Results 41 to 50 of about 71,763 (309)
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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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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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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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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Reduced Rank Stochastic Regression with a Sparse Singular value Decomposition [PDF]
, 2011 Summary For a reduced rank multivariate stochastic regression model of rank r*, the regression coefficient matrix can be expressed as a sum of r* unit rank matrices each of which is proportional to the outer product of the left and right ...
Nils Chr. Stenseth +2 more
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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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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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Stochastic Development Regression on Non-linear Manifolds [PDF]
, 2017 We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is based on stochastic development of Euclidean diffusion processes to the manifold.
Line Kühnel, Stefan Sommer
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