Results 31 to 40 of about 427,529 (281)
Stochastic EM for Shuffled Linear Regression
CoRR, 2018 We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known. Such datasets naturally arise from experiments in which the samples are shuffled or permuted during the protocol.
Abubakar Abid, James Y. Zou
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A review on quantile regression for stochastic computer experiments
Reliability Engineering & System Safety, 2020 We report on an empirical study of the main strategies for quantile regression in the context of stochastic computer experiments. To ensure adequate diversity, six metamodels are presented, divided into three categories based on order statistics, functional approaches, and those of Bayesian inspiration.
Torossian, Léonard +3 more
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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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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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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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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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Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors [PDF]
Journal of Sciences, Islamic Republic of Iran, 2019 In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression ...
Hoda Mohammadi, Abdolrahman Rasekh
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IEEE Access, 2022 This paper proposes an improved redundant rule based lasso regression stochastic gradient (RR-LR-SG) algorithm for time-delayed models. The improved SG algorithm can update the parameter elements with different step-sizes and directions, thus it is more ...
Hangtao Zhao, Lixin Lv, Yuejiang Ji
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Competence region estimation for black-box surrogate models
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 With advances in edge applications for industry andhealthcare, machine learning models are increasinglytrained on the edge. However, storage and memory in-frastructure at the edge are often primitive, due to costand real-estate constraints.
Tapan Shah
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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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