In this thesis, we study nonparametric identification of first-price auction models and propose a semi-nonparametric simulated integratedmoment estimation method to recover the underlying value distribution.In the first essay, we investigate the nonparametric identification of the first-price...

The analysis of auctions is an active area of research for both theoretical and empirical economists. Concentrating on a particular auction format, namely the First--Price Sealed--Bid auction, this dissertation contributes to the analysis of auction both from a methodological viewpoint and from...

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...

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...

When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and...

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...