Statistical methods of spatial analysis are often successful at either prediction or explanation, but not necessarily both. In a recent paper, Dearmon and Smith (2016) showed that by combining Gaussian Process Regression (GPR) with Bayesian Model Averaging (BMA), a modeling framework could be...

This paper considers the problem of estimating a partially linear varying coefficient fixed effects panel data model. Using the series method, we establish the root N normality for the estimator of the parametric component; and we show that the unknown function can be consistently estimated at...

We propose a nonparametric estimator of the Lorenz curve that satisfies its theoretical properties, including monotonicity and convexity. We adopt a transformation approach that transforms a constrained estimation problem into an unconstrained one, which is estimated nonparametrically. We...

Estimators for derivatives associated with a density function can be useful in identifying its modes and inflection points. In addition, these estimators play an important role in plug-in methods associated with bandwidth selection in nonparametric kernel density estimation. In this paper, we...

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically,...

This chapter focuses on examining how changes in the liquidity differential between nominal and TIPS yields influence optimal portfolio allocations in U.S. Treasury securities. Based on a nonparametric estimation technique and comparing the optimal allocation decisions of mean-variance and CRRA...

Motivated by increment process modeling for two correlated random and non-random systems from a discrete-time asset pricing with both risk free asset and risky security, we propose a class of semiparametric regressions for a combination of a non-random and a random system. Unlike classical...