Frontiers in Applied Mathematics and Statistics最新文献_第8页

Testing the forecasting skills of aftershock models using a Bayesian framework 利用贝叶斯框架测试余震模型的预测能力

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-14 DOI: 10.3389/fams.2023.1126511

Elisa Dong, R. Shcherbakov, K. Goda

{"title":"Testing the forecasting skills of aftershock models using a Bayesian framework","authors":"Elisa Dong, R. Shcherbakov, K. Goda","doi":"10.3389/fams.2023.1126511","DOIUrl":"https://doi.org/10.3389/fams.2023.1126511","url":null,"abstract":"The Epidemic Type Aftershock Sequence (ETAS) model and the modified Omori law (MOL) are two aftershock rate models that are used for operational earthquake/aftershock forecasting. Previous studies have investigated the relative performance of the two models for specific case studies. However, a rigorous comparative evaluation of the forecasting performance of the basic aftershock rate models for several different earthquake sequences has not been done before. In this study, forecasts of five prominent aftershock sequences from multiple catalogs are computed using the Bayesian predictive distribution, which fully accounts for the uncertainties in the model parameters. This is done by the Markov Chain Monte Carlo (MCMC) sampling of the model parameters and forward simulation of the ETAS or MOL models to compute the aftershock forecasts. The forecasting results are evaluated using five different statistical tests, including two comparison tests. The forecasting skill tests indicate that the ETAS model tends to perform consistently well on the first three tests. The MOL fails the same tests for certain forecasting time intervals. However, in the comparison tests, it is not definite whether the ETAS model is the better performing model. This work demonstrates the use of forecast testing for different catalogs, which is also applicable to catalogs with a higher magnitude of completeness.","PeriodicalId":36662,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44173474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Insights in mathematical biology 2022 社论：数学生物学的见解2022

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-13 DOI: 10.3389/fams.2023.1197661

R. Eftimie

引用次数: 0

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-10 DOI: 10.3389/fams.2023.1193191

Giulia Chiari, Giada Fiandaca, M. Delitala

{"title":"Hypoxia-related radiotherapy resistance in tumors: treatment efficacy investigation in an eco-evolutionary perspective","authors":"Giulia Chiari, Giada Fiandaca, M. Delitala","doi":"10.3389/fams.2023.1193191","DOIUrl":"https://doi.org/10.3389/fams.2023.1193191","url":null,"abstract":"In the study of therapeutic strategies for the treatment of cancer, eco-evolutionary dynamics are of particular interest, since characteristics of the tumor population, interaction with the environment and effects of the treatment, influence the geometric and epigenetic characterization of the tumor with direct consequences on the efficacy of the therapy and possible relapses. In particular, when considering radiotherapy, oxygen concentration plays a central role both in determining the effectiveness of the treatment and the selective pressure due to hypoxia.We propose a mathematical model, settled in the framework of epigenetically structured population dynamics and formulated in terms of systems of coupled non-linear integro-differential equations that aims to catch these phenomena and to provide a predictive tool for the tumor mass evolution and therapeutic effects.The outcomes of the simulations show how the model is able to explain the impact of environmental selection and therapies on the evolution of the mass, motivating observed dynamics such as relapses and therapeutic failures.This novel modeling framework, together with the experimental results obtained so far, offers a first hint for the development of therapies which can be adapted to overcome problems of resistance and relapses. Further studies, based on a quantification of medical data, could include the development of a mathematical tool for medical support in optimizing therapeutic protocols.","PeriodicalId":36662,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42013725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

AI-enabled case detection model for infectious disease outbreaks in resource-limited settings 在资源有限的环境中，用于传染病爆发的人工智能病例检测模型

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-08 DOI: 10.3389/fams.2023.1133349

