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
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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
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Predictive efficiency of ridge regression estimator [PDF]
Yugoslav Journal of Operations Research, 2017 In this article we have considered the problem of prediction within and outside the sample for actual and average values of the study variables in case of ordinary least squares and ridge regression estimators.
Tiwari Manoj, Sharma Amit
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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
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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
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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
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Development of the generalized ridge estimator for the Poisson-Inverse Gaussian regression model with multicollinearity [PDF]
Scientific ReportsThe Poisson-Inverse Gaussian regression model is a widely used method for analyzing count data, particularly in over-dispersion. However, the reliability of parameter estimates obtained through maximum likelihood estimation in this model can be ...
Fatimah A. Almulhim +5 more
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Modified Ridge Estimator for Poisson Regression
Cumhuriyet Science JournalPoisson regression is a statistical model used to model the relationship between a count-valued-dependent variable and one or more independent variables. A frequently encountered problem when modeling such relationships is multicollinearity, which occurs
Shuaib Mursal Ibrahim, Aydın Karakoca
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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
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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
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