Bayesian Model Averaging Approach Research Articles

Bayesian mixture model averaging for identifying the different gene expressions of chickpea (Cicer arietinum) plant tissue

ABSTRACTIdentification of different gene expressions of chickpea (Cicer arietinum) plant tissue is needed in order to develop new varieties of chickpea plant which is resistant to disease through the insertion of genes. This plant is the third legume plant of the Leguminosae (Fabaceae) family and is much needed in the world due to its high-protein seeds and roots that contain symbiotic nitrogen-fixing bacteria. This paper has succeeded to demonstrate the work of Bayesian mixture model averaging (BMMA) approach to identify the different gene expressions of chickpea plant tissue in Indonesia. The results show that the best BMMA normal models contain from 727 (73%) up to 939 (94%) models from 1,000 generated mixture normal models. The fitted BMMA models to gene expression differences data on average is 0.2878511 for Kolmogorov–Smirnov (KS) and 0.1278080 for continuous rank probability score (CRPS). Based on these BMMA models, there are three groups of gene IDs: downregulated, regulated, and upregulated. The results of this grouping can be useful to find new varieties of chickpea plants that are more resistant to disease. The BMMA normal models coupled with Occam's window as a data-driven modeling have succeed to demonstrate the work of building the gene expression differences microarray experiments data.

Firm Default Prediction: A Bayesian Model-Averaging Approach

I develop a new predictive approach using Bayesian model averaging to account for incomplete knowledge of the true model behind corporate default and bankruptcy filing. I find that uncertainty over the correct model is empirically large, with far fewer variables being significant predictors of default compared with conventional approaches. Only the ratio of total liabilities to total assets and the volatility of market returns are robust default predictors in the overall sample and individual industry groups. Model-averaged forecasts that aggregate information across models or allow for industry-specific effects substantially outperform individual models.

Asset Returns Under Model Uncertainty: Evidence from the Euro Area, the US and the UK

We analyze predictability of risk premium in the context of model uncertainty. Using data for the euro area, the US and the UK, we show that there is a large amount of model uncertainty and one can improve the forecasts of stock returns with a Bayesian Model Averaging (BMA) approach. The empirical evidence for the euro area suggests that several macroeconomic, financial and macro-financial variables are consistently among the most prominent determinants of risk premium. As for the US, only a few number of predictors play an important role. In the case of the UK, future stock returns are better forecasted by financial variables. These results are corroborated for both the M-open and the M-closed perspectives, different model priors and in the context of “in-sample” and “out-of-sample” forecasting. Finally, we highlight that the predictive ability of the BMA framework is stronger at longer periods, and clearly outperforms the constant expected returns and the autoregressive benchmark models.

English

This study tries to tackle the tourism forecasting problem using online search queries. This recent-developed methodology is subject to several criticisms, one of which is how to choose satisfying search queries to be built in the forecasting model. This study compares two popular candidates, which are the Bayesian Model Averaging (BMA) approach and the Least Absolute Shrinkage and Selector Operator (Lasso) approach. Evidence shows that the two approaches produce similar forecasting performance but different query selection results. Key words: Tourism forecasting, query selection, Bayesian model averaging, Lasso, Baidu query data.

FACTOR MODELS AND TIME‐VARYING PARAMETER FRAMEWORK FOR FORECASTING EXCHANGE RATES AND INFLATION: A SURVEY

AbstractA survey of models used for forecasting exchange rates and inflation reveals that the factor‐based and time‐varying parameter or state space models generate superior forecasts relative to all other models. This survey also finds that models based on Taylor rule and portfolio balance theory have moderate predictive power for forecasting exchange rates. The evidence on the use of Bayesian Model Averaging approach in forecasting exchange rates reveals limited predictive power, but strong support for forecasting inflation. Overall, the evidence overwhelmingly points to the context of the forecasts, relevance of the historical data, data transformation, choice of the benchmark, selected time horizons, sample period and forecast evaluation methods as the crucial elements in selecting forecasting models for exchange rate and inflation.

