In this paper, we aim to estimate the direction of an underlying signal from its nonlinear
observations following the semi-parametric single index model (SIM). Unlike for …

This paper presents a selective survey of recent developments in statistical inference and
multiple testing for high-dimensional regression models, including linear and logistic …

Predictors map individual instances in a population to the interval $[0, 1] $. For a collection
$\mathcal C $ of subsets of a population, a predictor is multi-calibrated with respect to …

This study proposes a novel method for estimation and hypothesis testing in high-
dimensional single-index models. We address a common scenario where the sample size …

In this paper, we study phase retrieval under model misspecification and generative priors.
In particular, we aim to estimate an $ n $-dimensional signal $\mathbf {x} $ from $ m $ iid …

In this paper, we aim to test the overall significance of regression coefficients in high-
dimensional single-index models. We first reformulate the hypothesis testing problem under …

Traditional statistical methods are faced with new challenges due to streaming data. The
major challenge is the rapidly growing volume and velocity of data, which makes storing …

Analysis of high-dimensional data has led to increased interest in both single index models
(SIMs) and best subset selection. SIMs provide an interpretable and flexible modeling …

We propose an estimator for the population mean θ 0= 𝔼 (Y) under the semi‐supervised
learning setting with the Missing at Random (MAR) assumption. This setting assumes that …

This paper is concerned with estimation and inference for ultrahigh dimensional partially
linear single-index models. The presence of high dimensional nuisance parameter and …