This paper gives a review of concentration inequalities which are widely employed in non-
asymptotical analyses of mathematical statistics in a wide range of settings, from distribution …

Constant-specified and exponential concentration inequalities play an essential role in the
finite-sample theory of machine learning and high-dimensional statistics area. We obtain …

There has been a surge of interest in developing robust estimators for models with heavy-
tailed and bounded variance data in statistics and machine learning, while few works …

In this paper, we consider the unified optimal subsampling estimation and inference on the
low-dimensional parameter of main interest in the presence of the nuisance parameter for …

To fast approximate the maximum likelihood estimator with massive data, Wang et al.(JASA,
2017) proposed an optimal subsampling method under the A-optimality criterion (OSMAC) …

For high-dimensional models with a focus on classification performance, the ℓ 1-penalized
logistic regression is becoming important and popular. However, the Lasso estimates could …

We focus on the COM-type negative binomial distribution with three parameters, which
belongs to COM-type (a, b, 0) class distributions and family of equilibrium distributions of …

The development of modern science and technology has facilitated the collection of a large
amount of matrix data in fields such as biomedicine. Matrix data modeling has been …

The optimal subsampling is an statistical methodology for generalized linear models (GLMs)
to make inference quickly about parameter estimation in massive data regression. Existing …

Count data is prevalent in various fields like ecology, medical research, and genomics. In
high-dimensional settings, where the number of features exceeds the sample size, feature …