13 citations · 38 across the 26 of their papers we have counts for
27 papers
Least absolute deviation estimation for AR(1) processes with roots close to unity
Nannan Ma, Hailin Sang, Guangyu Yang
We establish the asymptotic theory of least absolute deviation estimators for AR(1) processes with autoregressive parameter satisfying for some fixed as $n\to\in…
On the integrated mean squared error of wavelet density estimation for linear processes
Aleksandr Beknazaryan, Hailin Sang, Peter Adamic
Let be a linear process with density function . We study wavelet density estimation of . Under some regular conditions on the characterist…
Nonparametric regression with modified ReLU networks
Aleksandr Beknazaryan, Hailin Sang
We consider regression estimation with modified ReLU neural networks in which network weight matrices are first modified by a function before being multiplied by input vectors.…
Limit theorems for linear random fields with innovations in the domain of attraction of a stable law
Magda Peligrad, Hailin Sang, Yimin Xiao +1
In this paper we study the convergence in distribution and the local limit theorem for the partial sums of linear random fields with i.i.d. innovations that have infinite second mo…
A Statistician Teaches Deep Learning
G. Jogesh Babu, David Banks, Hyunsoon Cho +3
Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the…
Variable bandwidth kernel regression estimation
Janet Nakarmi, Hailin Sang, Lin Ge
In this paper we propose a variable bandwidth kernel regression estimator for observations in to improve the classical Nadaraya-Watson estimator. The bias i…
Shannon entropy estimation for linear processes
Timothy Fortune, Hailin Sang
In this paper, we estimate the Shannon entropy $S(f) = -\E[ \log (f(x))]$ of a one-sided linear process with probability density function . We employ the integral estimator $…
A Local Limit Theorem for Linear Random Fields
Timothy Fortune, Magda Peligrad, Hailin Sang
In this paper, we establish a local limit theorem for linear fields of random variables constructed from independent and identically distributed innovations each with finite second…
A Berry-Esseen bound of order for martingales
Songqi Wu, Xiaohui Ma, Hailin Sang +1
Renz (Ann. Probab. 1996) has established a rate of convergence in the central limit theorem for martingales with some restrictive conditions. In the present paper a mo…
Cramér Type Moderate Deviations for Random Fields
Aleksandr Beknazaryan, Hailin Sang, Yimin Xiao
We study the Cramér type moderate deviation for partial sums of random fields by applying the conjugate method. The results are applicable to the partial sums of linear random fiel…
On mutual information estimation for mixed-pair random variables
Aleksandr Beknazaryan, Xin Dang, Hailin Sang
We study the mutual information estimation for mixed-pair random variables. One random variable is discrete and the other one is continuous. We develop a kernel method to estimate…
A rank-based Cramér-von-Mises-type test for two samples
Jamye Curry, Xin Dang, Hailin Sang
We study a rank based univariate two-sample distribution-free test. The test statistic is the difference between the average of between-group rank distances and the average of with…
Central limit theorem for the variable bandwidth kernel density estimators
Janet Nakarmi, Hailin Sang
In this paper we study the ideal variable bandwidth kernel density estimator introduced by McKay (1993) and Jones, McKay and Hu (1994) and the plug-in practical version of the vari…
Kernel entropy estimation for linear processes
Hailing Sang, Yongli Sang, Fangjun Xu
Let be a linear process with bounded probability density function . We study the estimation of the quadratic functional $\int_{\mathbb{R}} f^2(x)\,…
Exact Moderate and Large Deviations for Linear Random Fields
Hailin Sang, Yimin Xiao
By extending the methods in Peligrad et al. (2014a, b), we establish exact moderate and large deviation asymptotics for linear random fields with independent innovations. These res…
Gini Covariance Matrix and its Affine Equivariant Version
Xin Dang, Hailin Sang, Lauren Weatherall
We propose a new covariance matrix called Gini covariance matrix (GCM), which is a natural generalization of univariate Gini mean difference (GMD) to the multivariate case. The ext…
Symmetric Gini Covariance and Correlation
Yongli Sang, Xin Dang, Hailin Sang
Standard Gini covariance and Gini correlation play important roles in measuring the dependence of random variables with heavy tails. However, the asymmetry brings a substantial dif…
Memory properties of transformations of linear processes
Hailin Sang, Yongli Sang
In this paper, we study the memory properties of transformations of linear processes. Dittmann and Granger (2002) studied the polynomial transformations of Gaussian FARIMA(0,d,0) p…
On kernel estimators of density for reversible Markov chains
Martial Longla, Magda Peligrad, Hailin Sang
In this paper we investigate the kernel estimator of the density for a stationary reversible Markov chain. The proofs are based on a new central limit theorem for a triangular arra…
Further Refinement of Self-normalized Cram\a'{e}r-type moderate Deviations
Hailin Sang, Lin Ge
In this paper, we study the self-normalized Cram\a'{e}r-type moderate deviations for centered independent random variables with . The main resul…
Adjusting for Misclassification: A Three-Phase Sampling Approach
Hailin Sang, Kenneth K. Lopiano, Denise A. Abreu +3
The United States Department of Agriculture's National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification oc…
Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes
Hailin Sang, Yan Sun
We propose a sparse coefficient estimation and automated model selection procedure for autoregressive (AR) processes with heavy-tailed innovations based on penalized conditional ma…
Exact Moderate and Large Deviations for Linear Processes
Magda Peligrad, Hailin Sang, Yunda Zhong +1
Large and moderate deviation probabilities play an important role in many applied areas, such as insurance and risk analysis. This paper studies the exact moderate and large deviat…
Central Limit Theorem for Linear Processes with Infinite Variance
Magda Peligrad, Hailin Sang
This paper addresses the following classical question: giving a sequence of identically distributed random variables in the domain of attraction of a normal law, does the associate…