Results 31 to 40 of about 425,331 (287)
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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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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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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On stochastic accelerated gradient with non-strongly convexity
AIMS Mathematics, 2022 In this paper, we consider stochastic approximation algorithms for least-square and logistic regression with no strong-convexity assumption on the convex loss functions.
Yiyuan Cheng +3 more
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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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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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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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Accounting for measurement error in log regression models with applications to accelerated testing. [PDF]
PLoS ONE, 2018 In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals.
Robert Richardson +3 more
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Approximation of backward stochastic differential equations using Malliavin weights and least-squares regression [PDF]
, 2016 We design a numerical scheme for solving a Dynamic Programming equation with Malliavin weights arising from the time-discretization of backward stochastic differential equations with the integration by parts-representation of the $Z$-component by (Ann ...
Gobet, Emmanuel, Turkedjiev, Plamen
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