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A nonparametric regression method for multiple longitudinal phenotypes using multivariate adaptive splines
In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data...
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Non-parametric estimation of the diffusion coefficient from noisy data
We consider a diffusion process ( X t ) t ≥ 0 , with drift b ( x ) and diffusion coefficient σ ( x ). At discrete times t k = k δ for k from 1 to M , we...
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Model selection and sharp asymptotic minimaxity
We obtain sharp minimax results for estimation of an n -dimensional normal mean under quadratic loss. The estimators are chosen by penalized least...
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Model averaging for semiparametric additive partial linear models
To improve the prediction accuracy of semiparametric additive partial linear models (APLM) and the coverage probability of confidence intervals of...
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How to Compare Different Loss Functions and Their Risks
Many learning problems are described by a risk functional which in turn is defined by a loss function, and a straightforward and widely known...
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Sequential Kernel Estimation of the Conditional Intensity of Nonstationary Point Processes
This paper develops adaptive nonparametric methods for analyzing seismic data. Kernel smoothing techniques are suitable for space–time point...
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On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data
The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general...
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On the efficiency of selection criteria in spline regression
This paper concerns the cubic smoothing spline approach to nonparametric regression. After first deriving sharp asymptotic formulas for the...
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A Comparison Between L1 Markov Random Field-based and Wavelet-based Estimators
We consider the problem of denoising a one-dimensional signal modeled as the realization of a Markov random field (MRF) by maximizing its posterior...
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Prediction of 0–1-Events for Short- and Long-memory Time Series
The problem of predicting 0–1-events is considered under general conditions, including stationary processes with short and long memory as well as...