In this paper we consider model averaging for quantile regressions (QR) when all models
under investigation are potentially misspecified and the number of parameters is diverging …

We in this paper propose a semiparametric partially linear varying coefficient (SPLVC)
modal regression, in which the conditional mode function of the response variable given …

Debiased machine learning estimators for nonparametric inference of smooth functionals of
the data-generating distribution can suffer from excessive variability and instability. For this …

This paper presents recent developments in model selection and model averaging for
parametric and nonparametric models. While there is extensive literature on model selection …

We consider model (variable) selection in a semi-parametric time series model with
functional coefficients. Variable selection in the semi-parametric model must account for the …

Abstract Freund and Schapire (1997) introduced “Discrete AdaBoost”(DAB) which has been
mysteriously effective for the high-dimensional binary classification or binary prediction. In …

Quantile regression (QR) is becoming increasingly popular due to its relevance in many
scientific investigations. Linear and nonlinear QR models have been studied extensively …

Additive models are regression methods which model the response variable as the sum of
univariate transfer functions of the input variables. Key benefits of additive models are their …

Many models in marketing are “parametric”. Parametric models impose a mathematical
function that links the variables, and this mathematical function contains parameters that …

This dissertation is composed with 4 essays. They explore modelling uncertainty following
two major directions. The former 2 contains topics on ordinary and general ridge-type …