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Non-asymptotic confidence estimation of the parameters in stochastic regression models with Gaussian noises [PDF]
, 2020 The article considers the problem of estimating linear parameters in stochastic regression models with Gaussian noises, such as an autoregression of the first order, threshold autoregression, and some others.
Konev, Victor V. +1 more
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Model Specification Tests in Nonparametric Stochastic Regression Models [PDF]
, 2002 In this paper, we consider testing for additivity in a class of nonparametric stochastic regression models. Two test statistics are constructed and their asymptotic distributions are established.
Wolff, Rodney C. +8 more
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Marginal and Conditional both Extreme Value Distributions: A Case of Stochastic Regression Model [PDF]
, 2023 A mathematical model is a mathematical connection that describes some real-life scenario. To handle real-world problems securely and effectively, simulation modelling is required.
Sulaxana Bharali +5 more
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Strong Consistency of Bayes Estimates in Stochastic Regression Models [PDF]
, 1996 Under minimum assumptions on the stochastic regressors, strong consistency of Bayes estimates is established in stochastic regression models in two cases: (1)When the prior distribution is discrete, the p.d.f.fof i.i.d.
Hu, Inchi
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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
doaj +1 more source
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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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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Asymptotic properties of projections with applications to stochastic regression problems [PDF]
, 1982 Almost sure convergence properties of least-squares estimates in stochastic regression models and an asymptotic theory of related Euclidean projections are developed herein. Applications to autoregressive processes and to dynamic input-output systems are
Lai, T.L, Wei, C.Z
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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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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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