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 prevail...

This paper focuses on the problem of variable selection in linear regression models. I briefly review the method of Bayesian model averaging, which has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. Some of the literature is discussed with particular emphasis on forecasting in economics. The role of the p...

Abstract. Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited. It is common for consumers of these methods to develop their own code, which has obvious appeal. However, canned statistical software can ameliorate one’s own a...

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way,...

Accurate groundwater level (GWL) prediction is essential for the sustainable management of resources. However, GWLs remains a challenge due to insufficient data and complicated hydrogeological system. In this study, we investigated ability Gravity Recovery Climate Experiment (GRACE) satellite data, Global Land Evaporation Amsterdam Model (GLEAM) Data Assimilation System (GLDAS) publicly availab...

Predictions of disease outcome in prognostic factor models are usually based on one selected model. However, often several models fit the data equally well, but these models might differ substantially in terms of included explanatory variables and might lead to different predictions for individual patients. For survival data we discuss two approaches for accounting for model selection uncertain...

Parameter estimation is often considered as a post model selection problem, i.e., the parameters of interest are estimated based on “the best” model. However, this approach does not take into account that was selected from set possible models. Ignoring uncertainty may lead to bias in estimation. In paper, we present Bayesian variable (BVS) for averaging which would address uncertainty. Although...

Abstract Inflow forecast plays an indispensable role in reservoir operation. Accuracy and effectiveness of model prediction play a decisive it. In this paper, the certainty coefficient, root mean square error (RMSE), absolute deviation (MAE) Nash-Suthcliffe coefficient (NSE) are used to consider effect Soil Water Assessment Tool (SWAT) Xin'anjiang (XAJ) on inflow Jinxi Reservoir. Results indica...