arXiv - STAT - Applications最新文献_第3页

Privacy risk from synthetic data: practical proposals 合成数据的隐私风险：实用建议

arXiv - STAT - Applications Pub Date : 2024-09-06 DOI: arxiv-2409.04257

Gillian M Raab

引用次数: 0

Enhancing Electrocardiography Data Classification Confidence: A Robust Gaussian Process Approach (MuyGPs) 增强心电图数据分类的可信度：鲁棒高斯过程方法 (MuyGPs)

arXiv - STAT - Applications Pub Date : 2024-09-06 DOI: arxiv-2409.04642

Ukamaka V. Nnyaba, Hewan M. Shemtaga, David W. Collins, Amanda L. Muyskens, Benjamin W. Priest, Nedret Billor

{"title":"Enhancing Electrocardiography Data Classification Confidence: A Robust Gaussian Process Approach (MuyGPs)","authors":"Ukamaka V. Nnyaba, Hewan M. Shemtaga, David W. Collins, Amanda L. Muyskens, Benjamin W. Priest, Nedret Billor","doi":"arxiv-2409.04642","DOIUrl":"https://doi.org/arxiv-2409.04642","url":null,"abstract":"Analyzing electrocardiography (ECG) data is essential for diagnosing and\u0000monitoring various heart diseases. The clinical adoption of automated methods\u0000requires accurate confidence measurements, which are largely absent from\u0000existing classification methods. In this paper, we present a robust Gaussian\u0000Process classification hyperparameter training model (MuyGPs) for discerning\u0000normal heartbeat signals from the signals affected by different arrhythmias and\u0000myocardial infarction. We compare the performance of MuyGPs with traditional\u0000Gaussian process classifier as well as conventional machine learning models,\u0000such as, Random Forest, Extra Trees, k-Nearest Neighbors and Convolutional\u0000Neural Network. Comparing these models reveals MuyGPs as the most performant\u0000model for making confident predictions on individual patient ECGs. Furthermore,\u0000we explore the posterior distribution obtained from the Gaussian process to\u0000interpret the prediction and quantify uncertainty. In addition, we provide a\u0000guideline on obtaining the prediction confidence of the machine learning models\u0000and quantitatively compare the uncertainty measures of these models.\u0000Particularly, we identify a class of less-accurate (ambiguous) signals for\u0000further diagnosis by an expert.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188143","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

Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph 在买方市场实验中使用实验内双方图衡量卖方结果

arXiv - STAT - Applications Pub Date : 2024-09-06 DOI: arxiv-2409.04174

Vaiva Pilkauskaitė, Jevgenij Gamper, Rasa Giniūnaitė, Agne Reklaitė

引用次数: 0

Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning 通过异构迁移学习对遥感场景进行深度聚类

arXiv - STAT - Applications Pub Date : 2024-09-05 DOI: arxiv-2409.03938

Isaac Ray, Alexei Skurikhin

引用次数: 0

Causal effect of the infield shift in the MLB MLB 内野转移的因果效应

arXiv - STAT - Applications Pub Date : 2024-09-05 DOI: arxiv-2409.03940

Sonia Markes, Linbo Wang, Jessica Gronsbell, Katherine Evans

引用次数: 0

A Stochastic Weather Model: A Case of Bono Region of Ghana 随机天气模型：加纳博诺地区案例

arXiv - STAT - Applications Pub Date : 2024-09-04 DOI: arxiv-2409.06731

Bernard Gyamfi

{"title":"A Stochastic Weather Model: A Case of Bono Region of Ghana","authors":"Bernard Gyamfi","doi":"arxiv-2409.06731","DOIUrl":"https://doi.org/arxiv-2409.06731","url":null,"abstract":"The paper sought to fit an Ornstein Uhlenbeck model with seasonal mean and\u0000volatility, where the residuals are generated by a Brownian motion for Ghanian\u0000daily average temperature. This paper employed the modified Ornstein Uhlenbeck\u0000model proposed by Bhowan which has a seasonal mean and stochastic volatility\u0000process. The findings revealed that, the Bono region experiences warm\u0000temperatures and maximum precipitation up to 32.67 degree celsius and 126.51mm\u0000respectively. It was observed that the Daily Average Temperature (DAT) of the\u0000region reverts to a temperature of approximately 26 degree celsius at a rate of\u000018.72% with maximum and minimum temperatures of 32.67degree celsius and\u000019.75degree celsius respectively. Although the region is in the middle belt of\u0000Ghana, it still experiences warm(hot) temperatures daily and experiences dry\u0000seasons relatively more than wet seasons in the number of years considered for\u0000our analysis. Our model explained approximately 50% of the variations in the\u0000daily average temperature of the region which can be regarded as relatively a\u0000good model. The findings of this paper are relevant in the pricing of weather\u0000derivatives with temperature as an underlying variable in the Ghanaian\u0000financial and agricultural sector. Furthermore, it would assist in the\u0000development and design of tailored agriculture/crop insurance models which\u0000would incorporate temperature dynamics rather than extreme weather\u0000conditions/events such as floods, drought and wildfires.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187903","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

Meal-taking activity monitoring in the elderly based on sensor data: Comparison of unsupervised classification methods 基于传感器数据的老年人进餐活动监测：无监督分类方法的比较

arXiv - STAT - Applications Pub Date : 2024-09-04 DOI: arxiv-2409.02971

Abderrahim DerouicheLAAS-S4M, UT3, Damien BrulinLAAS-S4M, UT2J, Eric CampoLAAS-S4M, UT2J, Antoine Piau

