[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122402-en":3,"doc-seo-122402-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122402,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Prediction and Monitoring with Machine Learning Under Complex Dependencies for Swine Production Data - Doctoral Dissertation","This dissertation develops and evaluates machine-learning methods for prediction and ongoing monitoring when data exhibit complex, structured dependencies. It introduces random-effects split conformal interval prediction to obtain reliable uncertainty quantification for learning algorithms, and proposes a Bayesian neural network with random effects for adaptive forecasting over time. For surveillance, it presents machine-learning control charts that handle serially correlated multivariate measurements in multi-process systems, demonstrated through a U.S. animal health case study and extensive simulation studies on swine production data.","Prediction and monitoring with machine learning under complex dependencies for  \nswine production data  \nby  \nDanyang Zhang  \nA dissertation submitted to the graduate faculty  \nin partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nMajor: Statistics  \nAcademic Plan Committee:  \nChong Wang, Major Professor  \nChunlin Li  \nDaniel C. L. Linhares  \nDaniel J. Nordman  \nHuaiqing Wu  \nThe student author, whose presentation of the scholarship herein was approved by the academic plan committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is  \nconferred.  \nIowa State University  \nAmes, Iowa  \n2025  \nCopyright © Danyang Zhang, 2025. All rights reserved.  \nii  \nDEDICATION  \nI would like to dedicate this thesis to my family without whose support I would not have been able to complete this work.  \niii  \nTABLE OF CONTENTS  \nPage  \nLIST OF TABLES .......................................... v  \n[LIST OF FIGURES ......................................... vi](LIST OF FIGURES ......................................... vi)  \n[ACKNOWLEDGMENTS .....................................](ACKNOWLEDGMENTS .....................................). viii  \nABSTRACT ............................................. ix  \nCHAPTER 1 . GENERAL INTRODUCTION .......................... 1  \n1.1 Background and motivation ................................ 1  \n1.2 Dissertation organization ................................. 2  \n1.3 References .......................................... 3  \nCHAPTER 2 . RANDOM-EFFECTS SPLIT CONFORMAL INTERVAL PREDICTION FOR MACHINE LEARNING ALGORITHMS ........................... 5  \n2.1 Abstract ........................................... 5  \n2.2 Introduction ......................................... 6  \n2.3 Methods ........................................... 9  \n2.4 Application to a Midwest swine production dataset ................... 11  \n2.5 Simulation study ...................................... 13  \n2.6 Conclusion and discussion ................................. 16  \n2.7 References .......................................... 23  \nCHAPTER 3. BAYESIAN NEURAL NETWORK (BNN) WITH RANDOM EFFECTS FOR ONGOING FORECASTING ............................... 25  \n3.1 Abstract ........................................... 25  \n3.2 Introduction ......................................... 26  \n3.3 Methods ........................................... 28  \n3.3.1 Building an initial BNN-RE model ........................ 28  \n3.3.2 Updating procedure ................................ 31  \n3.4 Application to a Midwest swine production dataset ................... 33  \n3.5 Simulation study ...................................... 37  \n3.6 Discussion and conclusion ................................. 39  \n3.7 References .......................................... 43  \nCHAPTER 4 . ONLINE MONITORING OF SERIALLY CORRELATED MULTIVARIATE DATA IN MULTI-PROCESS SYSTEMS USING MACHINE LEARNING CONTROL CHARTS: A CASE STUDY IN U.S. ANIMAL HEALTH SURVEILLANCE ........ 45  \niv  \n4.1 Abstract ........................................... 45  \n4.2 Introduction ......................................... 46  \n4.3 Methods ........................................... 48  \n4.3.1 Decorrelation .................................... 48  \n4.3.2 Process monitoring ................................. 51  \n4.4 Application to disease detection in a U.S. animal production system ......... 52  \n4.5 Simulation .......................................... 55  \n4.6 Constructing the DSVM-PD control chart for a new process .............. 57  \n4.7 Conclusion and discussion ................................. 59  \n4.8 References .......................................... 61  \nCHAPTER 5 . GENERAL CONCLUSION ............................ 64  \n5.1 Discussion and conclusion ................................. 64  \n5.2 Future work ......................................... 65","cbCailIR4i8cuOX2","https://ap.wps.com/l/cbCailIR4i8cuOX2","pdf",2422429,1,74,"English","en",105,"# Abstract\n# Chapter 1 - General Introduction\n## Background and motivation\n## Dissertation organization\n# Chapter 2 - Random-Effects Split Conformal Interval Prediction for Machine Learning Algorithms\n## Methods\n## Application to a Midwest swine production dataset\n## Simulation study\n# Chapter 3 - Bayesian Neural Network (BNN) with Random Effects for Ongoing Forecasting\n## Methods\n## Application to a Midwest swine production dataset\n## Simulation study\n# Chapter 4 - Online Monitoring of Serially Correlated Multivariate Data in Multi-Process Systems Using Machine Learning Control Charts\n## Methods\n## Case study in U.S. animal health surveillance\n## Constructing the DSVM-PD control chart\n# Chapter 5 - General Conclusion\n## Discussion and conclusion\n## Future work","[{\"question\":\"What problem does the dissertation address in swine production data?\",\"answer\":\"It addresses prediction and continuous monitoring when observations have complex, structured dependencies, including random effects and serial correlations across processes.\"},{\"question\":\"How does the dissertation produce prediction uncertainty for machine-learning models?\",\"answer\":\"It develops random-effects split conformal interval prediction methods that generate interval estimates with assessment through application and simulation on a swine production dataset.\"},{\"question\":\"What approach is used for ongoing forecasting and how is it updated?\",\"answer\":\"It proposes a Bayesian neural network with random effects (BNN-RE) and describes an updating procedure to revise the model as information accumulates over time.\"},{\"question\":\"How are online monitoring and disease detection handled in the animal health case study?\",\"answer\":\"It uses machine-learning control charts for serially correlated multivariate data, including decorrelation and process monitoring, and applies the resulting framework to disease detection in a U.S. animal production system.\"}]","Prediction and Monitoring with Machine Learning Under Complex Dependencies for Swine Production Data - Doctoral Dissertation | PDF",1785810454,186,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"prediction-and-monitoring-with-machine-learning-under-complex-dependencies-for-swine-production-data-doctoral-dissertation","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-and-monitoring-with-machine-learning-under-complex-dependencies-for-swine-production-data-doctoral-dissertation/122402/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the dissertation address in swine production data?","Question",{"text":75,"@type":76},"It addresses prediction and continuous monitoring when observations have complex, structured dependencies, including random effects and serial correlations across processes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation produce prediction uncertainty for machine-learning models?",{"text":80,"@type":76},"It develops random-effects split conformal interval prediction methods that generate interval estimates with assessment through application and simulation on a swine production dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach is used for ongoing forecasting and how is it updated?",{"text":84,"@type":76},"It proposes a Bayesian neural network with random effects (BNN-RE) and describes an updating procedure to revise the model as information accumulates over time.",{"name":86,"@type":73,"acceptedAnswer":87},"How are online monitoring and disease detection handled in the animal health case study?",{"text":88,"@type":76},"It uses machine-learning control charts for serially correlated multivariate data, including decorrelation and process monitoring, and applies the resulting framework to disease detection in a U.S. animal production system.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]