C. Sisimayi, C. Harley, F. Nyabadza, Maria Vivien V. Visaya

{"title":"AI-enabled case detection model for infectious disease outbreaks in resource-limited settings","authors":"C. Sisimayi, C. Harley, F. Nyabadza, Maria Vivien V. Visaya","doi":"10.3389/fams.2023.1133349","DOIUrl":"https://doi.org/10.3389/fams.2023.1133349","url":null,"abstract":"Introduction The utility of non-contact technologies for screening infectious diseases such as COVID-19 can be enhanced by improving the underlying Artificial Intelligence (AI) models and integrating them into data visualization frameworks. AI models that are a fusion of different Machine Learning (ML) models where one has leveraged the different positive attributes of these models have the potential to perform better in detecting infectious diseases such as COVID-19. Furthermore, integrating other patient data such as clinical, socio-demographic, economic and environmental variables with the image data (e.g., chest X-rays) can enhance the detection capacity of these models. Methods In this study, we explore the use of chest X-ray data in training an optimized hybrid AI model based on a real-world dataset with limited sample size to screen patients with COVID-19. We develop a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) model based on image features extracted through a CNN and EfficientNet B0 Transfer Learning Model and applied to an RF classifier. Our approach includes an intermediate step of using the RF's wrapper function, the Boruta Algorithm, to select important variable features and further reduce the number of features prior to using the RF model. Results and discussion The new model obtained an accuracy and recall of 96% for both and outperformed the base CNN model and four other experimental models that combined transfer learning and alternative options for dimensionality reduction. The performance of the model fares closely to relatively similar models previously developed, which were trained on large datasets drawn from different country contexts. The performance of the model is very close to that of the “gold standard” PCR tests, which demonstrates the potential for use of this approach to efficiently scale-up surveillance and screening capacities in resource limited settings.","PeriodicalId":36662,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47662913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Stabilizing machine learning models with Age-Period-Cohort inputs for scoring and stress testing 稳定机器学习模型与年龄-时期-队列输入评分和压力测试

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-08 DOI: 10.3389/fams.2023.1195810

J. Breeden, Ye. A. Leonova

{"title":"Stabilizing machine learning models with Age-Period-Cohort inputs for scoring and stress testing","authors":"J. Breeden, Ye. A. Leonova","doi":"10.3389/fams.2023.1195810","DOIUrl":"https://doi.org/10.3389/fams.2023.1195810","url":null,"abstract":"Machine learning models have been used extensively for credit scoring, but the architectures employed suffer from a significant loss in accuracy out-of-sample and out-of-time. Further, the most common architectures do not effectively integrate economic scenarios to enable stress testing, cash flow, or yield estimation. The present research demonstrates that providing lifecycle and environment functions from Age-Period-Cohort analysis can significantly improve out-of-sample and out-of-time performance as well as enabling the model's use in both scoring and stress testing applications. This method is demonstrated for behavior scoring where account delinquency is one of the provided inputs, because behavior scoring has historically presented the most difficulties for combining credit scoring and stress testing. Our method works well in both origination and behavior scoring. The results are also compared to multihorizon survival models, which share the same architectural design with Age-Period-Cohort inputs and coefficients that vary with forecast horizon, but using a logistic regression estimation of the model. The analysis was performed on 30-year prime conforming US mortgage data. Nonlinear problems involving large amounts of alternate data are best at highlighting the advantages of machine learning. Data from Fannie Mae and Freddie Mac is not such a test case, but it serves the purpose of comparing these methods with and without Age-Period-Cohort inputs. In order to make a fair comparison, all models are given a panel structure where each account is observed monthly to determine default or non-default.","PeriodicalId":36662,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47077486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian synthetic likelihood for stochastic models with applications in mathematical finance 随机模型的贝叶斯综合似然及其在数学金融中的应用

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-05 DOI: 10.3389/fams.2023.1187878

R. Maraia, Sebastian Springer, Teemu Härkönen, M. Simon, H. Haario

引用次数: 0

MAD (about median) vs. quantile-based alternatives for classical standard deviation, skewness, and kurtosis MAD(关于中位数)vs.基于分位数的经典标准差、偏度和峰度的替代方法

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-02 DOI: 10.3389/fams.2023.1206537

