The nonparametric regression model with correlated errors is a powerful tool for time series
forecasting. We are interested in the estimation of such a model, where the errors follow an …

In this paper, we consider James–Stein shrinkage and pretest estimation methods for time
series following generalized linear models when it is conjectured that some of the …

In this paper, we consider the quantile linear regression models with autoregressive errors.
By incorporating the expectation–maximization algorithm into the considered model, the …

Quantile regression (QR) is a natural alternative for depicting the impact of covariates on the
conditional distributions of a outcome variable instead of the mean. In this paper, we …

The aim of this thesis is to determine the data requirements and feasibility of data-driven top-
down stress testing for credit loss rates. To that end, we use the Adaptive Lasso method to …

This study investigates the nexus between the macroeconomic environment and
entrepreneurship in Nigeria using linear regression with ARMA (autoregressive moving …

Bridge penalized regression has many desirable statistical properties such as
unbiasedness, sparseness as well as 'oracle'. In Bayesian framework, bridge regularized …

Time lag effect exists widely in the course of economic operation. Some economic variables
are affected not only by various factors in the current period but also by various factors in the …

In the quantile regression framework, we incorporate Bayesian $ L_ {1/2} $ and adaptive $
L_ {1/2} $ penalties into quantile linear regression models with autoregressive (AR) errors to …

A two-step model selection procedure is proposed for autoregressive and moving-average
(ARMA) model class. It is an adaptive least absolute shrinkage and selection operator …