Results 41 to 50 of about 221 (137)
Scientific African, 2022 The multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by lots of authors.
Issam Dawoud +2 more
doaj +1 more source
Comparison of estimators efficiency for linear regressions with joint presence of autocorrelation and multicollinearity [PDF]
, 2021 This paper proposes a new estimator called Two stage K-L estimator by combining these two estimators previously proposed by Prais Winsten (1958) and Kibra with Lukman (2020) for autocorrelation and multicollinearity respectively and to derived the ...
Adenomon, Monday Osagie +1 more
core +1 more source
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
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
Dawoud–Kibria Estimator for Beta Regression Model: Simulation and Application
Frontiers in Applied Mathematics and Statistics, 2022 The linear regression model becomes unsuitable when the response variable is expressed as percentages, proportions, and rates. The beta regression (BR) model is more appropriate for the variable of this form.
Mohamed R. Abonazel +3 more
doaj +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
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
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