Statistical Methods for Measuring Market Risk Model Performance - Comparison in View of the Fundamental Review of the Trading Book

2015 ◽  
Author(s):  
Maximilian Dinse
Keyword(s):  
Statistical Methods ◽  
Risk Model ◽  
Model Performance ◽  
Market Risk ◽  
Performance Comparison

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population

2017 ◽  
Vol 28 (1) ◽  
pp. 309-320 ◽  
Author(s):  
Scott Powers ◽  
Valerie McGuire ◽  
Leslie Bernstein ◽  
Alison J Canchola ◽  
Alice S Whittemore
Keyword(s):  
Prediction Models ◽  
Risk Model ◽  
Disease Incidence ◽  
Model Performance ◽  
Target Population ◽  
R Package ◽  
Model Assessment ◽  
Weighted Estimates ◽  
Cancer Prediction ◽  
Model Predictions

Personal predictive models for disease development play important roles in chronic disease prevention. The performance of these models is evaluated by applying them to the baseline covariates of participants in external cohort studies, with model predictions compared to subjects' subsequent disease incidence. However, the covariate distribution among participants in a validation cohort may differ from that of the population for which the model will be used. Since estimates of predictive model performance depend on the distribution of covariates among the subjects to which it is applied, such differences can cause misleading estimates of model performance in the target population. We propose a method for addressing this problem by weighting the cohort subjects to make their covariate distribution better match that of the target population. Simulations show that the method provides accurate estimates of model performance in the target population, while un-weighted estimates may not. We illustrate the method by applying it to evaluate an ovarian cancer prediction model targeted to US women, using cohort data from participants in the California Teachers Study. The methods can be implemented using open-source code for public use as the R-package RMAP (Risk Model Assessment Package) available at http://stanford.edu/~ggong/rmap/ .

Cardiovascular risk model performance in women with and without hypertensive disorders of pregnancy

Heart ◽  
2018 ◽  
Vol 105 (4) ◽  
pp. 330-336 ◽  
Author(s):  
Veerle Dam ◽  
N Charlotte Onland-Moret ◽  
W M Monique Verschuren ◽  
Jolanda M A Boer ◽  
Laura Benschop ◽  
...  
Keyword(s):  
Risk Model ◽  
Model Performance ◽  
Predictive Performance ◽  
Baseline Risk ◽  
Hypertensive Disorders Of Pregnancy ◽  
Cvd Risk ◽  
Hypertensive Disorders ◽  
Framingham Risk ◽  
History Of ◽  
Score Model

ObjectivesCompare the predictive performance of Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs) and Systematic COronary Risk Evaluation (SCORE) model between women with and without a history of hypertensive disorders of pregnancy (hHDP) and determine the effects of recalibration and refitting on predictive performance.MethodsWe included 29 751 women, 6302 with hHDP and 17 369 without. We assessed whether models accurately predicted observed 10-year cardiovascular disease (CVD) risk (calibration) and whether they accurately distinguished between women developing CVD during follow-up and not (discrimination), separately for women with and without hHDP. We also recalibrated (updating intercept and slope) and refitted (recalculating coefficients) the models.ResultsOriginal FRS and PCEs overpredicted 10-year CVD risks, with expected:observed (E:O) ratios ranging from 1.51 (for FRS in women with hHDP) to 2.29 (for PCEs in women without hHDP), while E:O ratios were close to 1 for SCORE. Overprediction attenuated slightly after recalibration for FRS and PCEs in both hHDP groups. Discrimination was reasonable for all models, with C-statistics ranging from 0.70-0.81 (women with hHDP) and 0.72–0.74 (women without hHDP). C-statistics improved slightly after refitting 0.71–0.83 (with hHDP) and 0.73–0.80 (without hHDP). The E:O ratio of the original PCE model was statistically significantly better in women with hHDP compared with women without hHDP.ConclusionsSCORE performed best in terms of both calibration and discrimination, while FRS and PCEs overpredicted risk in women with and without hHDP, but improved after recalibrating and refitting the models. No separate model for women with hHDP seems necessary, despite their higher baseline risk.

