{"title":"偏差与平均绝对偏差之间权衡的下限","authors":"Alexis Derumigny , Johannes Schmidt-Hieber","doi":"10.1016/j.spl.2024.110182","DOIUrl":null,"url":null,"abstract":"<div><p>In nonparametric statistics, rate-optimal estimators typically balance bias and stochastic error. The recent work on overparametrization raises the question whether rate-optimal estimators exist that do not obey this trade-off. In this work we consider pointwise estimation in the Gaussian white noise model with regression function <span><math><mi>f</mi></math></span> in a class of <span><math><mi>β</mi></math></span>-Hölder smooth functions. Let ’worst-case’ refer to the supremum over all functions <span><math><mi>f</mi></math></span> in the Hölder class. It is shown that any estimator with worst-case bias <span><math><mrow><mo>≲</mo><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mi>β</mi><mo>/</mo><mrow><mo>(</mo><mn>2</mn><mi>β</mi><mo>+</mo><mn>1</mn><mo>)</mo></mrow></mrow></msup><mo>≕</mo><msub><mrow><mi>ψ</mi></mrow><mrow><mi>n</mi></mrow></msub></mrow></math></span> must necessarily also have a worst-case mean absolute deviation that is lower bounded by <span><math><mrow><mo>≳</mo><msub><mrow><mi>ψ</mi></mrow><mrow><mi>n</mi></mrow></msub><mo>.</mo></mrow></math></span> To derive the result, we establish abstract inequalities relating the change of expectation for two probability measures to the mean absolute deviation.</p></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"213 ","pages":"Article 110182"},"PeriodicalIF":0.9000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167715224001512/pdfft?md5=51662c001093a4b55185099a04b71ff2&pid=1-s2.0-S0167715224001512-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Lower bounds for the trade-off between bias and mean absolute deviation\",\"authors\":\"Alexis Derumigny , Johannes Schmidt-Hieber\",\"doi\":\"10.1016/j.spl.2024.110182\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In nonparametric statistics, rate-optimal estimators typically balance bias and stochastic error. The recent work on overparametrization raises the question whether rate-optimal estimators exist that do not obey this trade-off. In this work we consider pointwise estimation in the Gaussian white noise model with regression function <span><math><mi>f</mi></math></span> in a class of <span><math><mi>β</mi></math></span>-Hölder smooth functions. Let ’worst-case’ refer to the supremum over all functions <span><math><mi>f</mi></math></span> in the Hölder class. It is shown that any estimator with worst-case bias <span><math><mrow><mo>≲</mo><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mi>β</mi><mo>/</mo><mrow><mo>(</mo><mn>2</mn><mi>β</mi><mo>+</mo><mn>1</mn><mo>)</mo></mrow></mrow></msup><mo>≕</mo><msub><mrow><mi>ψ</mi></mrow><mrow><mi>n</mi></mrow></msub></mrow></math></span> must necessarily also have a worst-case mean absolute deviation that is lower bounded by <span><math><mrow><mo>≳</mo><msub><mrow><mi>ψ</mi></mrow><mrow><mi>n</mi></mrow></msub><mo>.</mo></mrow></math></span> To derive the result, we establish abstract inequalities relating the change of expectation for two probability measures to the mean absolute deviation.</p></div>\",\"PeriodicalId\":49475,\"journal\":{\"name\":\"Statistics & Probability Letters\",\"volume\":\"213 \",\"pages\":\"Article 110182\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0167715224001512/pdfft?md5=51662c001093a4b55185099a04b71ff2&pid=1-s2.0-S0167715224001512-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistics & Probability Letters\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167715224001512\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics & Probability Letters","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167715224001512","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
摘要
在非参数统计中,最优估计率通常会兼顾偏差和随机误差。最近关于超参数化的研究提出了一个问题:是否存在不服从这种权衡的最优估计率?在本研究中,我们将考虑在高斯白噪声模型中,用一类 β-Hölder 平滑函数中的回归函数 f 进行点估计。让 "最坏情况 "指的是 Hölder 类中所有函数 f 的上集。结果表明,任何具有最坏情况偏差≲n-β/(2β+1)≕ψn 的估计器,其最坏情况平均绝对偏差的下限必然也是≳ψn。为了推导出这一结果,我们建立了有关两个概率度量的期望变化与平均绝对偏差的抽象不等式。
Lower bounds for the trade-off between bias and mean absolute deviation
In nonparametric statistics, rate-optimal estimators typically balance bias and stochastic error. The recent work on overparametrization raises the question whether rate-optimal estimators exist that do not obey this trade-off. In this work we consider pointwise estimation in the Gaussian white noise model with regression function in a class of -Hölder smooth functions. Let ’worst-case’ refer to the supremum over all functions in the Hölder class. It is shown that any estimator with worst-case bias must necessarily also have a worst-case mean absolute deviation that is lower bounded by To derive the result, we establish abstract inequalities relating the change of expectation for two probability measures to the mean absolute deviation.
期刊介绍:
Statistics & Probability Letters adopts a novel and highly innovative approach to the publication of research findings in statistics and probability. It features concise articles, rapid publication and broad coverage of the statistics and probability literature.
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