TFP Growth and Commodity Prices in Emerging Economies

In this paper we aim at empirically testing cross-country impacts of commodity price shocks to aggregate TFP growth for a sample of emerging economies. Under a growth accounting framework, we estimate country-specific TFP growth (1992-2014) and select the attendant robust determinants by means of a Bayesian Model Averaging (BMA) approach. To identify the effects of structural shocks, we propose a Bayesian panel VAR model and calculate cyclically adjusted TFP growth net of demand shocks (i.e. output gap) and commodity prices. Our results suggest that: i) the relationship of commodity prices to TFP growth has been very high in small commodity-exporting economies (i.e. an increase of 10% in commodity prices is associated with a sizable expansion of TFP growth in a year for an average commodity exporter); ii) although our evidence is not sufficient to empirically distinguish among theoretical explanations, our results favour an interpretation that weights short-term effects of commodity prices on productivity, either through transitional dynamics to the manufacturing sector or through mismeasurement of TFP; and iii) cyclically adjusted TFP growth highlights the importance of negative supply shocks in commodity-exporting countries. All in all, much of the increase in TFP growth in the last decade was related to a favourable cyclical environment, a result with potentially significant policy implications for commodity-dependent economies.

On predicting the semiconductor industry cycle: a Bayesian model averaging approach

This study considers the model uncertainty and utilizes the Bayesian model averaging (BMA) approach to identify useful predictors of the semiconductor industry cycle from a list of 70 potential predictors. The posterior inclusion probabilities, posterior means, and posterior standard deviations over the period of 1995:05–2012:10 are estimated and consequently used to identify the main determinants of the industry cycle. It is found that the Philadelphia Semiconductor Index and total inventories in various downstream industries have important roles in signaling the industry growth. The results from an out-of-sample forecasting exercise also reveal the predictive potential and usefulness of BMA for the long-term prediction.

Bayesian analysis of periodic unit roots in the presence of a break

ABSTRACTA Bayesian testing approach for a periodic unit root in quarterly and monthly data is presented. Further a Bayesian test is introduced to test for unit roots at (non)seasonal spectral frequencies. All procedures admit one structural break in the periodic trend function, where the occurrence of a break and the associated timing are treated as additional model parameters. A Bayesian model averaging (BMA) approach is proposed and power functions of the tests are computed. Overall the results indicate that the BMA periodic unit root test exhibits favourable test properties even in small samples. In an empirical application the presented testing procedures are used to test for (non)seasonal forms of unemployment persistence among OECD countries.

Bayesian model averaging and principal component regression forecasts in a data rich environment

This study revisits the accuracy of the point and density forecasts of monthly US inflation and output growth that are generated using principal components regression (PCR) and Bayesian model averaging (BMA). I run a forecasting horse race between 24 BMA specifications and two PCR alternatives in an out-of-sample, 10-year rolling event evaluation. The differences in mean-square forecast errors between BMA and PCR are mostly insignificant but predictable. PCR methods perform best for predicting deviations of output and inflation from their expected paths, whereas BMA methods perform best for predicting “tail” events. This dichotomy implies that risk-neutral policy-makers may prefer the classical PCR approach, while the BMA approach would belong in the toolkit of a prudential, risk-averse forecaster.

MP54-14 OPTIMAL SKIN-TO-STONE DISTANCE IS A POSITIVE PREDICTOR FOR SUCCESSFUL OUTCOMES IN UPPER URETER CALCULI FOLLOWING EXTRACORPOREAL SHOCK WAVE LITHOTRIPSY: A BAYESIAN MODEL AVERAGING APPROACH

