Mixture models have been popular in the literature of both statistics and social science. In this dissertation, we propose a new mixture model, namely, nonparametric finite mixture of regression models, which can be viewed as a natural extension of finite mixture of linear regression. In the...

Previous studies show mixed results about the out-of-sample performance of various asset-pricing anomalies. To reduce data-snooping bias, this paper simulates a real-time trader who chooses among all asset-pricing anomalies published prior to that time using only non-forward-looking filters. I...

In this dissertation, several new statistical procedures in nonparametric and semiparametric models are proposed. The concerns of the research are efficiency, robustness and sparsity.In Chapter 3, we propose complete composite quantile regression (CQR) procedures for estimating both the...

CHAPTER 1: Discrete Choice Models With Local Interactions: A Game Theoretical ApproachConsider observations from a single equilibrium of a local interaction game in which each player, a firm, has a finite number of actions (discrete choice) and is subject to interactions that are localfor...

Nonparametric function estimation via local polynomial regression has been widely studied in the literature, especially in the past two decades. In practice, we confront two challenges. Firstly, the local least squares estimator may not be the best choice when errors are heavily tailed....

Nonparametric regression models have been used to explorefeatures of data. Various estimation procedures have been proposedfor estimating nonparametric regression models in the literature.The existing procedures may enjoy some statistically optimalproperties, but it is difficult to use the...

Motivated by an empirical analysis of a data set collected in the field of ecology, we proposed nonlinear varying-coefficient models, a new class of varying-coefficient models. We further propose an estimation procedure for the nonlinear varying-coefficient models by using local linear...