Results 21 to 30 of about 2,939,863 (258)
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 +1 more
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Journal of Big Data, 2023 This paper presents a stochastic imputation approach for large datasets using a correlation selection methodology when preferred commercial packages struggle to iterate due to numerical problems. A variable range-based guard rail modification is proposed
Benjamin D. Leiby, Darryl K. Ahner
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Modelling daily water temperature from air temperature for the Missouri River [PDF]
PeerJ, 2018 The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature.
Senlin Zhu +2 more
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Bagging ensemble selection for regression [PDF]
, 2012 Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have
Quan Sun +3 more
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BMC Bioinformatics, 2021 Background Approximate Bayesian Computation (ABC) has become a key tool for calibrating the parameters of discrete stochastic biochemical models. For higher dimensional models and data, its performance is strongly dependent on having a representative set
Richard M. Jiang +4 more
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Multiple Imputation of Missing Traffic Volume: An Advanced Framework and Multi-Domain Validation
Applied SciencesHigh-frequency traffic data from remote sensors often suffer from severe gaps and multi-day blackouts. Traditional deterministic imputation fails during these extended failures, artificially destroying natural traffic variance.
Zaid Abdulzahra Mahdi Mandalawi +1 more
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Superiority of the Stochastic Restricted Liu Estimator under misspecification
Statistica, 2007 This paper deals with the use of correct prior infromation in the estimation of regression coefficients when the regression model is misspecified due to the exclusion of some relevant regressor variables.
M. H. Hubert, Pushba Wijekoon
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Performance of Some Stochastic Restricted Ridge Estimator in Linear Regression Model
Journal of Applied Mathematics, 2014 This paper considers several estimators for estimating the stochastic restricted ridge regression estimators. A simulation study has been conducted to compare the performance of the estimators.
Jibo Wu, Chaolin Liu
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Adaptive stochastic model predictive control of linear systems using Gaussian process regression
IET Control Theory & Applications, 2021 This paper presents a stochastic model predictive control method for linear time‐invariant systems subject to state‐dependent additive uncertainties modelled by Gaussian process (GP).
Fei Li, Huiping Li, Yuyao He
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New Stochastic Restricted Biased Regression Estimators
MathematicsIn 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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