Results 51 to 60 of about 551 (134)
Modified almost unbiased two-parameter estimator for the Poisson regression model with an application to accident data [PDF]
, 2021 Due to the large amount of accidents negatively affecting the wellbeing of the survivors and their families, a substantial amount of research is conducted to determine the causes of road accidents.
Alheety, Mustafa I. +3 more
core +2 more sources
Modified One‐Parameter Liu Estimator for the Linear Regression Model
Modelling and Simulation in Engineering, Volume 2020, Issue 1, 2020., 2020 Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter.
Adewale F. Lukman +4 more
wiley +1 more source
Almon-KL estimator for the distributed lag model [PDF]
, 2021 The Almon technique is widely used to estimate the parameters of the distributed lag model (DLM). The technique suffers a setback from the challenge of multicollinearity because the explanatory variables and their lagged values are often correlated.
Kibria, Golam B.M., Lukman, Adewale F.
core +1 more source
Monte Carlo Study of Some Classification-Based Ridge Parameter Estimators [PDF]
, 2017 Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been proposed. In this study, estimators based on Dorugade (2014) and Adnan et al.
Ajiboye, Adegoke S. +2 more
core +3 more sources
M Robust Weighted Ridge Estimator in Linear Regression Model [PDF]
, 2023 Correlated regressors are a major threat to the performance of the conventional ordinary least squares (OLS) estimator. The ridge estimator provides more stable estimates in this circumstance.
Kayode Ayinde +2 more
core +2 more sources
Robust weighted ridge regression based on S – estimator [PDF]
, 2023 Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables.
Abimbola Hamidu Bello +3 more
core +2 more sources
Almost Unbiased Ridge Estimator in the Inverse Gaussian Regression Model [PDF]
, 2022 The inverse Gaussian regression (IGR) model is a very common model when the shape of the response variable is positively skewed. The traditional maximum likelihood estimator (MLE) is used to estimate the IGR model parameters.
Al-Taweel, Younus Hazim +1 more
core +5 more sources
A Modified New Two‐Parameter Estimator in a Linear Regression Model
Modelling and Simulation in Engineering, Volume 2019, Issue 1, 2019., 2019 The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified new two‐parameter estimator based on prior information for the vector of parameters is ...
Adewale F. Lukman +4 more
wiley +1 more source
Kibria-Lukman Hybrid Estimator for the Conway–Maxwell–Poisson Regression Model [PDF]
The Conway-Maxwell-Poisson regression (CMPR) model provides a flexi- ble framework for analyzing count data in cases of over- and under-dispersion. Estimating the parameter in CMPR typically relies on the maximum likeli- hood estimator (MLE), which can ...
Alrweili, Hleil
core +3 more sources
A comparative study between shrinkage methods (ridge-lasso) using simulation [PDF]
, 2023 The general linear model is widely used in many scientific fields, especially biological ones. The Ordinary Least Squares (OLS) estimators for the coefficients of the general linear model are characterized by good specifications symbolized by the acronym
AL-Temimi, Suhad Ali Shaheed +1 more
core +5 more sources