Semiparametric model averaging prediction: a Bayesian approach

2018 ◽  
Vol 60 (4) ◽  
pp. 407-422
Author(s):  
Jingli Wang ◽  
Jialiang Li
Keyword(s):  
Bayesian Approach ◽  
Model Averaging ◽  
Semiparametric Model

scholarly journals Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach

2019 ◽  
Vol 51 (02) ◽  
pp. 249-266
Author(s):  
Nicholas D. Payne ◽  
Berna Karali ◽  
Jeffrey H. Dorfman
Keyword(s):  
Bayesian Approach ◽  
Model Uncertainty ◽  
Bayesian Model Averaging ◽  
Model Performance ◽  
Model Averaging ◽  
Forecasting Model ◽  
Forecast Errors ◽  
Forecast Horizon ◽  
Combining Forecasts ◽  
The Bayesian Approach

AbstractBasis forecasting is important for producers and consumers of agricultural commodities in their risk management decisions. However, the best performing forecasting model found in previous studies varies substantially. Given this inconsistency, we take a Bayesian approach, which addresses model uncertainty by combining forecasts from different models. Results show model performance differs by location and forecast horizon, but the forecast from the Bayesian approach often performs favorably. In some cases, however, the simple moving averages have lower forecast errors. Besides the nearby basis, we also examine basis in a specific month and find that regression-based models outperform others in longer horizons.

scholarly journals Semiparametric model averaging of ultra-high dimensional time series

2015 ◽  
Author(s):  
Oliver Linton ◽  
Zudi Lu ◽  
Degui Li ◽  
Jia Chen
Keyword(s):  
Time Series ◽  
Model Averaging ◽  
Semiparametric Model ◽  
High Dimensional

Semiparametric model averaging prediction for lifetime data via hazards regression

2021 ◽  
Author(s):  
Jialiang Li ◽  
Tonghui Yu ◽  
Jing Lv ◽  
Mei‐Ling Ting Lee
Keyword(s):  
Model Averaging ◽  
Semiparametric Model ◽  
Lifetime Data

scholarly journals A Bayesian Approach to the Simulation Argument

Universe ◽  
2020 ◽  
Vol 6 (8) ◽  
pp. 109 ◽  
Author(s):  
David Kipping
Keyword(s):  
Computer Simulation ◽  
Bayesian Approach ◽  
Infinite Number ◽  
Model Uncertainty ◽  
Bayesian Model ◽  
High Probability ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
State Spaces ◽  
The One

The Simulation Argument posed by Bostrom suggests that we may be living inside a sophisticated computer simulation. If posthuman civilizations eventually have both the capability and desire to generate such Bostrom-like simulations, then the number of simulated realities would greatly exceed the one base reality, ostensibly indicating a high probability that we do not live in said base reality. In this work, it is argued that since the hypothesis that such simulations are technically possible remains unproven, statistical calculations need to consider not just the number of state spaces, but the intrinsic model uncertainty. This is achievable through a Bayesian treatment of the problem, which is presented here. Using Bayesian model averaging, it is shown that the probability that we are sims is in fact less than 50%, tending towards that value in the limit of an infinite number of simulations. This result is broadly indifferent as to whether one conditions upon the fact that humanity has not yet birthed such simulations, or ignore it. As argued elsewhere, it is found that if humanity does start producing such simulations, then this would radically shift the odds and make it very probably we are in fact simulated.

Semiparametric model averaging prediction for dichotomous response

2020 ◽  
Author(s):  
Fang Fang ◽  
Jialiang Li ◽  
Xiaochao Xia
Keyword(s):  
Model Averaging ◽  
Semiparametric Model

Integrated treatment of model and parameter uncertainties through a Bayesian approach

2012 ◽  
Vol 227 (1) ◽  
pp. 41-54 ◽  
Author(s):  
Enrique López Droguett ◽  
Ali Mosleh
Keyword(s):  
Bayesian Approach ◽  
Model Uncertainty ◽  
Integrated Assessment ◽  
Model Averaging ◽  
Uncertainty Assessment ◽  
Integrated Treatment ◽  
Parameter Uncertainties ◽  
Bayesian Approaches ◽  
Uncertainty Factor ◽  
Special Cases

Bayesian and non-Bayesian approaches have been proposed for treating model uncertainty; in general, model and parameter uncertainties have been tackled as separate domains. This article discusses a Bayesian framework for an integrated assessment of model and parameter uncertainties. The approach accommodates cases involving multiple dependent models, and we also demonstrate that under certain conditions, the model uncertainty assessment approaches known as model averaging and uncertainty-factor are special cases of the proposed formulation. These features are also demonstrated by means of a few examples of interest in the risk and safety domain.

scholarly journals An Assessment of Bayesian Model-Averaged Logistic Regression for Intensive-Care Prognosis

2020 ◽  
Author(s):  
Richard Dybowski
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Bayesian Approach ◽  
Bayesian Model ◽  
Regression Coefficient ◽  
Model Averaging ◽  
Regression Coefficients ◽  
Prognostic Models ◽  
Improve Performance ◽  
Theoretical Advantage

SUMMARYLogistic regression is the standard method for developing prognostic models for intensive care, but this approach does not take into account the uncertainty in the model selected and the uncertainty in its regression coefficients. This weakness can be addressed by adopting a Bayesian model-averaged approach to logistic regression; however, with respect to the dataset used for our study, we found maximum likelihood to be as effective as the more elaborate Bayesian approach, and an implementation of model averaging did not improve performance. Nevertheless, the Bayesian approach has the theoretical advantage that it can exploit prior knowledge about regression coefficient and model probabilities.

AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories

2020 ◽  
pp. 1-15
Author(s):  
Jialiang Li ◽  
Jing Lv ◽  
Alan T. K. Wan ◽  
Jun Liao
Keyword(s):  
Model Averaging ◽  
Semiparametric Model ◽  
Multiple Categories
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