On the mixed Kibria–Lukman estimator for the linear regression model [PDF]
Scientific Reports, 2022 This paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria–Lukman estimator by combining the mixed estimator and the Kibria–Lukman estimator.This new estimator is a general estimation, including OLS ...
Hongmei Chen, Jibo Wu
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Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model [PDF]
Sensors, 2022 A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working ...
Ayesha Sultan +4 more
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Modified Liu estimators in the linear regression model: An application to Tobacco data. [PDF]
PLoS ONE, 2021 BackgroundThe problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation.
Iqra Babar +5 more
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A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications [PDF]
Scientifica, 2020 The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models.
B. M. Golam Kibria, Adewale F. Lukman
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How thresholding in segmentation affects the regression performance of the linear model [PDF]
JASA Express Letters, 2023 Evaluating any model underlying the control of speech requires segmenting the continuous flow of speech effectors into sequences of movements. A virtually universal practice in this segmentation is to use a velocity-based threshold which identifies a ...
Stephan R. Kuberski, Adamantios I. Gafos
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Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model
Revstat Statistical Journal, 2023 In this study, we provide Theil-Sen parameter estimators, which are in multiple linear regression model based on a spatial median, to be examined by the jackknife method.
Tolga Zaman , Kamil Alakuş
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Robust model selection using the out-of-bag bootstrap in linear regression
Scientific Reports, 2022 Outlying observations have a large influence on the linear model selection process. In this article, we present a novel approach to robust model selection in linear regression to accommodate the situations where outliers are present in the data.
Fazli Rabbi +5 more
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Regression Model of Fixed Capital Investment With Dynamic Structural Parameters
Informaciâ i Innovacii, 2021 The article discusses the method of constructing regression models, when the parameters are variable values. The use of this method is illustrated by the example of the investment in fixed capital model.
N. N. Pankov
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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
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Multiple linear regression based model for the indoor temperature of mobile containers
Heliyon, 2022 It is important to work out precise and easy-to-use mathematical models to predict the indoor temperature in buildings for human residence. Such models can support model-based/predictive controls to efficiently maintain the temperature at a comfortable ...
Zoltán Patonai +2 more
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