Proceedings of the 5th International Conference on Statistics: Theory and Applications最新文献_第2页

Product Quality Control Forecast Using Machine Learning Algorithms: a Case Study 使用机器学习算法的产品质量控制预测:一个案例研究

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.118

R. Arboretti, Elena Barzizza, Nicolò Biasetton, R. Ceccato, L. Corain, Marta Disegna, Luca Pegoraro, L. Salmaso, A. Vinelli, P. Barbieri, Luca Bortolan, Matteo Canale, Marco Giada, Davide Longi, Fabio Napol, Danny Paganin

引用次数: 0

Diagonal Vector Autoregressive and Multivariate Autoregressive Distributed Lag Models and Their Variance Properties 对角向量自回归和多元自回归分布滞后模型及其方差特性

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.112

A. Usoro, Emediong Udoh

引用次数: 0

Using Indicators of Entrepreneurial Ecosystem Quality for Segmenting Countries: A Biclustering Approach 基于双聚类方法的创业生态系统质量指标研究

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.133

Teodora Rajković, Milica Maričić, Ognjen Andjelic, Marina Ignjatović

引用次数: 0

Lipschitz Variational Approximation of Total Variation Distance 总变差距离的Lipschitz变分近似

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.138

R. Ding

引用次数: 1

PRECISION-LerVAD: A personalized algorithm to detect cardiac arrythmia and major bleeding in LVAD devices PRECISION-LerVAD:一种用于检测LVAD设备中心律失常和大出血的个性化算法

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.143

M. Moazeni, L. Numan, M. Szymanski, N. P. van der Kaaij, F. Asselbergs, L. V. van Laake, E. Aarts

引用次数: 0

Evaluating closed-loop effects from vasodilator administration for pulmonary hypertension treatment 评价血管扩张剂治疗肺动脉高压的闭环效应

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.162

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

引用次数: 1

FReET: Software for Uncertainties Propagation FReET:不确定性传播软件

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.150

D. Novák

引用次数: 0

Deep Kernel Learning based Gaussian Processes for Bayesian Image Regression Analysis 基于深度核学习的高斯过程贝叶斯图像回归分析

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.001

Jian Zhang

{"title":"Deep Kernel Learning based Gaussian Processes for Bayesian Image Regression Analysis","authors":"Jian Zhang","doi":"10.11159/icsta23.001","DOIUrl":"https://doi.org/10.11159/icsta23.001","url":null,"abstract":"In neuroimaging applications, different types of regression models have been widely adopted to study the complex associations between images and clinical variables, including scalar-on-image regression, image-on-scalar regression, and image-on-image regression. There are many challenging problems in model interpretations, statistical inferences and predictions in those type of models. To address those issues, we propose a general Bayesian modeling framework for the image regression problems by integrating deep neural networks (DNN) and Gaussian processes (GP) with kernel learning. The proposed framework consists of two levels of hierarchy. At level 1, we assume images as realizations of different GPs and project them on lower dimensional Euclidean spaces using a kernel expansion approach. We adopt a novel DNN based approach to covariance kernel learning of the GPs which provides efficient and accurate image projections. At level 2, we specify the associations between the projected images and other predictors using Bayesian DNNs. We develop efficient variational inference algorithms for posterior computation. We compare the performance of the proposed method with the state-of-the-art methods via extensive numerical experiments on synthetic images from the benchmark datasets as well as analysis of the fMRI data in the large-scale imaging studies.","PeriodicalId":179008,"journal":{"name":"Proceedings of the 5th International Conference on Statistics: Theory and Applications","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122368587","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

Forecasting Financial Times Series with Long Memory and Structural Break 预测具有长记忆和结构断裂的金融时报序列

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.152

E. F. Kouame, Lanzeni Tuo

引用次数: 0

Deception Detection using Random Forest-based Ensemble Learning 基于随机森林集成学习的欺骗检测

Proceedings of the 5th International Conference on Statistics: Theory and Applications Pub Date : 2023-08-01 DOI: 10.11159/icsta23.141

Kun Bu, K. Ramachandran

{"title":"Deception Detection using Random Forest-based Ensemble Learning","authors":"Kun Bu, K. Ramachandran","doi":"10.11159/icsta23.141","DOIUrl":"https://doi.org/10.11159/icsta23.141","url":null,"abstract":"- The purpose of this work is to detect people lying using different ensemble machine learning algorithms to conclude a better classification model through comparison. Random forest (RF) performed efficient work while dealing with both classification and regression problems. In this paper, we proposed random forest-based ensemble learning, which is the combination of RF with SVM, GLM, KNNs, and GBM to improve the model performance. The data set that we used to fit into the machine learning models is the Miami University Deception Detection Database (MU3D). MU3D is a free resource containing 320 videos of Black and White targets, female and male, telling truths and lies. We fit the MU3D video level data set into random forest-based ensemble learning models, which include RF+SVM. Linear, RF+SVM. Poly, RF+GLM, RF+KNNs, RF+GBM (stochastic gradient boosting) and RF+WSRF (weighted subspace random forest). As a comprehensive comparison of the model performance, we conclude that our new combination of algorithms performs better than the traditional machine learning models. Our contribution in this work provides a robust classification method that improves the predicted performance while avoiding model overfitting.","PeriodicalId":179008,"journal":{"name":"Proceedings of the 5th International Conference on Statistics: Theory and Applications","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314281","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