Results 11 to 20 of about 2,939,863 (258)
Prior elicitation and variable selection for bayesian quantile regression [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
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Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models
Mathematics, 2020 We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input.
Yuri S. Popkov +3 more
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Trend detection and stochastic simulation prediction of streamflow at Yingluoxia hydrological station, Heihe River Basin, China [PDF]
Frontiers of Agricultural Science and Engineering, 2017 Investigating long-term variation and prediction of streamflow are critical to regional water resource management and planning. Under the continuous influence of climate change and human activity, the trends of hydrologic time series are nonstationary ...
Chenglong ZHANG,Mo LI,Ping GUO
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A posteriori error estimation for stochastic static problems [PDF]
, 2014 To solve stochastic static field problems, a discretization by the Finite Element Method can be used. A system of equations is obtained with the unknowns (scalar potential at nodes for example) being random variables. To solve this stochastic system, the
MAC, Hung, CLENET, Stephane
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Frontiers in Applied Mathematics and Statistics, 2021 The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make ...
P.L. de Andres +2 more
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Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions [PDF]
, 2007 The paper considers the construction of estimators of regression coefficients in a linear regression model when some stochastic and biased apriori information is available. Such apriori information is framed as stochastic restrictions.
---, Shalabh +2 more
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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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Learning from low precision samples
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 With advances in edge applications in industry and healthcare, machine learning models are increasingly trained on the edge. However, storage and memory infrastructure at the edge are often primitive, due to cost and real-estate constraints.
Ji In Choi +5 more
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Stats, 2022 The first hitting time of a boundary or threshold by the sample path of a stochastic process is the central concept of threshold regression models for survival data analysis.
Mei-Ling Ting Lee, George A. Whitmore
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A regularized stochastic configuration network based on weighted mean of vectors for regression [PDF]
PeerJ Computer Science, 2023 The stochastic configuration network (SCN) randomly configures the input weights and biases of hidden layers under a set of inequality constraints to guarantee its universal approximation property.
Yang Wang +4 more
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