Results 31 to 40 of about 82,111 (284)
A Generalized Linear Transformation and Its Effects on Logistic Regression
Mathematics, 2023 Linear transformations such as min–max normalization and z-score standardization are commonly used in logistic regression for the purpose of scaling.
Guoping Zeng, Sha Tao
doaj +1 more source
The performance of some new estimated ridge parameter regression model [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produces large variations in the sample. To overcome this problem,
Fatima ALfahdawe, Mustafa Alheety
doaj +1 more source
Mathematics, 2020 In the modeling and analysis of reliability data via the Lindley distribution, the maximum likelihood estimator is the most commonly used for parameter estimation. However, the maximum likelihood estimator is highly sensitive to the presence of outliers.
Muhammad Aslam Mohd Safari +2 more
doaj +1 more source
Revista de Economía MundialThis study explores how mineral resource depletion, population density, energy productivity, and economic development affect environmental degradation in Emerging 7 (E-7) countries—Brazil, Indonesia, China, Mexico, India, Türkiye, and Russia. The Method
Mehmet Bölükbas +2 more
doaj +1 more source
An Unbiased Two-Parameter Estimation with Prior Information in Linear Regression Model
The Scientific World Journal, 2014 We introduce an unbiased two-parameter estimator based on prior information and two-parameter estimator proposed by Özkale and Kaçıranlar, 2007. Then we discuss its properties and our results show that the new estimator is better than the two-parameter ...
Jibo Wu
doaj +1 more source
Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model
The Scientific World Journal, 2020 The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter.
Adewale F. Lukman +3 more
doaj +1 more source
Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices [PDF]
Annals of Statistics, 2019 The first part of this paper is devoted to the decision-theoretic analysis of random-design linear prediction. It is known that, under boundedness constraints on the response (and thus on regression coefficients), the minimax excess risk scales, up to ...
Jaouad Mourtada
semanticscholar +1 more source
Convex Combination of Ordinary Least Squares and Two-stage Least Squares Estimators
, 2015 In the presence of confounders, the ordinary least squares (OLS) estimator is known to be biased. This problem can be remedied by using the two-stage least squares (TSLS) estimator, based on the availability of valid instrumental variables (IVs). This reduction in bias, however, is offset by an increase in variance.
Ginestet, Cedric E. +2 more
openaire +2 more sources
Health Literacy, Self‐Efficacy and Knowledge of Sickle Cell Disease Among Caregivers
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Sickle cell disease (SCD) is a hereditary blood disorder in which abnormal haemoglobin leads to severe anaemia, painful crises and organ failure. Caregivers’ health literacy (HL) – their ability to assess, understand and apply information, and interact with healthcare professionals – is crucial for managing children with SCD, yet ...
Melanie Bruinooge +6 more
wiley +1 more source
Robust-stein estimator for overcoming outliers and multicollinearity
Scientific Reports, 2023 Linear regression models with correlated regressors can negatively impact the performance of ordinary least squares estimators. The Stein and ridge estimators have been proposed as alternative techniques to improve estimation accuracy.
Adewale F. Lukman +3 more
doaj +1 more source