The varying coefficient model is a useful extension of the linear regression model. Nevertheless, how to conduct variable selection for the varying coefficient model in a computationally efficient manner is poorly understood. To solve the problem, we propose here a novel method, which combines...

In Chinese stock market, firms reporting two consecutive annual losses are subject to special treatment (ST), with further losses causing the firms' stocks to be suspended from trading or to be delisted. We argue that these earnings-based delisting requirements are misconstrued. Such policies...

Retailers are interested in understanding which price promotions are profitable and which are not. However, simultaneously estimating the promotion effects of a large number of products on retailer sales and profits is technically challenging for both researchers and practitioners. To address...

Different from traditional statistical analysis that concerns about individuals, network analysis focuses more on the dichotomous relationships between those individuals. It is then of interest to investigate the relationship against a set of predictive variables. The widely used generalized...

Researchers typically encounter missing data in practice and have developed various imputation methods. However, the existing methods are mainly developed for independent data and the assumption of independence disregards the connections of units through various social relationships (e.g.,...

A novel function of live streaming is that viewers can send paid gifts to broadcasters. In addition, viewers can engage with broadcasters by sending danmaku, a type of comment scrolled across the screen in real time. This paper investigates the role of viewers' social interaction in paid gifting...

We propose in this article a Composite Logistic Regression (CLR) approach for ordinal panel data regression. The new method transforms the original ordinal regression problem into a number of binary ones. Thereafter, the method of conditional logistic regression (Chamberlain, 1984; Wooldridge, 2001;...

The penalised least squares approach with smoothly clipped absolute deviation penalty has been consistently demonstrated to be an attractive regression shrinkage and selection method. It not only automatically and consistently selects the important variables, but also produces estimators which...

In regression analysis, we employ contour projection (CP) to develop a new dimension reduction theory. Accordingly, we introduce the notions of the central contour subspace and generalized contour subspace. We show that both of their structural dimensions are no larger than that of the central...

The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection. In this article we combine these two classical ideas together to produce...