On the rate of convergence of fully connected deep neural network regression estimates Page
1 The Annals of Statistics 2021, Vol. 49, No. 4, 2231–2249 https://doi.org/10.1214/20-AOS2034 …

On deep learning as a remedy for the curse of dimensionality in nonparametric regression
Page 1 The Annals of Statistics 2019, Vol. 47, No. 4, 2261–2285 https://doi.org/10.1214/18-AOS1747 …

Semiparametric regression is concerned with the flexible incorporation of non-linear
functional relationships in regression analyses. Any application area that benefits from …

With many real-world examples, this book shows how to apply the powerful methods of
smoothing splines in practice. It covers basic smoothing spline models as well as more …

In recent years, a large variety of textbooks dealing with time-to-event analysis has been
published. Most of these books focus on the statistical analysis of observations in continuous …

P-splines first appeared in the limelight twenty years ago. Since then they have become
popular in applications and in theoretical work. The combination of a rich B-spline basis and …

In partially linear single-index models, we obtain the semiparametrically efficient profile least-
squares estimators of regression coefficients. We also employ the smoothly clipped absolute …

OBJECTIVE Fasting blood glucose (FBG) could be an independent predictor for coronavirus
disease 2019 (COVID-19) morbidity and mortality. However, when included as a predictor in …

In this paper, we are concerned with how to select significant variables in semiparametric
modeling. Variable selection for semiparametric regression models consists of two …

One of the most difficult problems in applications of semi-parametric partially linear single-
index models (PLSIM) is the choice of pilot estimators and complexity parameters which may …