Results 1 to 10 of about 1,779,669 (208)

A new kind of stochastic restricted biased estimator for logistic regression model. [PDF]

J Appl Stat, 2021
In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem.
Alheety MI, Månsson K, Golam Kibria BM.
europepmc   +2 more sources

A new class of Poisson Ridge-type estimator

Scientific Reports, 2023
The Poisson Regression Model (PRM) is one of the benchmark models when analyzing the count data. The Maximum Likelihood Estimator (MLE) is used to estimate the model parameters in PRMs. However, the MLE may suffer from various drawbacks that arise due to
Esra Ertan, Kadri Ulaş Akay
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A New Two-Parameter Estimator for Beta Regression Model: Method, Simulation, and Application

Frontiers in Applied Mathematics and Statistics, 2022
The beta regression is a widely known statistical model when the response (or the dependent) variable has the form of fractions or percentages. In most of the situations in beta regression, the explanatory variables are related to each other which is ...
Mohamed R. Abonazel, Zakariya Yahya Algamal, Fuad A. Awwad, Ibrahim M. Taha   +3 more
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New Stochastic Restricted Biased Regression Estimators

Mathematics
In this paper, we propose three stochastic restricted biased estimators for the linear regression model. These new estimators generalize the least squares estimator, mixed estimator, and biased estimator. We derive the necessary and sufficient conditions
Issam Dawoud, Hussein Eledum
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An unbiased estimator with prior information

Arab Journal of Basic and Applied Sciences, 2020
The ordinary least square (OLS) estimator suffers a breakdown in the presence of multicollinearity. The estimator is still unbiased but possesses a significant variance.
Adewale F. Lukman, Kayode Ayinde, Benedicta Aladeitan, Rasak Bamidele   +3 more
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Stochastic Restricted Biased Estimators in Misspecified Regression Model with Incomplete Prior Information

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, Pushpakanthie Wijekoon   +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 Advances
In 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, Sadiah M. A. Aljeddani, Muhammad Nauman Akram, Sajida Yasmeen   +3 more
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Joint optimization of MIMO radar waveform and biased estimator with prior information in the presence of clutter

EURASIP Journal on Advances in Signal Processing, 2011
In this article, we consider the problem of joint optimization of multi-input multi-output (MIMO) radar waveform and biased estimator with prior information on targets of interest in the presence of signal-dependent noise.
Liu Hongwei, Wang Hongyan, Liao Guisheng, Li Jun, Lv Hui   +4 more
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New robust two-parameter estimator for overcoming outliers and multicollinearity in Poisson regression model [PDF]

Scientific Reports
The Poisson maximum likelihood estimator (PMLE) is commonly used to estimate the coefficients of the Poisson regression model (PRM). However, it is well known that the PMLE is highly sensitive to outliers, which can distort the estimated coefficients and
Hebatalla H. Mohammad, Ali T. Hammad, Abeer A. EL-Helbawy, Zakiah I. Kalantan, Alexis Habineza, Eslam Hussam, Ahmed M. Gemeay   +6 more
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New two parameter hybrid estimator for zero inflated negative binomial regression models [PDF]

Scientific Reports
The zero-inflated negative binomial regression (ZINBR) model is used for modeling count data that exhibit both overdispersion and zero-inflated counts. However, a persistent challenge in the efficient estimation of parameters within ZINBR models is the ...
Fatimah A. Almulhim, M. Nagy, Ali T. Hammad, A. H. Mansi, Getachew Tekle Mekiso, M. M. Abd El-Raouf   +5 more
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