33 citations · 63 across the 12 of their papers we have counts for
12 papers
Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence
Ioannis Kalogridis
We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish existence and measurability of t…
Penalized Spline M-Estimators for Discretely Sampled Functional Data: Existence and Asymptotics
Ioannis Kalogridis
Location estimation is a central problem in functional data analysis. In this paper, we investigate penalized spline estimators of location for discretely sampled functional data u…
Robust Functional Regression with Discretely Sampled Predictors
Ioannis Kalogridis, Stanislav Nagy
The functional linear model is an important extension of the classical regression model allowing for scalar responses to be modeled as functions of stochastic processes. Yet, despi…
Robust and Adaptive Functional Logistic Regression
Ioannis Kalogridis
We introduce and study a family of robust estimators for the functional logistic regression model whose robustness automatically adapts to the data thereby leading to estimators wi…
Robust thin-plate splines for multivariate spatial smoothing
Ioannis Kalogridis
We propose a novel family of multivariate robust smoothers based on the thin-plate (Sobolev) penalty that is particularly suitable for the analysis of spatial data. The proposed fa…
Robust and efficient estimation of nonparametric generalized linear models
Ioannis Kalogridis, Gerda Claeskens, Stefan Van Aelst
Generalized linear models are flexible tools for the analysis of diverse datasets, but the classical formulation requires that the parametric component is correctly specified and t…
Robust penalized spline estimation with difference penalties
Ioannis Kalogridis, Stefan Van Aelst
Penalized spline estimation with discrete difference penalties (P-splines) is a popular estimation method for semiparametric models, but the classical least-squares estimator is hi…
Robust optimal estimation of location from discretely sampled functional data
Ioannis Kalogridis, Stefan Van Aelst
Estimating location is a central problem in functional data analysis, yet most current estimation procedures either unrealistically assume completely observed trajectories or lack…
Asymptotics for M-type smoothing splines with non-smooth objective functions
Ioannis Kalogridis
M-type smoothing splines are a broad class of spline estimators that include the popular least-squares smoothing spline but also spline estimators that are less susceptible to outl…
Robust penalized estimators for functional linear regression
Ioannis Kalogridis, Stefan Van Aelst
Functional data analysis is a fast evolving branch of statistics. Estimation procedures for the popular functional linear model either suffer from lack of robustness or are computa…
M-type penalized splines with auxiliary scale estimation
Ioannis Kalogridis, Stefan Van Aelst
Penalized spline smoothing is a popular and flexible method of obtaining estimates in nonparametric regression but the classical least-squares criterion is highly susceptible to mo…
Robust functional regression based on principal components
Ioannis Kalogridis, Stefan Van Aelst
Functional data analysis is a fast evolving branch of modern statistics and the functional linear model has become popular in recent years. However, most estimation methods for thi…