activity
20092021
most citedSmoothing splines estimators for functional linear regression

225 citations · 377 across the 4 of their papers we have counts for

collaborators

6 papers

econ.EM2021

A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters

Oualid Bada, Alois Kneip, Dominik Liebl +3

While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this p…

econ.EM2021

Semiparametric inference for partially linear regressions with Box-Cox transformation

Daniel Becker, Alois Kneip, Valentin Patilea

In this paper, a semiparametric partially linear model in the spirit of Robinson (1988) with Box- Cox transformed dependent variable is studied. Transformation regression models ar…

math.ST2019

Super-Consistent Estimation of Points of Impact in Nonparametric Regression with Functional Predictors

Dominik Poß, Dominik Liebl, Alois Kneip +3

Predicting scalar outcomes using functional predictors is a classic problem in functional data analysis. In many applications, however, only specific locations or time-points of th…

stat.ME2018

Cross-Component Registration for Multivariate Functional Data, With Application to Growth Curves

Cody Carroll, Hans-Georg Müller, Alois Kneip

Multivariate functional data are becoming ubiquitous with advances in modern technology and are substantially more complex than univariate functional data. We propose and study a n…

math.ST2009225 cited

Smoothing splines estimators for functional linear regression

Christophe Crambes, Alois Kneip, Pascal Sarda

The paper considers functional linear regression, where scalar responses are modeled in dependence of random functions . We propose a smoothing splines e…

math.ST2009152 cited

Common functional principal components

Michal Benko, Wolfgang Härdle, Alois Kneip

Functional principal component analysis (FPCA) based on the Karhunen--Loève decomposition has been successfully applied in many applications, mainly for one sample problems. In thi…