Results 21 to 30 of about 353,670 (291)

Regression Estimator Using Double Ranked Set Sampling

Sultan Qaboos University Journal for Science, 2002
The performance of a regression estimator based on the double ranked set sample (DRSS) scheme, introduced by Al-Saleh and Al-Kadiri (2000), is investigated when the mean of the auxiliary variable X is unknown.
Hani M. Samawi, Eman M. Tawalbeh
doaj   +1 more source

M-estimators for isotonic regression [PDF]

Journal of Statistical Planning and Inference, 2012
In this paper we propose a family of robust estimates for isotonic regression: isotonic M-estimators. We show that their asymptotic distribution is, up to an scalar factor, the same as that of Brunk's classical isotonic estimator. We also derive the influence function and the breakdown point of these estimates.
Alvarez, Enrique Ernesto, Yohai, Victor Jaime   +1 more
openaire   +4 more sources

Estimation of the general population parameter in single- and two-phase sampling

AIMS Mathematics, 2023
Estimation of population characteristics has been an area of interest for many years. Various estimators of the population mean and the population variance have been proposed from time-to-time with a view to improve efficiency of the estimates.
Saman Hanif Shahbaz, Aisha Fayomi , Muhammad Qaiser Shahbaz   +2 more
doaj   +1 more source

A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model

Stats, 2020
In a multiple linear regression model, the ordinary least squares estimator is inefficient when the multicollinearity problem exists. Many authors have proposed different estimators to overcome the multicollinearity problem for linear regression models ...
Issam Dawoud, B. M. Golam Kibria
doaj   +1 more source

Handling Nonresponse in Business Surveys

Survey Research Methods, 2012
Business surveys are a valuable indication of the current and the future economic situation. However refusals are very common in this context and may induce bias in the estimates of interest.
Riccardo Borgoni, Donata Marasini, Piero Quatto   +2 more
doaj   +1 more source

Model-robust regression and a Bayesian ``sandwich'' estimator [PDF]

, 2010
We present a new Bayesian approach to model-robust linear regression that leads to uncertainty estimates with the same robustness properties as the Huber--White sandwich estimator.
Lumley, Thomas, Rice, Kenneth M., Szpiro, Adam A.   +2 more
core   +3 more sources

Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model

Journal of Applied Mathematics, 2014
Batah et al. (2009) combined the unbiased ridge estimator and principal components regression estimator and introduced the modified r-k class estimator.
Jibo Wu
doaj   +1 more source

Improved Mixed Estimator Using Two Auxiliary Variables For Full Extreme Maximum And Minimum Values In Single Phase Sampling [PDF]

المجلة العراقية للعلوم الاحصائية
The use of multiple auxiliary variables has been established to improve precision in the estimators of ratio, regression and product respectively. However, the presence of extreme values in the distribution could annul such efficiency Olatayo et al ...
Timothy O. Olatayo, Peter N. Madu, Peter I. Ogunyinka   +2 more
doaj   +1 more source

Modified Ridge Estimator for Poisson Regression

Cumhuriyet Science Journal
Poisson 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
doaj   +1 more source

Which quantile is the most informative? Maximum likelihood, maximum entropy and quantile regression [PDF]

, 2010
This paper studies the connections among quantile regression, the asymmetric Laplace distribution, maximum likelihood and maximum entropy. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we ...
Bera, A. K., Galvao Jr, A. F., Montes-Rojas, G., Park, S. Y.   +3 more
core   +1 more source