Results 31 to 40 of about 197,245 (124)
Predictive Performance Evaluation of the Kibria-Lukman Estimator
WSEAS TRANSACTIONS ON MATHEMATICS, 2022 Regression models are commonly used in prediction, but their predictive performances may be affected by the problem called the multicollinearity. To reduce the effect of the multicollinearity, different biased estimators have been proposed as alternatives to the ordinary least squares estimator.
Issam Dawoud +2 more
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Robust biased estimators for Poisson regression model: Simulation and applications
Concurrency and Computation: Practice and Experience, Volume 35, Issue 7, 25 March 2023., 2023 Summary The method of maximum likelihood flops when there is linear dependency (multicollinearity) and outlier in the generalized linear models. In this study, we combined the ridge estimator with the transformed M‐estimator (MT) and the conditionally unbiased bounded influence estimator (CE).
Adewale F. Lukman +2 more
wiley +1 more source
A New Type Iterative Ridge Estimator: Applications and Performance Evaluations
Journal of Mathematics, Volume 2022, Issue 1, 2022., 2022 The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square error.
Aydın Karakoca, Niansheng Tang
wiley +1 more source
Performance of the Ridge and Liu Estimators in the zero‐inflated Bell Regression Model
Journal of Mathematics, Volume 2022, Issue 1, 2022., 2022 The Poisson regression model is popularly used to model count data. However, the model suffers drawbacks when there is overdispersion—when the mean of the Poisson distribution is not the same as the variance. In this situation, the Bell regression model fits well to the data. Also, there is a high tendency of excess zeros in the count data.
Zakariya Yahya Algamal +4 more
wiley +1 more source
Mitigating Multicollinearity in Linear Regression Model with Two Parameter Kibria-Lukman Estimators
WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL, 2023 This study delves into the challenges faced by the ordinary least square (OLS) estimator, traditionally regarded as the Best Linear Unbiased Estimator in classical linear regression models. Despite its reliability under specific conditions, OLS falters in the face of multicollinearity, a problem frequently encountered in regression analyses.
Idowu J. I. +5 more
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A Liu estimator for the beta regression model and its application to chemical data
Journal of Chemometrics, Volume 34, Issue 10, October 2020., 2020 Abstract Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several
Peter Karlsson +2 more
wiley +1 more source
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
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
African Scientific Reports, 2022 Despite its common usage in estimating the linear regression model parameters, the ordinary least squares estimator often suffers a breakdown when two or more predictor variables are strongly correlated.
Abiola T. Owolabi +2 more
doaj +1 more source
A New Effective Jackknifing Estimator in the Negative Binomial Regression Model
, 2023 The negative binomial regression model is a widely adopted approach when dealing with dependent variables that consist of non-negative integers or counts.
Haydar Koç, Tuba Koç
core +1 more source