scholarly journals Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?

2011 ◽  
Author(s):  
Chew Lian Chua ◽  
Sandy Suardi ◽  
Sarantis Tsiaplias
Keyword(s):  
Interest Rates ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Short Term

Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method

2009 ◽  
Author(s):  
Gong Li ◽  
Jing Shi ◽  
Junyi Zhou
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Energy ◽  
Statistical Models ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Short Term ◽  
Goodness Of Fit Test ◽  
Wind Speed Forecasting

Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

Bayesian Model Averaging of Naive Bayes for Clustering

2006 ◽  
Vol 36 (5) ◽  
pp. 1149-1161 ◽  
Author(s):  
G. Santafe ◽  
J.A. Lozano ◽  
P. Larranaga
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Naive Bayes ◽  
Model Averaging ◽  
Naïve Bayes
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