scholarly journals Histogram selection in non Gaussian regression

2009 ◽  
Vol 13 ◽  
pp. 70-86 ◽  
Author(s):  
Marie Sauvé
Keyword(s):  
Non Gaussian ◽  
Gaussian Regression

Quality-Related Locally Weighted Non-Gaussian Regression Based Soft Sensing for Multimode Processes

2018 ◽  
Vol 57 (51) ◽  
pp. 17452-17461 ◽  
Author(s):  
Yuchen He ◽  
Binbin Zhu ◽  
Chenyang Liu ◽  
Jiusun Zeng
Keyword(s):  
Soft Sensing ◽  
Non Gaussian ◽  
Gaussian Regression

scholarly journals Data augmentation for non-Gaussian regression models using variance-mean mixtures

Biometrika ◽  
2013 ◽  
Vol 100 (2) ◽  
pp. 459-471 ◽  
Author(s):  
N. G. Polson ◽  
J. G. Scott
Keyword(s):  
Regression Models ◽  
Data Augmentation ◽  
Non Gaussian ◽  
Gaussian Regression

ESTIMATING A NON-GAUSSIAN REGRESSION MODEL WITH MULTICOLLINEARITY

1989 ◽  
Vol 31 (1) ◽  
pp. 12-17 ◽  
Author(s):  
R. B. Cunningham And ◽  
C. R. Heathcote
Keyword(s):  
Regression Model ◽  
Non Gaussian ◽  
Gaussian Regression

scholarly journals Gaussian approximation of general non-parametric posterior distributions

2017 ◽  
Vol 7 (3) ◽  
pp. 509-529
Author(s):  
Zuofeng Shang ◽  
Guang Cheng
Keyword(s):  
Exponential Family ◽  
Approximation Theorem ◽  
Gaussian Approximation ◽  
Parametric Models ◽  
Smoothing Spline ◽  
Bayesian Procedures ◽  
Credible Intervals ◽  
Non Gaussian ◽  
Gaussian Regression ◽  
Non Parametric

AbstractIn a general class of Bayesian non-parametric models, we prove that the posterior distribution can be asymptotically approximated by a Gaussian process (GP). Our results apply to non-parametric exponential family that contains both Gaussian and non-Gaussian regression and also hold for both efficient (root-$n$) and inefficient (non-root-$n$) estimations. Our general approximation theorem does not rely on posterior conjugacy and can be verified in a class of GP priors that has a smoothing spline interpretation. In particular, the limiting posterior measure becomes prior free under a Bayesian version of ‘under-smoothing’ condition. Finally, we apply our approximation theorem to examine the asymptotic frequentist properties of Bayesian procedures such as credible regions and credible intervals.

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