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Stochastic semiparametric regression for spectrum cartography
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging regression task is addressed by decomposing the PSD at every location into a linear combination of the power spectra (due to individual transmitters and ...
Daniel Romero 0004 +2 more
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Stochastic Regression Model with Dependent Disturbances
Journal of Time Series Analysis, 2001In this paper, we consider the estimation of the coefficient of a stochastic regression model whose explanatory variables and disturbances are permitted to exhibit short‐memory or long‐memory dependence. Three estimators of the coefficient are proposed.
Choy, Kokyo, Taniguchi, Masanobu
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Binary Regression with Stochastic Covariates
Communications in Statistics - Theory and Methods, 2006In binary regression the risk factor X has been treated in the literature as a non-stochastic variable. In most situations, however, X is stochastic. We present solutions applicable to such situations. We show that our solutions are more precise than those obtained by treating X as non-stochastic when, in fact, it is stochastic.
exaly +2 more sources
Stochastic algorithms in nonlinear regression
Computational Statistics & Data Analysis, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Křivý, I., Tvrdík, J., Krpec, R.
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Bidirectional stochastic configuration network for regression problems
Neural Networks, 2021To adapt to the reality of limited computing resources of various terminal devices in industrial applications, a randomized neural network called stochastic configuration network (SCN), which can conduct effective training without GPU, was proposed. SCN uses a supervisory random mechanism to assign its input weights and hidden biases, which makes it ...
Weipeng Cao +5 more
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Preconditioned Bayesian Regression for Stochastic Chemical Kinetics
Journal of Scientific Computing, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alen Alexanderian +4 more
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A stochastic frontier regression model with dynamic frontier
Communications in Statistics - Simulation and Computation, 2020We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence.
T. V. Ramanathan +2 more
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Stochastic covariates in binary regression
2004Summary: Binary regression has many medical applications. In applying the technique, the tradition is to assume the risk factor \(X\) as a non-stochastic variable. In most situations, however, \(X\) is stochastic. In this study, we discuss the case when \(X\) is stochastic in nature, which is more realistic from a practical point of view than \(X ...
ORAL, Evrim, GÜNAY, Süleyman
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Stochastic approximation with a nonstationary regression function (Corresp.)
IEEE Transactions on Information Theory, 1972This correspondence is concerned with a stochastic approximation algorithm having a nonstationary regression function. Convergence conditions and a mean-square error bound are presented. Its possible application to feedback communication is discussed briefly.
Tzay Y. Young, R. Westerberg
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Broad stochastic configuration network for regression
Knowledge-Based Systems, 2022Chenglong Zhang 0001 +2 more
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