E. Pinsky, S. Klawansky

{"title":"MAD (about median) vs. quantile-based alternatives for classical standard deviation, skewness, and kurtosis","authors":"E. Pinsky, S. Klawansky","doi":"10.3389/fams.2023.1206537","DOIUrl":"https://doi.org/10.3389/fams.2023.1206537","url":null,"abstract":"In classical probability and statistics, one computes many measures of interest from mean and standard deviation. However, mean, and especially standard deviation, are overly sensitive to outliers. One way to address this sensitivity is by considering alternative metrics for deviation, skewness, and kurtosis using mean absolute deviations from the median (MAD). We show that the proposed measures can be computed in terms of the sub-means of the appropriate left and right sub-ranges. They can be interpreted in terms of average distances of values of these sub-ranges from their respective medians. We emphasize that these measures utilize only the first-order moment within each sub-range and, in addition, are invariant to translation or scaling. The obtained formulas are similar to the quantile measures of deviation, skewness, and kurtosis but involve computing sub-means as opposed to quantiles. While the classical skewness can be unbounded, both the MAD-based and quantile skewness always lies in the range [−1, 1]. In addition, while both the classical kurtosis and quantile-based kurtosis can be unbounded, the proposed MAD-based alternative for kurtosis lies in the range [0, 1]. We present a detailed comparison of MAD-based, quantile-based, and classical metrics for the six well-known theoretical distributions considered. We illustrate the practical utility of MAD-based metrics by considering the theoretical properties of the Pareto distribution with high concentrations of density in the upper tail, as might apply to the analysis of wealth and income. In summary, the proposed MAD-based alternatives provide a universal scale to compare deviation, skewness, and kurtosis across different distributions.","PeriodicalId":36662,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42514155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mathematical modeling for the control of fly-borne mastitis disease in cattle 牛蝇传乳腺炎疾病控制的数学模型

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-06-01 DOI: 10.3389/fams.2023.1171157

Moses Olayemi Adeyemi, T. O. Oluyo

{"title":"Mathematical modeling for the control of fly-borne mastitis disease in cattle","authors":"Moses Olayemi Adeyemi, T. O. Oluyo","doi":"10.3389/fams.2023.1171157","DOIUrl":"https://doi.org/10.3389/fams.2023.1171157","url":null,"abstract":"Several diseases cause losses in cattle farming, especially in the dairy industry among which mastitis disease (Bovine mastitis) is the leading cause of health and economic damages globally as it results in animals' ill health and reduced quality and quantity of milk produced by infected cows. Some mathematical studies have been conducted that focused on mastitis transmission from one udder-quarter to another in an infected cow, even though clinical studies established the cow–cow and flies–cow transmissions. The present study, therefore, proposed a mathematical model for the control of mastitis disease in cattle in the presence of flies as vectors. The formulated model was shown to have non-negative solutions in feasible regions for both cattle and flies populations. Furthermore, the model has a stable disease-free equilibrium if the sum of the effective reproduction numbers for cattle–cattle and fly–cattle transmissions (ℜch and ℜch) is less than unity, and there is a possibility of multiple endemic equilibria if otherwise. The numerical results indicated that the infectious populations can be reduced if the rates of the control parameters are increased, thereby curtailing or eradicating mastitis in the cattle population.","PeriodicalId":36662,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44638763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Advances in data-driven approaches and modeling of complex systems 社论：数据驱动方法和复杂系统建模的进展

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-05-25 DOI: 10.3389/fams.2023.1215077

M. H. Mohd, T. Nguyen-Huu, Junpyo Park, J. Addawe, H. Haga

引用次数: 0

Editorial: Mathematical and empirical foundations of models of consciousness 社论:意识模型的数学和经验基础

IF 1.4

Frontiers in Applied Mathematics and Statistics Pub Date : 2023-05-22 DOI: 10.3389/fams.2023.1214939

X. Arsiwalla, Johannes Kleiner, S. Tull, Pedro Resende, Kobi Kremnitzer

引用次数: 0