Results 11 to 20 of about 173 (111)
New two parameter hybrid estimator for zero inflated negative binomial regression models [PDF]
Scientific ReportsThe zero-inflated negative binomial regression (ZINBR) model is used for modeling count data that exhibit both overdispersion and zero-inflated counts. However, a persistent challenge in the efficient estimation of parameters within ZINBR models is the ...
Fatimah A. Almulhim +5 more
doaj +2 more sources
A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator [PDF]
Computational Journal of Mathematical and Statistical SciencesThe 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 many authors.
Mohamed Reda Abonazel
doaj +3 more sources
Predictive modeling of COVID-19 death cases in Pakistan. [PDF]
Infect Dis Model, 2020 Background The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life.
Daniyal M +4 more
europepmc +3 more sources
Axioms, 2022 The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate ...
Issam Dawoud +3 more
doaj +1 more source
A new hybrid estimator for linear regression model analysis: Computations and simulations
Scientific African, 2023 The Linear regression model explores the relationship between a response variable and one or more independent variables. The parameters in the model are often estimated using the Ordinary Least Square Estimator (OLSE).
G.A. Shewa, F.I. Ugwuowo
doaj +1 more source
K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model
Mathematics, 2023 Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in both the linear and generalized linear models. The Kibria and Lukman estimator (KLE) was developed as an alternative to the MLE to handle multicollinearity ...
Adewale F. Lukman +5 more
doaj +1 more source
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
Jackknifing K-L estimator in Poisson regression model [PDF]
, 2022 At the point when there is collinearity between the reaction variable and various illustrative factors, displaying the connection between the reaction variable and a few informative factors is troublesome.
Algamal, Zakariya Yahya, Hamad, Abed Ali
core +2 more sources
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
Jackknife Kibria-Lukman M-Estimator: Simulation and Application
Journal of Nigerian Society of Physical Sciences, 2022 The ordinary least square (OLS) method is very efficient in estimating the regression parameters in a linear regression model under classical assumptions.
Segun L. Jegede +3 more
doaj +1 more source