225 citations · 474 across the 9 of their papers we have counts for
11 papers
Combining Concurrent and Historical Functional Linear Regression
Alois Kneip, Dominik Liebl, Sven Otto
We study a function-on-function linear regression model in which the response at time depends on both the past trajectory of a predictor and its concurrent value. The model com…
Nonparametric local polynomial regression for functional covariates
Moritz Jirak, Alois Kneip, Alexander Meister +1
We consider nonparametric regression with functional covariates, that is, they are elements of an infinite-dimensional Hilbert space. A locally polynomial estimator is constructed,…
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…
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…
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…
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…
On the Optimal Reconstruction of Partially Observed Functional Data
Alois Kneip, Dominik Liebl
We propose a new reconstruction operator that aims to recover the missing parts of a function given the observed parts. This new operator belongs to a new, very large class of func…
Functional linear regression with points of impact
Alois Kneip, Dominik Poß, Pascal Sarda
The paper considers functional linear regression, where scalar responses are modeled in dependence of i.i.d. random functions . We study a generali…
Factor models and variable selection in high-dimensional regression analysis
Alois Kneip, Pascal Sarda
The paper considers linear regression problems where the number of predictor variables is possibly larger than the sample size. The basic motivation of the study is to combine the…
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…
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…