We present a nonparametric regression method for denoising 3-D image sequences
acquired via fluorescence microscopy. The proposed method exploits the redundancy of the …

In this data‐rich era, it is essential to develop advanced techniques to analyze and
understand large amounts of data and extract the underlying information in a flexible way …

Given a finite collection of estimators or classifiers, we study the problem of model selection
type aggregation, that is, we construct a new estimator or classifier, called aggregate, which …

Measuring dependence in multivariate time series is tantamount to modeling its dynamic
structure in space and time. In risk management, the nonnormal behavior of most financial …

The following study investigates the measurement equivalence of an online and paper-and-
pencil (PAP) survey of human values. For this purpose, a total of 250 respondents …

In a regression setup with deterministic design, we study the pure aggregation problem and
introduce a natural extension from the Gaussian distribution to distributions in the …

We propose an adaptive functional autoregressive (AFAR) forecast model to predict
electricity price curves. With time-varying operators, the AFAR model can be safely used in …

With the recent availability of high-frequency financial data the long-range dependence of
volatility regained researchers' interest and has led to the consideration of long-memory …

This chapter reviews the recent developments of Value at Risk (VaR) estimation. In this
survey, the most available univariate and multivariate methods are presented. The …

In this work we undertake a thorough study of the non-asymptotic properties of the vanilla
generative adversarial networks (GANs). We prove a sharp oracle inequality for the Jensen …