Results 1 to 10 of about 30,297 (145)
Scientific African, 2022 The parameters in the Poisson regression model are usually estimated using the maximum likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or outliers in the Poisson regression model.
Kingsley Chinedu Arum
exaly +3 more sources
A Poisson Ridge Regression Estimator [PDF]
Economic Modelling, 2011 The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML). The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression
Månsson, Kristofer, Shukur, Ghazi
core +3 more sources
Journal of Probability and Statistics, 2018 The analysis of misspecification was extended to the recently introduced stochastic restricted biased estimators when multicollinearity exists among the explanatory variables. The Stochastic Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost
Manickavasagar Kayanan +1 more
doaj +3 more sources
New ridge parameter estimators for the quasi-Poisson ridge regression model
Scientific ReportsThe quasi-Poisson regression model is used for count data and is preferred over the Poisson regression model in the case of over-dispersed count data.
Aamir Shahzad +3 more
doaj +3 more sources
Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation [PDF]
Surveys in Mathematics and its Applications, 2009 Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR).
Sharad Damodar Gore, Feras Sh. M. Batah
doaj +2 more sources
Polynomial ridge flowfield estimation [PDF]
Physics of Fluids, 2021 Computational fluid dynamics plays a key role in the design process across many industries. Recently, there has been increasing interest in data-driven methods in order to exploit the large volume of data generated by such computations. This paper introduces the idea of using spatially correlated polynomial ridge functions for rapid flowfield ...
A. Scillitoe +3 more
openaire +2 more sources
Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications
Iraqi Journal for Computer Science and Mathematics, 2023 Regression models explore the relationship between the response variable and one or more explanatory variables. It becomes practically challenging in real-life applications to model this relationship when the explanatory variables are linearly dependent.
Zakariya Algamal +3 more
doaj +1 more source
Modified jackknife ridge estimator for the Conway-Maxwell-Poisson model
Scientific African, 2023 Recently, research papers have shown a strong interest in modeling count data. The over-dispersion or under-dispersion are frequently seen in the count data.
Zakariya Yahya Algamal +3 more
doaj +1 more source
Generalized ridge estimator shrinkage estimation based on particle swarm optimization algorithm [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 It is well-known that in the presence of multicollinearity, the ridge estimator is an alternative to the ordinary least square (OLS) estimator. Generalized ridge estimator (GRE) is an generalization of the ridge estimator.
Qamar Abdul kareem, Zakariya Algamal
doaj +1 more source
Ridge regression and its applications in genetic studies.
PLoS ONE, 2021 With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling.
M Arashi +3 more
doaj +1 more source