Results 11 to 20 of about 71,521 (260)

Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data

open access: yesRemote Sensing, 2022
Land cover area estimates can be derived via design-based approaches using a probability (random) reference sample. The collection of samples is usually costly and requires an effective sampling design.
Luca Kleinewillinghöfer   +6 more
doaj   +1 more source

Another Look at Partitioned Ridge Regression Estimators [PDF]

open access: yesThe 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
doaj   +1 more source

M-estimators for isotonic regression [PDF]

open access: yesJournal 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   +1 more
openaire   +4 more sources

Risk Estimation via Regression [PDF]

open access: yesOperations Research, 2015
We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate financial risk factors and an inner simulation level is used to price securities and compute portfolio losses given risk factor outcomes.
Mark Broadie   +2 more
openaire   +2 more sources

Modified Unbiased Optimal Estimator For Linear Regression Model [PDF]

open access: yesمجلة جامعة الانبار للعلوم الصرفة, 2023
In this paper, we propose a novel form of Generalized Unbiased Optimal Estimator where the explanatory variables are multicollinear. The proposed estimator's bias, variance, and mean square error matrix (MSE) are calculated.
Hussein AL-jumaili, Mustafa Alheety
doaj   +1 more source

Illuminance Flow Estimation by Regression [PDF]

open access: yesInternational Journal of Computer Vision, 2010
We investigate the estimation of illuminance flow using Histograms of Oriented Gradient features (HOGs). In a regression setting, we found for both ridge regression and support vector machines, that the optimal solution shows close resemblance to the gradient based structure tensor (also known as the second moment matrix).
Karlsson, SM   +3 more
openaire   +7 more sources

Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 2; peer review: 2 approved, 1 approved with reservations]

open access: yesF1000Research, 2021
Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the
Olukayode Adebimpe   +4 more
doaj   +1 more source

Predictive Estimation of Population Mean in Ranked Set Sampling

open access: yesRevstat Statistical Journal, 2019
The article presents predictive estimation of population mean of the study variable in Ranked Set Sampling (RSS). It is shown that the predictive estimators in RSS using mean per unit estimator, ratio estimator and regression estimator as predictor for ...
Shakeel Ahmed   +2 more
doaj   +1 more source

A Suggested Biased Estimator for Correcting Multicollinearity in Multinomial Logistic Regression [PDF]

open access: yesThe Egyptian Statistical Journal, 2014
Multinomial logistic model suffers from multicollinearity that causes wider confidence intervals and incorrect decisions for testing hypotheses for the regression parameters.
Rasha A.Farghali
doaj   +1 more source

On the Admissibility of the Regression Estimator

open access: yesJournal of the Royal Statistical Society Series B: Statistical Methodology, 1984
SUMMARY Admissibility of the regression estimator is established for two types of sampling designs: single stage sampling using probability proportional to a size variable and double sampling.
Bellhouse, D. R., Joshi, V. M.
openaire   +2 more sources

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