Prolonged Neuromuscular Block After Rocuronium Administration in Laparoscopic Pyloromyotomy Patients: A Retrospective Bayesian Regression Analysis

2020 ◽  
Author(s):  
Laura E. Gilbertson ◽  
Michael C. Fiedorek ◽  
Christopher S. Fiedorek ◽  
Tuan A. Trinh ◽  
Humphrey Lam ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Neuromuscular Block ◽  
Bayesian Regression ◽  
Laparoscopic Pyloromyotomy

Bayesian and non-Bayesian regression analysis applied on wind speed data

2021 ◽  
Vol 13 (5) ◽  
pp. 053303
Author(s):  
Vincent Tanoe ◽  
Saul Henderson ◽  
Amir Shahirinia ◽  
Mohammad Tavakoli Bina
Keyword(s):  
Regression Analysis ◽  
Wind Speed ◽  
Bayesian Regression ◽  
Wind Speed Data

Bayesian regression analysis using poly-t densities

1977 ◽  
Vol 6 (3) ◽  
pp. 329-354 ◽  
Author(s):  
Jacques H. Drèze
Keyword(s):  
Regression Analysis ◽  
Bayesian Regression

Driving behavior at a roundabout: A hierarchical Bayesian regression analysis

2014 ◽  
Vol 26 ◽  
pp. 20-26 ◽  
Author(s):  
Abhisek Mudgal ◽  
Shauna Hallmark ◽  
Alicia Carriquiry ◽  
Konstantina Gkritza
Keyword(s):  
Regression Analysis ◽  
Driving Behavior ◽  
Bayesian Regression ◽  
Hierarchical Bayesian

scholarly journals Simulation Study for Penalized Bayesian Elastic Net Quantile Regression

2021 ◽  
Vol 26 (3) ◽  
Author(s):  
Muntadher Almusaedi ◽  
Ahmad Naeem Flaih
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Regression Model ◽  
Prior Distribution ◽  
Main Idea ◽  
Bayesian Regression ◽  
Elastic Net ◽  
Adaptive Lasso ◽  
Quantile Regression Model ◽  
Truncated Gamma Distribution

Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the Bayesian regression model, the variance of the estimators are reduced notable and the bias is getting smaller. The tradeoff between the bias and variance of the penalized Bayesian regression estimator consequently produce more interpretable model with more prediction accuracy. In this paper, we proposed new hierarchical model for the Bayesian quantile regression by employing the scale mixture of normals mixing with truncated gamma distribution that stated by (Li and Lin, 2010) as Laplace prior distribution. Therefore, new Gibbs sampling algorithms are introduced. A comparison has made with classical quantile regression model and with lasso quantile regression model by conducting simulations studies. Our model is comparable and gives better results.

scholarly journals Supplementary material to "Complex interactions of in-stream DOM and nutrient spiralling unravelled by Bayesian regression analysis"

2020 ◽  
Author(s):  
Matthias Pucher ◽  
Peter Flödl ◽  
Daniel Graeber ◽  
Klaus Felsenstein ◽  
Thomas Hein ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Bayesian Regression ◽  
Complex Interactions ◽  
Supplementary Material ◽  
Nutrient Spiralling

Improved weather indices based Bayesian regression model for forecasting crop yield

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 879-886
Author(s):  
M. YEASIN ◽  
K. N. SINGH ◽  
A. LAMA ◽  
B. GURUNG
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Crop Yield ◽  
Wheat Yield ◽  
Bayesian Framework ◽  
Bayesian Regression ◽  
Estimation Of Parameters ◽  
Yield Data ◽  
Conjugate Priors ◽  
Modern Era

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Bayesian framework. The Bayesian technique is one of the most powerful methodologies in the modern era of statistics. We have discussed different types of prior (informative, non-informative and conjugate priors). The Markov chain Monte Carlo (MCMC) methodology has been briefly discussed for the estimation of parameters under Bayesian framework. To illustrate these models, production data of banana, mango and wheat yield data are taken under consideration. We compared the traditional regression model with the Bayesian regression model and conclusively infer that the models estimated under Bayesian framework provided superior results as compared to the models estimated under the classical approach.

Robust Bayesian Regression Analysis with HPD-Regions

Compstat ◽  
1988 ◽  
pp. 349-354
Author(s):  
K. Felsenstein ◽  
K. Pötzelberger
Keyword(s):  
Regression Analysis ◽  
Bayesian Regression

scholarly journals Bayesian Regression Analysis with Examples in S-PLUS and R

2011 ◽  
Vol 10 (1) ◽  
pp. 268-276
Author(s):  
Sheikh P. Ahmad ◽  
A. A. Khan ◽  
A. Ahmed
Keyword(s):  
Regression Analysis ◽  
Bayesian Regression
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