{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T04:08:06Z","timestamp":1747368486102,"version":"3.40.5"},"reference-count":0,"publisher":"University of Florida George A Smathers Libraries","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["FLAIRS"],"abstract":"<jats:p>Regression analysis is a cornerstone of predictive modeling, with linear regression and kernel regression standing as two of its most prominent paradigms.\u00a0However, each approach has inherent limitations: linear regression is highly susceptible to outliers in noisy and unevenly distributed datasets, while kernel regression often suffers from overfitting.\u00a0When data exhibits linear trends, linear regression tends to generalize better, whereas kernel regression may fail to capture the broader patterns effectively.\u00a0To address these challenges, we propose a novel methodology that integrates linear regression principles with kernel density estimation, termed Kernel Density Estimated Linear Regression (KDLR).\u00a0This approach leverages kernel density estimation to assign higher weights to data points in dense regions, simultaneously de-emphasizing the influence of sparse or noisy points.\u00a0This dynamic weighting mechanism enhances robustness and improves the model\u2019s ability to recognize meaningful patterns.\u00a0Extensive evaluations on datasets, including the California housing dataset with varying levels of outliers, demonstrate the efficacy of KDLR.\u00a0Using the mean squared error metric, KDLR consistently achieves superior accuracy compared to traditional regression methods.\u00a0By prioritizing dense data regions, it captures significant trends while mitigating the effects of noise.\u00a0Applications of KDLR span diverse fields, including stock price prediction in finance, patient data modeling in healthcare, and climate modeling in environmental studies - domains where robust anomaly and noise handling are critical.\u00a0This research establishes KDLR as a transformative tool for predictive analytics, offering a compelling blend of precision, resilience, and adaptability.\u00a0Its ability to manage noisy data effectively while identifying critical patterns positions KDLR as a versatile and innovative solution for both academic research and industrial practice, with the potential to redefine best practices in regression modeling.<\/jats:p>","DOI":"10.32473\/flairs.38.1.139000","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T15:20:51Z","timestamp":1747322451000},"source":"Crossref","is-referenced-by-count":0,"title":["Kernel Density Estimated Linear Regression"],"prefix":"10.32473","volume":"38","author":[{"given":"Roshan","family":"Kalpavruksha","sequence":"first","affiliation":[]},{"given":"Rohan","family":"Kalpavruksha","sequence":"additional","affiliation":[]},{"given":"Teryn","family":"Cha","sequence":"additional","affiliation":[]},{"given":"Sung-Hyuk","family":"Cha","sequence":"additional","affiliation":[]}],"member":"17357","published-online":{"date-parts":[[2025,5,14]]},"container-title":["The International FLAIRS Conference Proceedings"],"original-title":[],"link":[{"URL":"https:\/\/journals.flvc.org\/FLAIRS\/article\/download\/139000\/144091","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.flvc.org\/FLAIRS\/article\/download\/139000\/144091","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T15:20:51Z","timestamp":1747322451000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.flvc.org\/FLAIRS\/article\/view\/139000"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.32473\/flairs.38.1.139000","relation":{},"ISSN":["2334-0762","2334-0754"],"issn-type":[{"value":"2334-0762","type":"electronic"},{"value":"2334-0754","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}