This presentation reconsiders Knight's Risk, Uncertainty, and Profit of 1921 in light of the emergence of the World Wide Web in early-1990s, Emanuel Derman's pioneering work in Model Risk Management at Goldman Sachs in mid-1990s, backlash against quantitative models in aftermath of the Global...

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...

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...

The maximum rank correlation (MRC) estimator was originally studied by Han [1987. Nonparametric analysis of a generalized regression model. J. Econometrics 35, 303-316] and Sherman [1993. The limiting distribution of the maximum rank correlation estimator. Econometrica 61, 123-137] from the...

shrinkage and selection. In this article, we extend its application to the REGression model with AutoRegressive errors (REGAR). Two types of lasso estimators are carefully studied. The first is similar to the traditional lasso estimator with only two tuning parameters (one for regression...

We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Speciffically, LSA can transfer many different...

Contemporary statistical research frequently deals with problems involving a diverging number of parameters. For those problems, various shrinkage methods (e.g., LASSO, SCAD, etc) are found particularly useful for the purpose of variable selection (Fan and Peng, 2004; Huang et al., 2007b)....

There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality...

We propose in this article a novel dimension reduction method for varying coefficient models. The proposed method explores the rank reducible structure of those varying coefficients, hence, can do dimension reduction and semiparametric estimation, simultaneously. As a result, the new method not...