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Titlebook: Nonparametric Statistics; 4th ISNPS, Salerno, Michele La Rocca,Brunero Liseo,Luigi Salmaso Conference proceedings 2020 Springer Nature Swi

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書目名稱Nonparametric Statistics
副標(biāo)題4th ISNPS, Salerno,
編輯Michele La Rocca,Brunero Liseo,Luigi Salmaso
視頻videohttp://file.papertrans.cn/668/667833/667833.mp4
概述Presents the latest advances in nonparametric and semiparametric statistics.Addresses theory, methodology, applications, and computational aspects.Includes contributions on nonparametric curve estimat
叢書名稱Springer Proceedings in Mathematics & Statistics
圖書封面Titlebook: Nonparametric Statistics; 4th ISNPS, Salerno,  Michele La Rocca,Brunero Liseo,Luigi Salmaso Conference proceedings 2020 Springer Nature Swi
描述.Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held in Salerno, Italy, on June 11-15, 2018. It covers theory, methodology, applications and computational aspects, addressing topics such as nonparametric curve estimation, regression smoothing, models for time series and more generally dependent data, varying coefficient models, symmetry testing, robust estimation, and rank-based methods for factorial design. It also discusses nonparametric and permutation solutions for several different types of data, including ordinal data, spatial data, survival data and the joint modeling of both longitudinal and time-to-event data, permutation and resampling techniques, and practical applications of nonparametric statistics..The International Society for Nonparametric Statistics is a unique global organization, and its international conferences are intended to foster the exchange of ideas and the latest advances and trends among researchers from around the world and to develop and disseminate nonparametric sta
出版日期Conference proceedings 2020
關(guān)鍵詞nonparametric statistics; semiparametric statistics; dependent data; nonparametric curve estimation; per
版次1
doihttps://doi.org/10.1007/978-3-030-57306-5
isbn_softcover978-3-030-57308-9
isbn_ebook978-3-030-57306-5Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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書目名稱Nonparametric Statistics網(wǎng)絡(luò)公開度學(xué)科排名




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2194-1009 spects.Includes contributions on nonparametric curve estimat.Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held
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Low and High Resonance Components Restoration in Multichannel Data,algorithm. Numerical experiments show the performance of the proposed method in different synthetic scenarios highlighting the advantage of estimating the two components separately rather than together.
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An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models,and high dimensional, not rarely outstripping the number of patients included in the study. For this reason, sparse estimators are usually used in the study of high-dimensional survival data. In this paper, we propose an extension of the differential geometric least angle regression method to high-dimensional relative risk regression models.
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Permutation Tests for Multivariate Stratified Data: Synchronized or Unsynchronized Permutations?,ed independent we can think to perform permutation tests independently (unsynchronized) for each strata, and then combining the resulting p-values. In this work, we show that when strata are independent we can adopt equivalently both synchronized and unsynchronized permutations.
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