You have accessJournal of UrologyStone Disease: Shock Wave Lithotripsy1 Apr 2016MP54-14 OPTIMAL SKIN-TO-STONE DISTANCE IS A POSITIVE PREDICTOR FOR SUCCESSFUL OUTCOMES IN UPPER URETER CALCULI FOLLOWING EXTRACORPOREAL SHOCK WAVE LITHOTRIPSY: A BAYESIAN MODEL AVERAGING APPROACH Dong Hyuk Kang, Kang Su Cho, Hae Do Jung, Doo Yong Chung, Jong Kyou Kwon, Jae Hung Jung, Seung Hwan Lee, Won Sik Ham, Young Deuk Choi, and Joo Yong Lee Dong Hyuk KangDong Hyuk Kang More articles by this author , Kang Su ChoKang Su Cho More articles by this author , Hae Do JungHae Do Jung More articles by this author , Doo Yong ChungDoo Yong Chung More articles by this author , Jong Kyou KwonJong Kyou Kwon More articles by this author , Jae Hung JungJae Hung Jung More articles by this author , Seung Hwan LeeSeung Hwan Lee More articles by this author , Won Sik HamWon Sik Ham More articles by this author , Young Deuk ChoiYoung Deuk Choi More articles by this author , and Joo Yong LeeJoo Yong Lee More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.584AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The skin-to-stone distance (SSD), a predictor of extracorporeal shock wave lithotripsy (ESWL) success, remains controversial in patients with ureter stones. Until recently, SSD was a significant factor in half of all published studies. However, in the remaining studies, there was no significant difference in SSD for success or stone-free rate after SWL. Thus, we investigated why SSD was not a predicting factor for successful outcome following ESWL in previous studies. We also determined the optimal SSD, which can be used as a new positive predictor for successful ESWL outcomes in patients with upper ureter stones. METHODS We retrospectively reviewed the medical records of 1,519 patients who underwent their first ESWL between January 2005 and December 2013. Among these patients, 492 had upper ureter stones that measured 4–20 mm and were eligible for our analyses. Maximal stone length (MSL), Hounsfield units (HU), and SSD were determined on pretreatment non-contrast computed tomography (NCCT). For subgroup analyses, patients were divided into four groups. Group 1 consisted of patients with SSD<25th percentile, group 2 consisted of patients with SSD in the 25th to 50th percentile, group 3 patients had SSD in the 50th to 75th percentile, and group 4 patients had SSD≥75th percentile. RESULTS In analyses of group 2 patients versus others, there were no statistical differences in mean age, MSL, or HU (Table 1). However, the one session success rate in group 2 was higher than other groups (77.9% vs. 67.0%; P=0.032). The multivariate logistic regression model revealed that shorter MSL, lower HU, and the group 2 SSD were positive predictors for successful outcomes in ESWL (Table 2). Using the Bayesian model-averaging approach, longer MSL, lower HU, and group 2 SSD can be also positive predictors for successful outcomes following ESWL. CONCLUSIONS Our data indicate that a group 2 SSD of approximately 10 cm is a positive predictor for success following ESWL. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e731-e732 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Dong Hyuk Kang More articles by this author Kang Su Cho More articles by this author Hae Do Jung More articles by this author Doo Yong Chung More articles by this author Jong Kyou Kwon More articles by this author Jae Hung Jung More articles by this author Seung Hwan Lee More articles by this author Won Sik Ham More articles by this author Young Deuk Choi More articles by this author Joo Yong Lee More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Bayesian model averaging for the prediction of water main failure for small to large Canadian municipalities

Water utilities often rely on water main failure prediction models for developing preventive or proactive repair and replacement action programs. Due to inherent uncertainties in modeling, it is challenging to understand the water main failure processes and to predict the failure effectively. In this study, Bayesian model averaging (BMA) method is presented to identify the influential covariates and to predict the failure rates of water mains considering model uncertainties. To accredit the proposed model, it is implemented to predict the failure of pipes of the water distribution network of the City of Kelowna, BC and Greater Vernon Water, BC, Canada. Results indicate that the proposed BMA approach captures the effect of the potential explanatory variables more effectively through the posterior probabilities in contrast to that of the p-value given by the classical regression analysis. Moreover, BMA approach performs better compared to classical regression analysis when limited pipe failure data are available.

