scholarly journals A Review of the ‘BMS’ Package for R with Focus on Jointness

Econometrics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 6
Author(s):  
Shahram Amini ◽  
Christopher F. Parmeter
Keyword(s):  
Software Package ◽  
Bayesian Model ◽  
Alternative Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Simple Function ◽  
General Overview ◽  
Model Search ◽  
Wide Range

We provide a general overview of Bayesian model averaging (BMA) along with the concept of jointness. We then describe the relative merits and attractiveness of the newest BMA software package, BMS, available in the statistical language R to implement a BMA exercise. BMS provides the user a wide range of customizable priors for conducting a BMA exercise, provides ample graphs to visualize results, and offers several alternative model search mechanisms. We also provide an application of the BMS package to equity premia and describe a simple function that can easily ascertain jointness measures of covariates and integrates with the BMS package.

scholarly journals A Local Ensemble Prediction System for Fog and Low Clouds: Construction, Bayesian Model Averaging Calibration, and Validation

2008 ◽  
Vol 47 (12) ◽  
pp. 3072-3088 ◽  
Author(s):  
Stevie Roquelaure ◽  
Thierry Bergot
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Low Clouds ◽  
Wide Range ◽  
Ensemble Strategy ◽  
Low Visibility

Abstract At main international airports, air traffic safety and economic issues related to poor visibility conditions are crucial. Meteorologists face the challenge of supplying airport authorities with accurate forecasts of fog and cloud ceiling. These events are difficult to forecast because conditions evolve on short space and time scales during their life cycle. To obtain accurate forecasts of fog and low clouds, the Code de Brouillard à l’Echelle Locale (the local scale fog code)–Interactions between Soil, Biosphere, and Atmosphere (COBEL–ISBA) local numerical forecast system was implemented at Charles de Gaulle International Airport in Paris. However, even with dedicated observations and initialization, uncertainties remain in both initial conditions and mesoscale forcings. A local ensemble prediction system (LEPS) has been designed around the COBEL–ISBA numerical model and tested to assess the predictability of low visibility procedures events, defined as a visibility less than 600 m and/or a ceiling below 60 m. This work describes and evaluates a local ensemble strategy for the prediction of low visibility procedures. A Bayesian model averaging method has been applied to calibrate the ensemble. The study shows that the use of LEPS for specific local event prediction is well adapted and useful for low visibility prediction in the aeronautic context. Moreover, a wide range of users, especially those with low cost–loss ratios, can expect economic savings with the use of this probabilistic system.

Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging

2013 ◽  
Vol 141 (6) ◽  
pp. 2107-2119 ◽  
Author(s):  
J. McLean Sloughter ◽  
Tilmann Gneiting ◽  
Adrian E. Raftery
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Prediction Models ◽  
Weather Prediction ◽  
Model Averaging ◽  
Wind Vector ◽  
Bivariate Distributions ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Wide Range

Abstract Probabilistic forecasts of wind vectors are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Unlike other common forecasting problems, which deal with univariate quantities, statistical approaches to wind vector forecasting must be based on bivariate distributions. The prevailing paradigm in weather forecasting is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty can then be assessed through ensemble forecasts, where multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic predictions. Ensemble forecasts are often uncalibrated, however, and Bayesian model averaging (BMA) is a statistical way of postprocessing these forecast ensembles to create calibrated predictive probability density functions (PDFs). It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights reflect the forecasts’ relative contributions to predictive skill over a training period. In this paper the authors extend the BMA methodology to use bivariate distributions, enabling them to provide probabilistic forecasts of wind vectors. The BMA method is applied to 48-h-ahead forecasts of wind vectors over the North American Pacific Northwest in 2003 using the University of Washington mesoscale ensemble and is shown to provide better-calibrated probabilistic forecasts than the raw ensemble, which are also sharper than probabilistic forecasts derived from climatology.

MODEL SELECTION AND AVERAGING OF HEALTH COSTS IN EPISODE TREATMENT GROUPS

2016 ◽  
Vol 47 (1) ◽  
pp. 153-167 ◽  
Author(s):  
Shujuan Huang ◽  
Brian Hartman ◽  
Vytaras Brazauskas
Keyword(s):  
Random Forest ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Cost Model ◽  
Model Averaging ◽  
Large Data ◽  
Data Set ◽  
Treatment Groups ◽  
Wide Range

Episode Treatment Groups (ETGs) classify related services into medically relevant and distinct units describing an episode of care. Proper model selection for those ETG-based costs is essential to adequately price and manage health insurance risks. The optimal claim cost model (or model probabilities) can vary depending on the disease. We compare four potential models (lognormal, gamma, log-skew-t and Lomax) using four different model selection methods (AIC and BIC weights, Random Forest feature classification and Bayesian model averaging) on 320 ETGs. Using the data from a major health insurer, which consists of more than 33 million observations from 9 million claimants, we compare the various methods on both speed and precision, and also examine the wide range of selected models for the different ETGs. Several case studies are provided for illustration. It is found that Random Forest feature selection is computationally efficient and sufficiently accurate, hence being preferred in this large data set. When feasible (on smaller data sets), Bayesian model averaging is preferred because of the posterior model probabilities.

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.

Sign in / Sign up

Export Citation Format

Share Document