Assessment of risk model performance

Absolute Risk ◽  
2017 ◽  
pp. 75-100
Author(s):  
Ruth M. Pfeiffer ◽  
Mitchell H. Gail
Keyword(s):  
Risk Model ◽  
Model Performance

scholarly journals Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0166206 ◽  
Author(s):  
Tianshu Han ◽  
Shuang Tian ◽  
Li Wang ◽  
Xi Liang ◽  
Hongli Cui ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Model ◽  
Model Performance ◽  
Predictive Ability ◽  
Diabetes Risk

scholarly journals Development and validation of a prognostic model based on comorbidities to predict Covid-19 severity. A population-based study

2020 ◽  
Author(s):  
Francisco Gude-Sampedro ◽  
Carmen Fernández-Merino ◽  
Lucía Ferreiro ◽  
Óscar Lado-Baleato ◽  
Jenifer Espasandín-Domínguez ◽  
...  
Keyword(s):  
Disease Severity ◽  
Risk Model ◽  
Model Performance ◽  
Population Based ◽  
Chronic Obstructive ◽  
Operating Characteristics ◽  
Obstructive Pulmonary Disease ◽  
Logistic Regression Models ◽  
Health Authorities ◽  
Obesity Hypertension

Abstract Background The prognosis of patients with Covid-19 infection is uncertain. We derived and validated a new risk model for predicting progression to disease severity, hospitalization, admission to intensive care unit (ICU) and mortality in patients with Covid-19 infection (Gal-Covid-19 scores). Methods This is a retrospective cohort study of patients with Covid-19 infection confirmed by reverse transcription polymerase chain reaction (RT-PCR) in Galicia, Spain. Data were extracted from electronic health records of patients, including age, sex and comorbidities according to International Classification of Primary Care codes (ICPC-2). Logistic regression models were used to estimate the probability of disease severity. Calibration and discrimination were evaluated to assess model performance. Results The incidence of infection was 0.39% (10 454 patients). A total of 2492 patients (23.8%) required hospitalization, 284 (2.7%) were admitted to the ICU and 544 (5.2%) died. The variables included in the models to predict severity included age, gender and chronic comorbidities such as cardiovascular disease, diabetes, obesity, hypertension, chronic obstructive pulmonary disease, asthma, liver disease, chronic kidney disease and haematological cancer. The models demonstrated a fair–good fit for predicting hospitalization {AUC [area under the receiver operating characteristics (ROC) curve] 0.77 [95% confidence interval (CI) 0.76, 0.78]}, admission to ICU [AUC 0.83 (95%CI 0.81, 0.85)] and death [AUC 0.89 (95%CI 0.88, 0.90)]. Conclusions The Gal-Covid-19 scores provide risk estimates for predicting severity in Covid-19 patients. The ability to predict disease severity may help clinicians prioritize high-risk patients and facilitate the decision making of health authorities.

Assessment of the size of VaR backtests for small samples

2020 ◽  
Vol 67 (2) ◽  
pp. 114-151
Author(s):  
Daniel Kaszyński ◽  
Bogumił Kamiński ◽  
Bartosz Pankratz
Keyword(s):  
Value At Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Methodological Approach ◽  
Market Risk ◽  
Small Sample ◽  
Small Samples ◽  
P Value ◽  
Typical Situation ◽  
Risk Management Process

The market risk management process includes the quantification of the risk connected with defined portfolios of assets and the diagnostics of the risk model. Value at Risk (VaR) is one of the most common market risk measures. Since the distributions of the daily P&L of financial instruments are unobservable, literature presents a broad range of backtests for VaR diagnostics. In this paper, we propose a new methodological approach to the assessment of the size of VaR backtests, and use it to evaluate the size of the most distinctive and popular backtests. The focus of the paper is directed towards the evaluation of the size of the backtests for small-sample cases – a typical situation faced during VaR backtesting in banking practice. The results indicate significant differences between tests in terms of the p-value distribution. In particular, frequency-based tests exhibit significantly greater discretisation effects than duration-based tests. This difference is especially apparent in the case of small samples. Our findings prove that from among the considered tests, the Kupiec TUFF and the Haas Discrete Weibull have the best properties. On the other hand, backtests which are very popular in banking practice, that is the Kupiec POF and Christoffersen’s Conditional Coverage, show significant discretisation, hence deviations from the theoretical size.

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