A BMA Analysis to Assess the Urbanization and Climate Change Impact on Urban Watershed Runoff

A reliable planning of urban drainage systems aimed at the mitigation of flooding, should take into account the possible change over time of impervious cover in the urban watershed and of the climate features. The present study proposes a methodology to analyze the changing in runoff response for a urban watershed accounting several plausible future states of new urbanization and climate. To this aim, several models simulating the evolution scenario of impervious watershed area and of climate change were adopted. However, it is known that an evolution scenario represents only one of all possible occurrence and it is not necessary the true future state, therefore it is needed to find the plausible forecast of the future state by taking into account and combining several possible evolution models. According to this aim, in the present study the Bayesian Model Averaging (BMA) approach was applied to several evolution models for climate variables. The Bayesian Model Averaging is a statistic multi-model method that computes a weighted average of the series of available competing models forecast overcoming the problem of arbitrary selecting of single best model and, consequently, the relative requirements of uncertainty analysis. The weighted average is the probability density function (pdf) of the quantity to be forecasted, while the weights correspond to the comparative performance of the models over training period of observation. After the application of BMA, for a given probability, the impervious area extension and the design rainfall event were identified and used as input data for a numerical model based on the SWMM software which was adopted to simulate the behavior of the urban drainage-system adopted as case study. Particularly, the proposed procedure was applied with reference to the Sicilian climate regions (southern Italy).

Optimal Skin-to-Stone Distance Is a Positive Predictor for Successful Outcomes in Upper Ureter Calculi following Extracorporeal Shock Wave Lithotripsy: A Bayesian Model Averaging Approach.

ObjectivesTo investigate whether skin-to-stone distance (SSD), which remains controversial in patients with ureter stones, can be a predicting factor for one session success following extracorporeal shock wave lithotripsy (ESWL) in patients with upper ureter stones.Patients and MethodsWe retrospectively reviewed the medical records of 1,519 patients who underwent their first ESWL between January 2005 and December 2013. Among these patients, 492 had upper ureter stones that measured 4–20 mm and were eligible for our analyses. Maximal stone length, mean stone density (HU), and SSD were determined on pretreatment non-contrast computed tomography (NCCT). For subgroup analyses, patients were divided into four groups. Group 1 consisted of patients with SSD<25th percentile, group 2 consisted of patients with SSD in the 25th to 50th percentile, group 3 patients had SSD in the 50th to 75th percentile, and group 4 patients had SSD≥75th percentile.ResultsIn analyses of group 2 patients versus others, there were no statistical differences in mean age, stone length and density. However, the one session success rate in group 2 was higher than other groups (77.9% vs. 67.0%; P = 0.032). The multivariate logistic regression model revealed that shorter stone length, lower stone density, and the group 2 SSD were positive predictors for successful outcomes in ESWL. Using the Bayesian model-averaging approach, longer stone length, lower stone density, and group 2 SSD can be also positive predictors for successful outcomes following ESWL.ConclusionsOur data indicate that a group 2 SSD of approximately 10 cm is a positive predictor for success following ESWL.

Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling

Flood information, especially extreme flood, is necessary for any large hydraulic structure design and flood risk management. Flood mitigation also requires a comprehensive assessment of flood risk and an explicit quantification of the flood uncertainty. In the present study, we use a multimodel ensemble approach based on Bayesian model averaging (BMA) method to account for model structure and distribution uncertainties. The usefulness of this approach is assessed by a case study over the Willamette River Basin (WRB) in Pacific Northwest, USA. Besides the standard log-Pearson Type III distribution, we also identified that the generalized extreme value and three-parameter lognormal distributions were both potential distributions in WRB. Three different statistical models, including the Bulletin-17B quantile model, index-flood model, and spatial Bayesian hierarchical model, were considered in the study. The BMA method is then used to assign weights to different models, where better performing model receives higher weights. It was found that the major uncertainty in extreme flood prediction is contributed by model structure, while the choice of distribution plays a lesser important role in quantification of flood uncertainty. The BMA approach provides a more robust extreme flood prediction than any single model.

