Results 11 to 20 of about 1,024,622 (260)
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
Study of Some Kinds of Ridge Regression Estimators in Linear Regression Model
Tikrit Journal of Pure Science, 2020 In linear regression model, the biased estimation is one of the most commonly used methods to reduce the effect of the multicollinearity. In this paper, a simulation study is performed to compare the relative efficiency of some kinds of biased ...
Mustafa Nadhim Lattef, Mustafa I ALheety
doaj +1 more source
Structural Change Analysis in Linear Regression Model.
Revista de Matemática: Teoría y Aplicaciones, 2010 Assuming that the observations are from normal distribution we obtain de distribution of the maximum likelihood ratio test if there is a change in the parameters at an unknown time and we find the maximum likehood estimators of the time change too.
Blanca Rosa Pérez Salvador +1 more
doaj +1 more source
Re-sampling in Linear Regression Model Using Jackknife and Bootstrap [PDF]
المجلة العراقية للعلوم الاحصائية, 2010 Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic.
Zakariya Y. Algamal, Khairy B. Rasheed
doaj +1 more source
RESEARCH ON GPS HEIGHT FITTING BASED ON LINEAR REGRESSION MODEL [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 This paper mainly expounds the parameter estimation method, the outlier diagnosis and the establishment of the optimal regression equation in the linear regression model theory, the analysis of the principle of the polynomial fitting model, the ...
K. Y. Yang +7 more
doaj +1 more source
Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model
Journal of Probability and Statistics, 2020 Among several variable selection methods, LASSO is the most desirable estimation procedure for handling regularization and variable selection simultaneously in the high-dimensional linear regression models when multicollinearity exists among the ...
Manickavasagar Kayanan +1 more
doaj +1 more source
Generating atmospheric forcing perturbations for an ocean data assimilation ensemble
Tellus: Series A, Dynamic Meteorology and Oceanography, 2019 Running ensemble of reanalyses or forecasts has proved successful at improving their performances, despite the cost. Generating ensemble simulations requires generating perturbations within the models, and for the assimilated observations and subsidiary ...
Isabelle Mirouze, Andrea Storto
doaj +1 more source
Hidden Markov Linear Regression Model and its Parameter Estimation
IEEE Access, 2020 This article first defines a hidden Markov linear regression model for the purpose of further studying the mutual transformation between different states in the linear regression model, and the regression relationship between the dependent variable and ...
Hefei Liu, Kunqjnu Wang, Yong Li
doaj +1 more source
Identifying Outlier Observations in Linear - Circular Regression Model
پژوهشهای ریاضی, 2020 One way to identify outlier observations in regression models, is to measure the difference between the observations and their expected values under fitted model. This identification in circular regression, is possible by using of a circular distance. In
Seyede Sedighe Azimi +1 more
doaj
Modified One-Parameter Liu Estimator for the Linear Regression Model
Modelling and Simulation in Engineering, 2020 Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model.
Adewale F. Lukman +3 more
doaj +1 more source