{"title":"Meal-taking activity monitoring in the elderly based on sensor data: Comparison of unsupervised classification methods","authors":"Abderrahim DerouicheLAAS-S4M, UT3, Damien BrulinLAAS-S4M, UT2J, Eric CampoLAAS-S4M, UT2J, Antoine Piau","doi":"arxiv-2409.02971","DOIUrl":"https://doi.org/arxiv-2409.02971","url":null,"abstract":"In an era marked by a demographic change towards an older population, there\u0000is an urgent need to improve nutritional monitoring in view of the increase in\u0000frailty. This research aims to enhance the identification of meal-taking\u0000activities by combining K-Means, GMM, and DBSCAN techniques. Using the\u0000Davies-Bouldin Index (DBI) for the optimal meal taking activity clustering, the\u0000results show that K-Means seems to be the best solution, thanks to its\u0000unrivalled efficiency in data demarcation, compared with the capabilities of\u0000GMM and DBSCAN. Although capable of identifying complex patterns and outliers,\u0000the latter methods are limited by their operational complexities and dependence\u0000on precise parameter configurations. In this paper, we have processed data from\u00004 houses equipped with sensors. The findings indicate that applying the K-Means\u0000method results in high performance, evidenced by a particularly low\u0000Davies-Bouldin Index (DBI), illustrating optimal cluster separation and\u0000cohesion. Calculating the average duration of each activity using the GMM\u0000algorithm allows distinguishing various categories of meal-taking activities.\u0000Alternatively, this can correspond to different times of the day fitting to\u0000each meal-taking activity. Using K-Means, GMM, and DBSCAN clustering\u0000algorithms, the study demonstrates an effective strategy for thoroughly\u0000understanding the data. This approach facilitates the comparison and selection\u0000of the most suitable method for optimal meal-taking activity clustering.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188150","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

Fundamental properties of linear factor models 线性因子模型的基本特性

arXiv - STAT - Applications Pub Date : 2024-09-04 DOI: arxiv-2409.02521

Damir Filipovic, Paul Schneider

引用次数: 0

Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon 使用 LSTM 和 GRU 的神经网络模拟亚马逊活跃火灾

arXiv - STAT - Applications Pub Date : 2024-09-04 DOI: arxiv-2409.02681

Ramon Tavares

{"title":"Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon","authors":"Ramon Tavares","doi":"arxiv-2409.02681","DOIUrl":"https://doi.org/arxiv-2409.02681","url":null,"abstract":"This study presents a comprehensive methodology for modeling and forecasting\u0000the historical time series of fire spots detected by the AQUA_M-T satellite in\u0000the Amazon, Brazil. The approach utilizes a mixed Recurrent Neural Network\u0000(RNN) model, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit\u0000(GRU) architectures to predict monthly accumulations of daily detected fire\u0000spots. A summary of the data revealed a consistent seasonality over time, with\u0000annual maximum and minimum fire spot values tending to repeat at the same\u0000periods each year. The primary objective is to verify whether the forecasts\u0000capture this inherent seasonality through rigorous statistical analysis. The\u0000methodology involved careful data preparation, model configuration, and\u0000training using cross-validation with two seeds, ensuring that the data\u0000generalizes well to the test and validation sets, and confirming the\u0000convergence of the model parameters. The results indicate that the mixed LSTM\u0000and GRU model offers improved accuracy in forecasting 12 months ahead,\u0000demonstrating its effectiveness in capturing complex temporal patterns and\u0000modeling the observed time series. This research significantly contributes to\u0000the application of deep learning techniques in environmental monitoring,\u0000specifically in fire spot forecasting. In addition to improving forecast\u0000accuracy, the proposed approach highlights the potential for adaptation to\u0000other time series forecasting challenges, opening new avenues for research and\u0000development in machine learning and natural phenomenon prediction. Keywords:\u0000Time Series Forecasting, Recurrent Neural Networks, Deep Learning.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188151","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 Dynamic Generalized Additive Model for Mortality during COVID-19 Pandemic COVID-19 大流行期间死亡率的贝叶斯动态广义加法模型

arXiv - STAT - Applications Pub Date : 2024-09-04 DOI: arxiv-2409.02378

Wei Zhang, Antonietta Mira, Ernst C. Wit

{"title":"Bayesian Dynamic Generalized Additive Model for Mortality during COVID-19 Pandemic","authors":"Wei Zhang, Antonietta Mira, Ernst C. Wit","doi":"arxiv-2409.02378","DOIUrl":"https://doi.org/arxiv-2409.02378","url":null,"abstract":"While COVID-19 has resulted in a significant increase in global mortality\u0000rates, the impact of the pandemic on mortality from other causes remains\u0000uncertain. To gain insight into the broader effects of COVID-19 on various\u0000causes of death, we analyze an Italian dataset that includes monthly mortality\u0000counts for different causes from January 2015 to December 2020. Our approach\u0000involves a generalized additive model enhanced with correlated random effects.\u0000The generalized additive model component effectively captures non-linear\u0000relationships between various covariates and mortality rates, while the random\u0000effects are multivariate time series observations recorded in various\u0000locations, and they embody information on the dependence structure present\u0000among geographical locations and different causes of mortality. Adopting a\u0000Bayesian framework, we impose suitable priors on the model parameters. For\u0000efficient posterior computation, we employ variational inference, specifically\u0000for fixed effect coefficients and random effects, Gaussian variational\u0000approximation is assumed, which streamlines the analysis process. The\u0000optimisation is performed using a coordinate ascent variational inference\u0000algorithm and several computational strategies are implemented along the way to\u0000address the issues arising from the high dimensional nature of the data,\u0000providing accelerated and stabilised parameter estimation and statistical\u0000inference.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224684","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