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Titlebook: Emerging Topics in Modeling Interval-Censored Survival Data; Jianguo Sun,Ding-Geng Chen Book 2022 The Editor(s) (if applicable) and The Au

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發(fā)表于 2025-3-21 16:05:02 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Emerging Topics in Modeling Interval-Censored Survival Data
編輯Jianguo Sun,Ding-Geng Chen
視頻videohttp://file.papertrans.cn/309/308479/308479.mp4
概述Explores the historical development of interval-censored survival data analysis.Systematically discusses the emerging methodologies used to analyze interval-censored data.Details R/SAS implementations
叢書名稱ICSA Book Series in Statistics
圖書封面Titlebook: Emerging Topics in Modeling Interval-Censored Survival Data;  Jianguo Sun,Ding-Geng Chen Book 2022 The Editor(s) (if applicable) and The Au
描述.This book primarily aims to discuss emerging topics in statistical methods and to booster research, education, and training to advance statistical modeling on interval-censored survival data. Commonly collected from public health and biomedical research, among other sources, interval-censored survival data can easily be mistaken for typical right-censored survival data, which can result in erroneous statistical inference due to the complexity of this type of data. The book invites a group of internationally leading researchers to systematically discuss and explore the historical development of the associated methods and their computational implementations, as well as emerging topics related to interval-censored data. It covers a variety of topics, including univariate interval-censored data, multivariate interval-censored data, clustered interval-censored data, competing risk interval-censored data, data with interval-censored covariates, interval-censored data from electric medical records, and misclassified interval-censored data. Researchers, students, and practitioners can directly make use of the state-of-the-art methods covered in the book to tackle their problems in researc
出版日期Book 2022
關(guān)鍵詞Survival Analysis; Censored Data; Predictive Model; Confidence Interval; Hazards Model; Case-cohort Studi
版次1
doihttps://doi.org/10.1007/978-3-031-12366-5
isbn_softcover978-3-031-12368-9
isbn_ebook978-3-031-12366-5Series ISSN 2199-0980 Series E-ISSN 2199-0999
issn_series 2199-0980
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Non-linear algebraic equations,rt of this chapter, we introduce a general goodness-of-fit test procedure for copula-based interval-censored data using the information ratio (IR). It can be applied to any copula family with a parametric form, such as the frequently used Archimedean and Gaussian families. Finally, we present an R p
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Linear Integral Equations in One Variable,ced number of unknown parameters. In this project, we will illustrate the key characteristics of the sieve nonparametric maximum likelihood estimation with emphasis on numerical computation. We will develop an R-based software to facilitate the public use for computing the spline-based sieve nonpara
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Ordinary Differential Equations,mitations of the methods and equipment. Up to this date, there is no consensus as to how data under the limit of quantitation or even detection should be treated..In this chapter, we treat the concentration of these compounds as interval-censored random variables with lower and upper limits given by
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