[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117968-en":3,"doc-seo-117968-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117968,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Use of machine learning techniques in non-probabilistic samples","Non-probabilistic surveys are increasingly adopted due to their low cost and ease of implementation, despite their limitations compared with probability sampling. When such sampling conditions are not satisfied, classical estimation methods become unsuitable, motivating new approaches. This work studies statistical matching for non-probabilistic samples and enhances it with XGBoost, assessing whether applying this machine-learning augmented estimation improves results on a real COVID-19 survey-derived variable of interest.","5th International Conference on Advanced Research Methods and Analytics (CARMA2023)  \nUniversidad de Sevilla, Sevilla, 2023  \nDOI: [http://dx.doi.org/10.4995/CARMA2023.2023.16416](http://dx.doi.org/10.4995/CARMA2023.2023.16416)  \nUse of machine learning techniques in non-probabilistic samples  \nJorge Rueda1, Beatriz Cobo2, Luis Castro2  \n1Department of Statistics and Operations Research, University of Granada, Spain, 2Department of Quantitative Methods for Economics and Business, University of Granada, Spain.  \nAbstract  \nNon-probabilistic surveys are increasingly used because they are easy and cheap to carry out. Even official statistical agencies are starting to use this type of surveys in their research, due to the difficulty and the amount of resources needed to carry out probabilistic surveys, which are currently the best option due to their reliability. When non-probabilistic surveys are used, the classical estimation methods cannot be used since the initial conditions for carrying them out are not met, so over the years new estimation techniques have been emerging in this type of sampling. Some of the most relevant estimation techniques currently being used are those related to machine learning techniques.  \nIn this work we focus on the estimation technique for non-probabilistic samples statistical matching, which can be enhanced and improved if we complement it with a machine learning technique known as XGBoost. We are going to study a variable of interest extracted from a real non-probabilistic survey carried out during the COVID-19 pandemic, and check if by applying such estimations we obtain better results than without applying this type of techniques.  \nKeywords: Machine learning; non-probabilistic sampling; statistical matching; XGBoost.  \nThis work is licensed under a Creative Commons License CC BY-NC-SA 4.0  \nEditorial Universitat Politcnica de Valncia 241  \n1. Introduction  \nThe major strength of probability sampling is that the probability selection mechanism permits the development of statistical theory to examine the properties of sample estimators. The weakness of all nonprobability methods is that no such theoretical development is possible; as a consequence, nonprobability samples can be assessed only by subjective valuation (Kalton, 1983) . Over the years the development of non-probabilistic surveys has boomed and many techniques have been developed to calculate reliable estimates from non-probabilistic survey data.  \nMany advanced in artificial intelligence models, such as deep learning techniques, have shown remarkable accuracy in prediction. Artificial intelligence models perform poorly when dealing with relatively small data sets, while machine learning models have good predictive performance on smaller data sets. However, a single machine learning approach often leads to overfitting and difficulty handling the large number of imbalanced data sets that occur in real world problems. To make up for the shortcomings of a single machine learning method, the conjoint learning technique based on the GBDT (Gradient Boost Decision Tree) algorithm was developed and has gradually become the mainstream approach in the field of learning research automatic. eXtreme Gradient Boosting (XGBoost) is a highly efficient booster set learning model originated from the decision tree model, which uses the tree classifier for better prediction results and higher operational efficiency.  \nThis technique has been used in many settings, for example Li and Yao (2018) classify gene mutations using machine learning models, XGBoost and SVM, in the hope of improving gene mutation classification performance. In terms of performance of the two qualifying models, XGBoost outperformed SVM. From the confounding metrics, it could be seen that XGBoost had better predictive ability, especially for those with enough classes featured. Liu et al. (2021) used a mortality prediction model using the XGBoost decision tree model for patients with ","cbCaifDIeYiTSa5K","https://ap.wps.com/l/cbCaifDIeYiTSa5K","pdf",409031,1,7,"English","en",105,"# Introduction\n## Strengths and limitations of probability vs non-probability sampling\n## Machine learning and XGBoost background\n## Related work and applications","[{\"question\":\"Why are non-probabilistic surveys used despite their limitations?\",\"answer\":\"They are easier and cheaper to carry out, and even official statistical agencies increasingly use them. Their weakness is that classical theoretical evaluation of estimators is not available.\"},{\"question\":\"What estimation approach does the work focus on?\",\"answer\":\"The study focuses on statistical matching for non-probabilistic samples and improves it by complementing it with XGBoost.\"},{\"question\":\"How is XGBoost evaluated in this study?\",\"answer\":\"The authors extract a variable of interest from a real non-probabilistic COVID-19 survey and compare results obtained with the enhanced estimation technique against results without using these techniques.\"}]","Use of machine learning techniques in non-probabilistic samples | PDF",1785680578,18,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"use-of-machine-learning-techniques-in-non-probabilistic-samples","",{"@graph":36,"@context":85},[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/use-of-machine-learning-techniques-in-non-probabilistic-samples/117968/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are non-probabilistic surveys used despite their limitations?","Question",{"text":75,"@type":76},"They are easier and cheaper to carry out, and even official statistical agencies increasingly use them. Their weakness is that classical theoretical evaluation of estimators is not available.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What estimation approach does the work focus on?",{"text":80,"@type":76},"The study focuses on statistical matching for non-probabilistic samples and improves it by complementing it with XGBoost.",{"name":82,"@type":73,"acceptedAnswer":83},"How is XGBoost evaluated in this study?",{"text":84,"@type":76},"The authors extract a variable of interest from a real non-probabilistic COVID-19 survey and compare results obtained with the enhanced estimation technique against results without using these techniques.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]