Beyond Assumptions: How Bootstrap Techniques Transform Statistical Inference in Regression Models

Beyond Assumptions: How Bootstrap Techniques Transform Statistical Inference in Regression Models | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 16 July 2025 V1 Latest version Share on Beyond Assumptions: How Bootstrap Techniques Transform Statistical Inference in Regression Models Authors : Surya Rao Rayarao 0009-0001-8467-7865 [email protected] and Naga Donikena Authors Info & Affiliations https://doi.org/10.22541/au.175269775.58230855/v1 257 views 184 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Bootstrap methods have emerged as powerful tools for statistical inference in regression analysis, providing robust alternatives to traditional asymptotic approaches. This comprehensive survey examines the theoretical foundations and practical applications of bootstrap resampling techniques specifically within the context of regression models. We present a detailed analysis of various bootstrap approaches including residual bootstrap, pairs bootstrap, wild bootstrap, and parametric bootstrap, each tailored to address specific assumptions and challenges in regression analysis. The paper explores the theoretical properties of these methods, including consistency, asymptotic behavior, and finite-sample performance characteristics. Through extensive theoretical analysis and illustrative examples, we demonstrate how bootstrap methods effectively handle heteroscedasticity, model uncertainty, prediction intervals, and hypothesis testing in regression contexts. Our survey reveals that bootstrap techniques provide superior performance in many scenarios where traditional methods may fail, particularly in the presence of nonnormal errors, small sample sizes, and complex regression structures. The theoretical foundations establish bootstrap methods as essential tools for modern regression analysis, offering practitioners reliable inference procedures with minimal distributional assumptions. Supplementary Material File (714_beyond_assumptions_bootstrap_techniques_regression_models.pdf) Download 200.15 KB Information & Authors Information Version history V1 Version 1 16 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords bootstrap confidence intervals heteroscedasticity model selection prediction intervals regression analysis resampling statistical inference Authors Affiliations Surya Rao Rayarao 0009-0001-8467-7865 [email protected] Department of Statistics and Data Sciences Department of Computer Science, The University of Texas at Austin Austin View all articles by this author Naga Donikena Department of Statistics and Data Sciences Department of Computer Science, The University of Texas at Austin Austin View all articles by this author Metrics & Citations Metrics Article Usage 257 views 184 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Surya Rao Rayarao, Naga Donikena. Beyond Assumptions: How Bootstrap Techniques Transform Statistical Inference in Regression Models. Authorea . 16 July 2025. DOI: https://doi.org/10.22541/au.175269775.58230855/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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