Nonparametric regression methods provide an alternative approach to parametric
estimation that requires only weak identification assumptions and thus minimizes the risk of …

This paper studies evolution of the asymmetric sheltering role of Bitcoin compared to gold
against oil-related uncertainties with varying severity of the COVID-19 pandemic. Using a …

The Oxford Handbook of Panel Data examines new developments in the theory and
applications of panel data. It includes basic topics like non-stationary panels, co-integration …

The complexity of semiparametric models poses new challenges to statistical inference and
model selection that frequently arise from real applications. In this work, we propose new …

Semiparametric models are often considered for analyzing longitudinal data for a good
balance between flexibility and parsimony. In this paper, we study a class of marginal …

We provide an overview of linear quantile regression models for continuous responses
repeatedly measured over time. We distinguish between marginal approaches, that explicitly …

We propose a new nonparametric conditional cumulative distribution function kernel
estimator that admits a mix of discrete and categorical data along with an associated …

Quantile regression provides a framework for modeling statistical quantities of interest other
than the conditional mean. The regression methodology is well developed for linear models …

We propose a data-driven least-square cross-validation method to optimally select
smoothing parameters for the nonparametric estimation of conditional cumulative …

We investigated the risk factors for childhood malnutrition in India based on the 2005/2006
Demographic and Health Survey by applying a novel estimation technique for additive …