Wavelets for Nonparametric Stochastic Regression with Pairwise Negative Quadrant Dependent Random Variables [PDF]
Journal of Sciences, Islamic Republic of Iran, 2005 We propose a wavelet based stochastic regression function estimator for the estimation of the regression function for a sequence of pairwise negative quadrant dependent random variables with a common one-dimensional probability density function.
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Stochastically ordered multiple regression [PDF]
Biostatistics, 2010 In various application areas, prior information is available about the direction of the effects of multiple predictors on the conditional response distribution. For example, in epidemiology studies of potentially adverse exposures and continuous health responses, one can typically assume a priori that increasing the level of an exposure does not lead ...
Bornkamp, Björn +2 more
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Generalized Stochastic Restricted LARS Algorithm
Ruhuna Journal of Science, 2022 The Least Absolute Shrinkage and Selection Operator (LASSO) is used to tackle both the multicollinearity issue and the variable selection concurrently in the linear regression model.
Manickavasagar Kayanan +1 more
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Artifact for #468: TERA: Optimizing Stochastic Regression Tests in Machine Learning Projects [PDF]
, 2021 This is the artifact submission of paper #468: TERA: Optimizing Stochastic Regression Tests in Machine Learning ...
Aryaman Jain +3 more
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Modeling and Calibration for Some Stochastic Differential Models
Fractal and Fractional, 2022 In many scientific fields, the dynamics of the system are often known, and the main challenge is to estimate the parameters that model the behavior of the system.
Abdelmalik Moujahid, Fernando Vadillo
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Stochastic Tree Ensembles for Regularized Nonlinear Regression [PDF]
Journal of the American Statistical Association, 2021 This paper develops a novel stochastic tree ensemble method for nonlinear regression, which we refer to as XBART, short for Accelerated Bayesian Additive Regression Trees. By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques from recursive partitioning approaches, the new method ...
Jingyu He, P. Richard Hahn
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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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STOCHASTIC REGRESSION-MODEL WITH HETEROSCEDASTIC DISTURBANCE [PDF]
, 2011 [[abstract]]This paper discusses some properties of stochastic regression model with continuous form of heteroscedastic disturbance. The strong consistency and asymptotic normality of a generalized weighted least squares estimate will be investigated ...
CHANG DS;LIN GC
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FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction
Sensors, 2020 The implementation of neural network regression prediction based on digital circuits is one of the challenging problems in the field of machine learning and cognitive recognition, and it is also an effective way to relieve the pressure of the Internet in
Yunqi Gao +4 more
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