scholarly journals Strong uniform consistency results of the weighted average of conditional artificial data points

2008 ◽  
Vol 138 (5) ◽  
pp. 1496-1515 ◽  
Author(s):  
Cédric Heuchenne
Keyword(s):  
Weighted Average ◽  
Artificial Data ◽  
Uniform Consistency ◽  
Consistency Results ◽  
Data Points ◽  
Strong Uniform Consistency

scholarly journals A novel atrial fibrillation prediction model for Chinese subjects: a nationwide cohort investigation of 682 237 study participants with random forest model

EP Europace ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 1307-1312 ◽  
Author(s):  
Wei-Syun Hu ◽  
Meng-Hsuen Hsieh ◽  
Cheng-Li Lin
Keyword(s):  
Atrial Fibrillation ◽  
Random Forest ◽  
Prediction Model ◽  
Roc Curve ◽  
Model Performance ◽  
Weighted Average ◽  
Random Forest Model ◽  
Test Set ◽  
Forest Model ◽  
Data Points

Abstract Aims We aimed to construct a random forest model to predict atrial fibrillation (AF) in Chinese population. Methods and results This study was comprised of 682 237 subjects with or without AF. Each subject had 19 features that included the subjects’ age, gender, underlying diseases, CHA2DS2-VASc score, and follow-up period. The data were split into train and test sets at an approximate 9:1 ratio: 614 013 data points were placed into the train set and 68 224 data points were placed into the test set. In this study, weighted average F1, precision, and recall values were used to measure prediction model performance. The F1, precision, and recall values were calculated across the train set, the test set, and all data. The area under receiving operating characteristic (ROC) curve was also used to evaluate the performance of the prediction model. The prediction model achieved a k-fold cross-validation accuracy of 0.979 (k = 10). In the test set, the prediction model achieved an F1 value of 0.968, precision value of 0.958, and recall value of 0.979. The area under ROC curve of the model was 0.948 (95% confidence interval 0.947–0.949). This model was validated with a separate dataset. Conclusions This study showed a novel AF risk prediction scheme for Chinese individuals with random forest model methodology.

Using Multiple Surrogates for Minimization of the RMS Error in Meta-Modeling

2008 ◽  
Author(s):  
Felipe A. C. Viana ◽  
Raphael T. Haftka
Keyword(s):  
Cross Validation ◽  
Weighted Average ◽  
Large Set ◽  
High Dimensions ◽  
Engineering Problems ◽  
Cross Validation Error ◽  
Data Points ◽  
Meta Modeling ◽  
Rms Error ◽  
Rms Errors

Surrogate models are commonly used to replace expensive simulations of engineering problems. Frequently, a single surrogate is chosen based on past experience. Previous work has shown that fitting multiple surrogates and picking one based on cross-validation errors (PRESS in particular) is a good strategy, and that cross validation errors may also be used to create a weighted surrogate. In this paper, we discuss whether to use the best PRESS solution or a weighted surrogate when a single surrogate is needed. We propose the minimization of the integrated square error as a way to compute the weights of the weighted average surrogate. We find that it pays to generate a large set of different surrogates and then use PRESS as a criterion for selection. We find that the cross validation error vectors provide an excellent estimate of the RMS errors when the number of data points is high. Hence the use of cross validation errors for choosing a surrogate and for calculating the weights of weighted surrogates becomes more attractive in high dimensions. However, it appears that the potential gains from using weighted surrogates diminish substantially in high dimensions.

Comparative Evaluation of the Polar Organic Chemical Integrative Sampler (POCIS) in Two Types of Validation Systems

2021 ◽  
Author(s):  
Kazushi Noro ◽  
Etiënne L. M. Vermeirssen ◽  
Arisa Banno ◽  
Junko Ono ◽  
Yoshinori Yabuki
Keyword(s):  
Natural Water ◽  
Tap Water ◽  
Low Cost ◽  
Weighted Average ◽  
Organic Chemical ◽  
Average Value ◽  
Data Points ◽  
Time Weighted Average ◽  
Few Data ◽  
Neonicotinoid Pesticides

<p>Polar organic chemical integrative sampler (POCIS) devices have been suggested for monitoring contaminant fluctuations in aquatic environments, resulting from chemical leak accidents. However, the response of the POCIS device in the emergency condition in natural water remains unclear. The response of the POCIS device to contaminant fluctuation was investigated using a simplified chamber test with tap water and a channel test with natural water in the present study. The fluctuation in the chamber and the channel replicated the condition of river water under a chemical leak scenario (maximum concentration: 1–10 μg L<sup>–1</sup>, half-life: 1 d). The target chemicals were neonicotinoid pesticides (dinotefuran, clothianidin, thiamethoxam, imidacloprid, acetamiprid, and thiacloprid) and bisphenol A. The ratio of the POCIS measured value to the time-weighted average value of grab samplings (POCIS/TWA) for the channel test (temperature: 15 °C, flow velocity: 15 cm s<sup>–1</sup>) ranged from 61% (clothianidin) to 133% (thiacloprid). The results indicated that the POCIS device could be effectively used as a monitoring device in an aquatic environment under the chemical leak scenario. In addition, the differences in the POCIS/TWA ratios obtained from the chamber test and the channel test were in the range of –50 to 50%, except for a few data points. Thus, the simplified chamber test could be used as a validation system to evaluate the POCIS device at a low cost.</p>

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