Competition and the growth of nations: International evidence from Bayesian model averaging

I apply Bayesian model averaging (BMA) and complementary approaches to international data for the time span 1988–2007 in order to find out whether competition in the economic and political arenas is a robust determinant of aggregate growth, and whether there exists jointness among competition variables versus other growth determinants. Several indicators of competition are used, and I control for a range of other factors potentially affecting growth. My results provide some support for the importance and positive impact on growth of financial market competition. No other competition area emerges as robust growth driver. The jointness analysis suggests a moderate degree of complementarity between financial market competition and important growth determinants, implying manageable policy complexity.

Bayesian Model Averaging with Stratified Sampling for Probabilistic Quantitative Precipitation Forecasting in Northern China during Summer 2010

Abstract To study the impact of training sample heterogeneity on the performance of Bayesian model averaging (BMA), two BMA experiments were performed on probabilistic quantitative precipitation forecasts (PQPFs) in the northern China region in July and August of 2010 generated from an 11-member short-range ensemble forecasting system. One experiment, as in many conventional BMA studies, used an overall training sample that consisted of all available cases in the training period, while the second experiment used stratified sampling BMA by first dividing all available training cases into subsamples according to their ensemble spread, and then performing BMA on each subsample. The results showed that ensemble spread is a good criterion to divide ensemble precipitation cases into subsamples, and that the subsamples have different statistical properties. Pooling the subsamples together forms a heterogeneous overall sample. Conventional BMA is incapable of interpreting heterogeneous samples, and produces unreliable PQPF. It underestimates the forecast probability at high-threshold PQPF and local rainfall maxima in BMA percentile forecasts. BMA with stratified sampling according to ensemble spread overcomes the problem reasonably well, producing sharper predictive probability density functions and BMA percentile forecasts, and more reliable PQPF than the conventional BMA approach. The continuous ranked probability scores, Brier skill scores, and reliability diagrams of the two BMA experiments were examined for all available forecast days, along with a logistic regression experiment. Stratified sampling BMA outperformed the raw ensemble and conventional BMA in all verifications, and also showed better skill than logistic regression in low-threshold forecasts.

Does improved SSTA prediction ensure better seasonal rainfall forecasts?

AbstractSeasonal rainfall forecasts in Australia are issued based on concurrent sea surface temperature anomalies (SSTAs) using a Bayesian model averaging (BMA) approach. The SSTA fields are derived from the Predictive Ocean‐Atmosphere Model for Australia (POAMA) initialized in the preceding season. This study investigates the merits of the rainfall forecasted using POAMA SSTAs in contrast to that forecasted using a multimodel combination of SSTAs derived using five existing models. In addition, seasonal rainfall forecasts derived from multimodel and POAMA SSTA fields are subsequently combined to obtain a single weighted forecast over Australia. These three forecasts are compared against “idealized” forecasts where observed SSTAs are used instead of those predicted. The results indicate that while seasonal rainfall forecasts derived using multimodel‐based SSTA indices offer improvements in selected seasons over a majority of grid cells in comparison to the case where a single SSTA model is used in two seasons, these improvements are not as significant as the improvements in the SSTA field that drive the rainfall forecasting model. The forecasts derived from the combination of multimodel and POAMA SSTA indices forecasts are found to offer greater improvements over the multimodel or the POAMA forecasts for a majority of grid cells in all seasons. It is also observed that these combined forecasts are touching the upper limits of forecastability, which are reached when observed SSTAs are used to forecast the rainfall. This suggests that further improvements in rainfall forecasting are only possible through the use of an improved forecasting algorithm, and not the driver (SSTA) information used in the current study.

Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation.

