Bandit algorithms are increasingly used in real-world sequential decision-making problems.
Associated with this is an increased desire to be able to use the resulting datasets to answer …

There is a great desire to use adaptive sampling methods, such as reinforcement learning
(RL) and bandit algorithms, for the real-time personalization of interventions in digital …

Bandit algorithms are increasingly used in real world sequential decision making problems,
from online advertising to mobile health. As a result, there are more datasets collected using …

We consider model-based sufficient dimension reduction for generalized linear models and
prove the consistency and asymptotic normality of the prediction estimator studied …

This article aims to seek a selection and estimation procedure for a class of tensor
regression problems with multivariate covariates and matrix responses, which can provide …

Taylor's power law (TPL) from empirical ecological theory has had many explanations
proposed for its widespread observation in data. We show that the class of reduced-rank …

Regression models the conditional distribution of a random variable (s)(response (s)) given
a set of predictors. When the predictors are high-dimensional, the regression task becomes …

Online reinforcement learning (RL) algorithms are a very promising tool for personalizing
decision-making for digital interventions, eg, in mobile health, online education, and public …

Complements to Vector Generalized Linear and Additive Models Page 1 Thomas W. Yee
Complements to Vector Generalized Linear and Additive Models September 28, 2022 Springer …

In this paper, we obtain the exponential-type inequalities for maximal partial sums of
negatively superadditive dependent (NSD) random variables, which extends the …