We provide a review on the empirical likelihood method for regression-type inference
problems. The regression models considered in this review include parametric …

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 …

We study semiparametric varying-coefficient partially linear models when some linear
covariates are not observed, but ancillary variables are available. Semiparametric profile …

We develop a specification test for the transition density of a discretely sampled continuous-
time jump-diffusion process, based on a comparison of a nonparametric estimate of the …

In many applications, quality of a process is best characterized by a functional relationship
between a response variable and one or more explanatory variables. Profile monitoring is …

Effective uncertainty management processes are essential elements in the design of
prognostic modules if they to be viable for Integrated Vehicle Health Management (IVHM) …

We consider selection of nested and non-nested semiparametric models. Using profile
likelihood we can define both a likelihood ratio statistic and an Akaike information for models …

We investigate the asymptotic normality of the posterior distribution in the discrete setting,
when model dimension increases with sample size. We consider a probability mass function …

This paper gives a selective review on some recent developments of nonparametric
methods in both continuous and discrete time finance, particularly in the areas of …

Parametric option pricing models are widely used in finance. These models capture several
features of asset price dynamics; however, their pricing performance can be significantly …