, Enno; Rothe, Christoph; Schienle, MelanieIn this paper, we study a general class of semiparametric optimization estimators of a vectorvalued parameter. The criterion function depends on two types of in nitedimensional nuisance parameters: a conditional expectation ...

20140903BuchSemiparametric Estimationwith GeneratedCovariates Mammen, Enno; Rothe, Christoph; Schienle, MelanieWe study a general class of semiparametric estimators when the infinitedimensional nuisance parameters include a conditional expectation function that has been estimated nonparametri cally using generated covariates. ...

19980119BuchSmooth discrimination analysis Mammen, Enno; Tsybakov, Aleksandr B.Discriminant analysis for two data sets in IRd with probability densities f and g can be based on the estimation of the set G = {x : f(x) ≥ g(x)}. We consider applications where it is appropriate to assume that the region ...

20070426BuchTime Series Modelling with Semiparametric Factor Dynamics Borak, Szymon; Härdle, Wolfgang Karl; Mammen, Enno; Park, Byeong U.Highdimensional regression problems which reveal dynamic behavior are typically analyzed by time propagation of a few number of factors. The inference on the whole system is then based on the lowdimensional time series ...