Proceedings of the 3rd International Conference on Statistics: Theory and Applications最新文献_第2页

Combining Time Series and Sentiment Analysis for Stock MarketForecasting 结合时间序列和情绪分析进行股市预测

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-08-01 DOI: 10.11159/icsta21.132

Hsiao-Chuan Chou, K. Ramachandran

引用次数: 0

Change Point Analysis and Clustering Examined Through Chicago Crime During COIVD-19 通过covid -19期间芝加哥犯罪的变化点分析和聚类分析

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-08-01 DOI: 10.11159/icsta21.137

Mena C. R. Whalen, Joseph Feinglass, Noelle I. Samia, A. Papachristos

引用次数: 0

Estimation of Parameters of Logistic Regression with Missing Covariates via Joint Conditional Likelihood Method 用联合条件似然法估计缺失协变量Logistic回归的参数

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-08-01 DOI: 10.11159/icsta21.117

Phuoc-Loc Tran, Truong-Nhat Le, Shen‐Ming Lee, Chin-Shang Li

引用次数: 0

Mathematical Modeling to Inform Policy 为政策提供信息的数学建模

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-08-01 DOI: 10.11159/icsta21.007

J. Gerardin

引用次数: 0

Using Routine Case Data to Quantify the Protective Effectiveness of Seasonal Malaria Chemoprevention in Burkina Faso 利用常规病例数据量化布基纳法索季节性疟疾化学预防的保护效果

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-08-01 DOI: 10.11159/icsta21.138

Sebastian Rodriguez, Beatriz Galatas, Cheick Saïd Compaoré, Boulaye Dao, I. Ozodiegwu, A. Noor, J. Gerardin, Noelle I. Samia

引用次数: 0

Graph Neural Network Emulation of Cardiac Mechanics 心脏力学的图神经网络仿真

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-07-29 DOI: 10.11159/icsta21.127

D. Dalton, Alan Lazarus, A. Rabbani, Hao Gao, D. Husmeier

引用次数: 5

Inference in Cardiovascular Modelling Subject to Medical Interventions 心血管模型受医疗干预的推论

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-07-29 DOI: 10.11159/icsta21.109

L. Paun, A. Borowska, M. Colebank, M. Olufsen, D. Husmeier

{"title":"Inference in Cardiovascular Modelling Subject to Medical Interventions","authors":"L. Paun, A. Borowska, M. Colebank, M. Olufsen, D. Husmeier","doi":"10.11159/icsta21.109","DOIUrl":"https://doi.org/10.11159/icsta21.109","url":null,"abstract":"Pulmonary hypertension (PH), i.e., high blood pressure in the lungs, is a serious medical condition that can damage the right ventricle of the heart and ultimately lead to heart failure. Standard diagnostic procedures are based on right-heart catheterization, which is an invasive technique that can potentially have serious side effects. Recent methodological advancements in fluid dynamics modelling of the pulmonary blood circulation system promise to mathematically predict the blood pressure based on non-invasive measurements of the blood flow. Thus, subsequent to PH diagnostication, further investigations would no longer require catheterization. However, in order for these alternative techniques to be applicable in the clinic, accurate model calibration and parameter estimation are paramount. Medical interventions taken to combat high blood pressure (as predicted from the mathematical model) alter the underlying cardiovascular physiology, thus interfering with the parameter estimation procedure. In the present study, we have carried out a series of cardiovascular simulations to assess the reliability of cardiovascular physiological parameter estimation in the presence of medical interventions. Our principal result is that if the closed-loop effect of medical interventions is accounted for, the model calibration provides accurate parameter estimates. This finding has important implications for the applicability of cardio-physiological modelling in the clinical practice.","PeriodicalId":403959,"journal":{"name":"Proceedings of the 3rd International Conference on Statistics: Theory and Applications","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121356389","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

Generalized Functional Responses in Habitat Selection Fitted by Decision Trees and Random Forests 决策树和随机森林拟合生境选择的广义功能响应

Proceedings of the 3rd International Conference on Statistics: Theory and Applications Pub Date : 2021-07-01 DOI: 10.11159/icsta21.125

Shaykhah Aldossari, D. Husmeier, Jason Matthiopoulos

{"title":"Generalized Functional Responses in Habitat Selection Fitted by Decision Trees and Random Forests","authors":"Shaykhah Aldossari, D. Husmeier, Jason Matthiopoulos","doi":"10.11159/icsta21.125","DOIUrl":"https://doi.org/10.11159/icsta21.125","url":null,"abstract":"Species Distribution Models (SDMs) are important regression tools in the ecological sciences that can support distribution predictions using different environmental variables. Most of the research in the area of SDMs has assumed that regression coefficients in these models are fixed. However, species respond differently to different habitats depending on the habitat availability, meaning that regression coefficients change as functions of habitat availability, a phenomenon known as a functional response in habitat selection. The generalized functional response (GFR) is a varying-coefficient extension of the basic SDM framework, designed for more robust forecasts of species distributions in a rapidly changing world. The original GFR model formulated the varying regression coefficients using a polynomial function approach, which led to improvements of forecasting performance in many applications. The purpose of this paper is to improve the out-of-sample performance of the GFR model using a decision tree and Breiman's random forest algorithm. We compare the original GFR model with a decision tree and random forests using the GFR model by applying both models to a real population dataset on house sparrows. The results revealed a noticeable improvement in terms of out-of-sample R² in the decision tree and the random forest approaches over the original GFR model.","PeriodicalId":403959,"journal":{"name":"Proceedings of the 3rd International Conference on Statistics: Theory and Applications","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127256802","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: 2