arXiv - STAT - Applications最新文献_第4页

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

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":"7 1","pages":""},"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":"19 1","pages":""},"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":"36 1","pages":""},"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

Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial with Causal Forests 估算精神病学中的治疗效果异质性：因果森林回顾与教程

arXiv - STAT - Applications Pub Date : 2024-09-03 DOI: arxiv-2409.01578

Erik Sverdrup, Maria Petukhova, Stefan Wager

{"title":"Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial with Causal Forests","authors":"Erik Sverdrup, Maria Petukhova, Stefan Wager","doi":"arxiv-2409.01578","DOIUrl":"https://doi.org/arxiv-2409.01578","url":null,"abstract":"Flexible machine learning tools are being used increasingly to estimate\u0000heterogeneous treatment effects. This paper gives an accessible tutorial\u0000demonstrating the use of the causal forest algorithm, available in the R\u0000package grf. We start with a brief non-technical overview of treatment effect\u0000estimation methods with a focus on estimation in observational studies,\u0000although similar methods can be used in experimental studies. We then discuss\u0000the logic of estimating heterogeneous effects using the extension of the random\u0000forest algorithm implemented in grf. Finally, we illustrate causal forest by\u0000conducting a secondary analysis on the extent to which individual differences\u0000in resilience to high combat stress can be measured among US Army soldiers\u0000deploying to Afghanistan based on information about these soldiers available\u0000prior to deployment. Throughout we illustrate simple and interpretable\u0000exercises for both model selection and evaluation, including targeting operator\u0000characteristics curves, Qini curves, area-under-the-curve summaries, and best\u0000linear projections. A replication script with simulated data is available at\u0000github.com/grf-labs/grf/tree/master/experiments/ijmpr","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188153","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

Conditional multi-step attribution for climate forcings 气候作用力的有条件多步骤归因

arXiv - STAT - Applications Pub Date : 2024-09-02 DOI: arxiv-2409.01396

Christopher R. Wentland, Michael Weylandt, Laura P. Swiler, Thomas S. Ehrmann, Diana Bull

{"title":"Conditional multi-step attribution for climate forcings","authors":"Christopher R. Wentland, Michael Weylandt, Laura P. Swiler, Thomas S. Ehrmann, Diana Bull","doi":"arxiv-2409.01396","DOIUrl":"https://doi.org/arxiv-2409.01396","url":null,"abstract":"Attribution of climate impacts to a source forcing is critical to\u0000understanding, communicating, and addressing the effects of human influence on\u0000the climate. While standard attribution methods, such as optimal\u0000fingerprinting, have been successfully applied to long-term, widespread effects\u0000such as global surface temperature warming, they often struggle in low\u0000signal-to-noise regimes, typical of short-term climate forcings or climate\u0000variables which are loosely related to the forcing. Single-step approaches,\u0000which directly relate a source forcing and final impact, are unable to utilize\u0000additional climate information to improve attribution certainty. To address\u0000this shortcoming, this paper presents a novel multi-step attribution approach\u0000which is capable of analyzing multiple variables conditionally. A connected\u0000series of climate effects are treated as dependent, and relationships found in\u0000intermediary steps of a causal pathway are leveraged to better characterize the\u0000forcing impact. This enables attribution of the forcing level responsible for\u0000the observed impacts, while equivalent single-step approaches fail. Utilizing a\u0000scalar feature describing the forcing impact, simple forcing response models,\u0000and a conditional Bayesian formulation, this method can incorporate several\u0000causal pathways to identify the correct forcing magnitude. As an exemplar of a\u0000short-term, high-variance forcing, we demonstrate this method for the 1991\u0000eruption of Mt. Pinatubo. Results indicate that including stratospheric and\u0000surface temperature and radiative flux measurements increases attribution\u0000certainty compared to analyses derived solely from temperature measurements.\u0000This framework has potential to improve climate attribution assessments for\u0000both geoengineering projects and long-term climate change, for which standard\u0000attribution methods may fail.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"180 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187898","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

A method to convert traditional fingerprint ACE / ACE-V outputs ("identification", "inconclusive", "exclusion") to Bayes factors 将传统指纹 ACE / ACE-V 输出（"识别"、"不确定"、"排除"）转换为贝叶斯因子的方法

arXiv - STAT - Applications Pub Date : 2024-08-31 DOI: arxiv-2409.00451

Geoffrey Stewart Morrison

引用次数: 0

Localizing Single and Multiple Oscillatory Sources: A Frequency Divider Approach 定位单个和多个振荡源：分频器方法

arXiv - STAT - Applications Pub Date : 2024-08-31 DOI: arxiv-2409.00566

Rajasekhar Anguluri, Anamitra Pal

引用次数: 0

Personalized Pricing Decisions Through Adversarial Risk Analysis 通过对抗性风险分析做出个性化定价决策

arXiv - STAT - Applications Pub Date : 2024-08-31 DOI: arxiv-2409.00444

Daniel García Rasines, Roi Naveiro, David Ríos Insua, Simón Rodríguez Santana

{"title":"Personalized Pricing Decisions Through Adversarial Risk Analysis","authors":"Daniel García Rasines, Roi Naveiro, David Ríos Insua, Simón Rodríguez Santana","doi":"arxiv-2409.00444","DOIUrl":"https://doi.org/arxiv-2409.00444","url":null,"abstract":"Pricing decisions stand out as one of the most critical tasks a company\u0000faces, particularly in today's digital economy. As with other business\u0000decision-making problems, pricing unfolds in a highly competitive and uncertain\u0000environment. Traditional analyses in this area have heavily relied on game\u0000theory and its variants. However, an important drawback of these approaches is\u0000their reliance on common knowledge assumptions, which are hardly tenable in\u0000competitive business domains. This paper introduces an innovative personalized\u0000pricing framework designed to assist decision-makers in undertaking pricing\u0000decisions amidst competition, considering both buyer's and competitors'\u0000preferences. Our approach (i) establishes a coherent framework for modeling\u0000competition mitigating common knowledge assumptions; (ii) proposes a principled\u0000method to forecast competitors' pricing and customers' purchasing decisions,\u0000acknowledging major business uncertainties; and, (iii) encourages structured\u0000thinking about the competitors' problems, thus enriching the solution process.\u0000To illustrate these properties, in addition to a general pricing template, we\u0000outline two specifications - one from the retail domain and a more intricate\u0000one from the pension fund domain.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187901","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