Results 21 to 30 of about 427,529 (281)
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, Heumann, Christian
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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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The Stochastic Fluctuation of the Quantile Regression Curve [PDF]
SSRN Electronic Journal, 2008 Let (X1, Y1), . . ., (Xn, Yn) be i.i.d. rvs and let l(x) be the unknown p-quantile regression curve of Y on X. A quantile-smoother ln(x) is a localised, nonlinear estimator of l(x). The strong uniform consistency rate is established under general conditions. In many applications it is necessary to know the stochastic fluctuation of the process {ln(x) -
Wolfgang Härdle, Song Song
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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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Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization [PDF]
, 2013 We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results
Shalev-Shwartz, Shai, Zhang, Tong
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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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Stochastic Estimation of the Maximum of a Regression Function [PDF]
The Annals of Mathematical Statistics, 1952 Let $M(x)$ be a regression function which has a maximum at the unknown point $\theta. M(x)$ is itself unknown to the statistician who, however, can take observations at any level $x$. This paper gives a scheme whereby, starting from an arbitrary point $x_1$, one obtains successively $x_2, x_3, \cdots$ such that $x_n$ converges to $\theta$ in ...
Kiefer, J., Wolfowitz, J.
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ON THE USING OF THE SHANNON INFORMATION QUANTITY IN THE TASKS ASSOCIATED WITH LINEAR REGRESSION
St. Petersburg Polytechnical University Journal: Physics and Mathematics, 2019 The article discusses the use of the Shannon information quantity (SIQ) in the tasks associated with linear regression. It is shown that the SIQ contained in the response components with respect to stochastic parameters is expressed through the Fisher ...
Pichugin Yury
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