Bottom-up estimation and top-down prediction: Solar energy prediction combining information from multiple sources

Accurately forecasting solar power using the data from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements from an automated monitoring network so as to better predict solar power in a timely manner. To this end, we propose a new approach of analyzing large-scale multilevel models with great computational efficiency requiring minimum monitoring and intervention. This approach features a division of the large scale data set into smaller ones with manageable sizes, based on their physical locations, and fit a local model in each area. The local model estimates are then combined sequentially from the specified multilevel models using our novel bottom-up approach for parameter estimation. The prediction, on the other hand, is implemented in a top-down matter. The proposed method is applied to the solar energy prediction problem for the U.S. Department of Energy’s SunShot Initiative

Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

Understanding the oscillating behaviors that govern organisms' internal biological processes requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Harmonizing the two types of experiment, however, poses significant statistical challenges due to identifiability issues, numerical instability, and ill behavior in high dimension. This article devises a new Bayesian calibration framework for oscillating biochemical models. The proposed Bayesian model is estimated relying on an advanced Markov chain Monte Carlo (MCMC) technique which can efficiently infer the parameter values that match the simulated and observed oscillatory processes. Also proposed is an approach to sensitivity analysis based on the intervention posterior. This approach measures the influence of individual parameters on the target process by using the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous fungus, Neurospora crassa.Comment: manuscript 33 pages, appendix 6 page

Soluble neprilysin and long-term clinical outcomes in patients with coronary artery disease undergoing percutaneous coronary intervention: a retrospective cohort study

Background: Neprilysin has an essential role in regulating fluid balance and vascular resistance, and neprilysin inhibitors have shown beneficial effects in patients with heart failure. However, the potential predictive value of neprilysin levels as a biomarker for cardiovascular risk remains unclear. The aim of this study was to assess the prognostic value of soluble neprilysin (sNEP) levels in patients with ischemic heart disease. Methods: Neprilysin levels were measured in 694 consecutive patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI). These patients were classified into two groups according to their serum levels of neprilysin and categorized into the lower neprilysin group (n = 348) and the higher neprilysin group (n = 346). The primary clinical endpoint was all-cause mortality, and the secondary endpoint was a composite of major adverse cardiac events (MACE). Results: The median sNEP level was 76.0 pg/ml. The median sNEP levels were higher in patients with left ventricular ejection fraction (LVEF) ≥40% (77.6 pg/ml, interquartile range 46.6–141.3) than in those with LVEF \u3c 40% (70.0 pg/ml, interquartile range 47.1–100.6; P = 0.032). Among all patients, each clinical outcome and MACE did not differ significantly according to the groups divided into median, tertile, or quartile of sNEP levels during a median follow-up of 28.4 months. We did not find a significant relationship between sNEP levels and clinical outcomes in multivariate Cox regression analysis. Among patients with LVEF \u3c 40%, an increased sNEP level was associated with a higher rate of all-cause death (adjusted hazard ratio 2.630, 95% confidence interval 1.049–6.595, P = 0.039). Conclusion: Serum sNEP levels are not associated with long-term mortality or cardiovascular outcomes after PCI in patients with CAD. In the LVEF \u3c 40% group, increased sNEP levels may be associated with a higher risk of all-cause death

Ischemic and Bleeding Events Associated with Thrombocytopenia and Thrombocytosis after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction

The early and late ischemic and bleeding clinical outcomes according to baseline platelet count after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) remain unclear. Overall, 10,667 patients from the Cardiovascular Risk and identification of potential high-risk population in AMI (COREA-AMI) I and II registries were classified according to the following universal criteria on baseline platelet counts: (1) moderate to severe thrombocytopenia (platelet \u3c 100 K/µL, n = 101), (2) mild thrombocytopenia (platelet = 100~149 K/µL, n = 631), (3) normal reference (platelet = 150~450 K/µL, n = 9832), and (4) thrombocytosis (platelet \u3e 450 K/µL, n = 103). The primary endpoint was the occurrence of major adverse cardiovascular events (MACE). The secondary outcome was Bleeding Academic Research Consortium (BARC) 2, 3, and 5 bleeding. After adjusting for confounders, the moderate to severe thrombocytopenia (HR, 2.03; 95% CI, 1.49–2.78); p \u3c 0.001), mild thrombocytopenia (HR, 1.15; 95% CI, 1.01–1.34; p = 0.045), and thrombocytosis groups (HR, 1.47; 95% CI, 1.07–2.03; p = 0.019) showed higher 5-year MACE rates than the normal reference. In BARC 2, 3, and 5 bleeding outcomes, the bleedings rates were higher than the normal range in the moderate to severe thrombocytopenia (HR, 2.18; 95% CI, 1.36–3.49; p = 0.001) and mild thrombocytopenia (HR, 1.41; 95% CI, 1.12–1.78; p = 0.004) groups. Patients with AMI had higher 5-year MACE rates after PCI if they had lower- or higher-than-normal platelet counts. Thrombocytopenia revealed higher early and late bleeding rates whereas thrombocytosis showed long-term bleeding trends, although these trends were not statistically significant

Integration of data from probability surveys and big found data for finite population inference using mass imputation

Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.This article is published as S. Yang, J.K. Kim, and Y. Hwang (2021). "Integration of data from probability surveys and big found data for finite population inference using mass imputation," Survey Methodology, 47, no. 1, 29-58. https://www150.statcan.gc.ca/n1/pub/12-001-x/12-001-x2021001-eng.htm Copyright 2021 Her Majesty the Queen in Right of Canada as represented by the Minister of Industry. Posted with permission

Ground motion amplification models for Japan using machine learning techniques

Earthquake-induced ground motions can be altered by various factors that are associated with the characteristics of earthquake sources, paths, and sites. Conventionally, regression approaches have been used to develop empirical prediction models for ground motion amplifications. We developed models for ground motion amplifications based on three machine learning techniques (i.e., random forest, gradient boosting, and artificial neural network) using the database of the records at the KiK-net stations in Japan. The proposed machine learning based models outperforms the regression based model. The random forest based model provides the best estimation of amplification factors. Average shear wave velocity and the depth of the borehole are the two factors that influence the amplification model the most. Maps of the amplification factors for all KiK-net stations under moderate and large earthquake scenarios are provided. The three machine learning technique based models are also provided for the forward prediction of other earthquake scenarios

Bottom-up estimation and top-down prediction: Solar energy prediction combining information from multiple sources

Accurately forecasting solar power using the data from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements from an automated monitoring network so as to better predict solar power in a timely manner. To this end, we propose a new approach of analyzing large-scale multilevel models with great computational efficiency requiring minimum monitoring and intervention. This approach features a division of the large scale data set into smaller ones with manageable sizes, based on their physical locations, and fit a local model in each area. The local model estimates are then combined sequentially from the specified multilevel models using our novel bottom-up approach for parameter estimation. The prediction, on the other hand, is implemented in a top-down matter. The proposed method is applied to the solar energy prediction problem for the U.S. Department of Energy’s SunShot Initiative.This article is published as Hwang, Youngdeok; Lu, Siyuan; Kim, Jae-Kwang. Bottom-up estimation and top-down prediction: Solar energy prediction combining information from multiple sources. Ann. Appl. Stat. 12 (2018), no. 4, 2096--2120. doi: 10.1214/18-AOAS1145. Posted with permission.</p