Results 11 to 20 of about 4,452 (291)
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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Estimation methods of logistic regression in context of multicollinearity (Comparative study) [PDF]
Maǧallaẗ Al-Buḥūṯ Al-Tiǧāriyyaẗ, 2023 The binary logistic regression (BLR) model is used as an alternative to the commonly used linear regression model when the response variable is binary.
Hassan Mohamed Ali +2 more
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Another Look at Partitioned Ridge Regression Estimators [PDF]
The Egyptian Statistical Journal, 1992 Several biased estimators have been proposed as alternatives to the Least squares estimator when multicollinearity is present in the multiple linear regression model.
Linda Abskharoon, Mahmoud Mahmoud
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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 C Arum +2 more
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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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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
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Superiority of the MCRR Estimator Over Some Estimators In A Linear Model [PDF]
المجلة العراقية للعلوم الاحصائية, 2011 Modified (r, k) class ridge regression (MCRR) which includes unbiased ridge regression (URR), (r, k) class, principal components regression (PCR) and the ordinary least squares (OLS) estimators is proposed in regression analysis, to overcome the problem ...
Feras Sh. M. Batah
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BAYESIAN-RIDGE ESTIMATOR FOR LINEAR REGRESSION MODEL [PDF]
, 2023 Multicollinearity is a problem associated with inter-dependence of explanatory variables in linear regression model. The inefficiency of the Ordinary Least Square (OLS) Estimator lead to development of various other methods which include the Ridge ...
Idowu Usman
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Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 This paper introduces some new estimators for estimating ridge parameter, based on correlation between response and regressor variables for ridge regression analysis.
A.V. Dorugade
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Minimax Ridge Regression Estimation. [PDF]
The Annals of Statistics, 1977 The technique of ridge regression, first proposed by Hoerl and Kennard, has become a popular tool for data analysts faced with a high degree of multicollinearity in their data. By using a ridge estimator, one hopes to both stabilize one's estimates (lower the condition number of the design matrix) and improve upon the squared error loss of the least ...
openaire +2 more sources