Brain areas within the motor system interact directly or indirectly during motor-imagery and motor-execution tasks. These interactions and their functionality can change following stroke and recovery. How brain network interactions reorganize and recover their functionality during recovery and treatment following stroke are not well understood. To contribute to answering these questions, we recorded blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals from 10 stroke survivors and evaluated dynamical causal modeling (DCM)-based effective connectivity among three motor areas: primary motor cortex (M1), pre-motor cortex (PMC) and supplementary motor area (SMA), during motor-imagery and motor-execution tasks. We compared the connectivity between affected and unaffected hemispheres before and after mental practice and combined mental practice and physical therapy as treatments. The treatment (intervention) period varied in length between 14 to 51 days but all patients received the same dose of 60 h of treatment. Using Bayesian model selection (BMS) approach in the DCM approach, we found that, after intervention, the same network dominated during motor-imagery and motor-execution tasks but modulatory parameters suggested a suppressive influence of SM A on M1 during the motor-imagery task whereas the influence of SM A on M1 was unrestricted during the motor-execution task. We found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere. Using Bayesian model averaging (BMA) approach, we found that the intervention improved the regional connectivity among the motor areas during both the tasks. The connectivity between PMC and M1 was stronger in motor-imagery tasks whereas the connectivity from PMC to M1, SM A to M1 dominated in motor-execution tasks. There was significant behavioral improvement (p = 0.001) in sensation and motor movements because of the intervention as reflected by behavioral Fugl-Meyer (FMA) measures, which were significantly correlated (p = 0.05) with a subset of connectivity. These findings suggest that PMC and M1 play a crucial role during motor-imagery as well as during motor-execution task. In addition, M1 causes more exchange of causal information among motor areas during a motor-execution task than during a motor-imagery task due to its interaction with SM A. This study expands our understanding of motor network involved during two different tasks, which are commonly used during rehabilitation following stroke. A clear understanding of the effective connectivity networks leads to a better treatment in helping stroke survivors regain motor ability.

Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging

Ensembles of forecasts are typically employed to account for the forecast uncertainties inherent in predictions of future weather states. However, biases and dispersion errors often present in forecast ensembles require statistical post‐processing. Univariate post‐processing models such as Bayesian model averaging (BMA) have been successfully applied for various weather quantities. Nonetheless, BMA and many other standard post‐processing procedures are designed for a single weather variable, thus ignoring possible dependencies among weather quantities. In line with recently upcoming research to develop multivariate post‐processing procedures, for example, BMA for bivariate wind vectors, or flexible procedures applicable for multiple weather quantities of different types, a bivariate BMA model for joint calibration of wind speed and temperature forecasts is proposed on the basis of the bivariate truncated normal distribution. It extends the univariate truncated normal BMA model designed for post‐processing ensemble forecast of wind speed by adding a normally distributed temperature component with a covariance structure representing the dependency among the two weather quantities.The method is applied to wind speed and temperature forecasts of the eight‐member University of Washington mesoscale ensemble and of the 11‐member Aire Limitée Adaptation dynamique Développement International‐Hungary Ensemble Prediction System (ALADIN‐HUNEPS) ensemble of the Hungarian Meteorological Service, and its predictive performance is compared to that of the independent BMA calibration of these weather quantities and the general Gaussian copula method. The results indicate improved calibration of probability and accuracy of point forecasts in comparison to the raw ensemble and the independent BMA approach, and the overall performance of this bivariate model is able to keep up with that of the Gaussian copula method. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Determinants of investment under incentive regulation: The case of the Norwegian electricity distribution networks

Investment in electricity networks, as regulated natural monopolies, is among the highest regulatory and energy policy priorities. The electricity sector regulators adopt different incentive mechanisms to ensure that the firms undertake sufficient investment to maintain and modernise the grid. Thus, an effective regulatory treatment of investment requires understanding the response of companies to the regulatory incentives. This study analyses the determinants of investment in electricity distribution networks using a panel dataset of 129 Norwegian companies observed from 2004 to 2010. A Bayesian Model Averaging approach is used to provide a robust statistical inference by taking into account the uncertainties around model selection and estimation. The results show that three factors drive nearly all network investments: investment rate in previous period, socio-economic costs of energy not supplied and finally useful life of assets. The results indicate that Norwegian companies have, to some degree, responded to the investment incentives provided by the regulatory framework. However, some of the incentives do not appear to be effective in driving the investments.