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 Arum
exaly +3 more sources
A bias-reduced estimator for generalized Poisson regression with application to carbon dioxide emission in Canada [PDF]
Scientific ReportsThe generalized Poisson regression model (GPRM) provides a flexible framework for modeling count data, especially those exhibiting over- or underdispersion.
Fatimah M. Alghamdi +6 more
doaj +2 more sources
A New Mixed Biased Estimator for Ill‐Conditioning Challenges in Linear Regression Model With Chemometrics Applications [PDF]
Analytical Science AdvancesIn linear regression models, the ordinary least squares (OLS) method is used to estimate the unknown regression coefficients. However, the OLS estimator may provide unreliable estimates in non‐orthogonal models.
Muhammad Amin +3 more
doaj +2 more sources
Transfer Learning for Moderate–Dimensional Ridge-Regularized Robust Linear Regression [PDF]
EntropyThis paper studies transfer learning for ridge-regularized robust linear regression in the moderate–dimensional regime, where the number of predictors is of the same order as the sample size and the regression coefficients are not assumed to be sparse ...
Lingfeng Lyu, Xiao Guo, Zongqi Liu
doaj +2 more sources
A New Tobit Ridge-Type Estimator of the Censored Regression Model With Multicollinearity Problem
Frontiers in Applied Mathematics and Statistics, 2022 In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem.
Mohamed R Abonazel +2 more
exaly +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
New robust estimator for handling outliers and multicollinearity in gamma regression model with application to breast cancer data [PDF]
Scientific ReportsThe gamma regression model (GRM) is commonly used to analyze continuous data that are positively skewed. However, the GRM is sensitive to multicollinearity and outliers. These two problems often occur in regression analysis.
Arwa M. Alshangiti +7 more
doaj +2 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
Modified Ridge Logistic Estimator Based on Singular Value Decomposition [PDF]
The Egyptian Statistical Journal, 2023 This paper aims to introduce a modification of the ridge estimator based on the singular value decomposition (SVD) technique of the design matrix (X ) to combat multicollinearity in the binary logistic model.
Monira Hussein, Mostafa Abd el-Rahman
doaj +1 more source
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