Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide

曹阳; 李玫; 谢万军; 张罗漫; 吴宗贵

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Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide

Updated：2026-03-12

- Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide
- Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide
- 解放军医学杂志（英文版） 2003年第2期页码：124-128
- Affiliations：
  
  1. Department of Health Statistics
  2. Second Military Medical University
  3. ,Switzerland
  4. Department of Cardiovasology
  5. Changzheng Hospital
  6. Classification
  7. Assessment and Survey Unit
  8. World Health Organization
  9. Geneva
- Author bio：
- Funds：
- DOI：
  中图分类号： R195
- 纸质出版：2003
- Accepted：
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Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide[J]. 解放军医学杂志（英文版）, 2003,(2):124-128.

[1]曹阳,李玫,谢万军,张罗漫,吴宗贵.Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide[J].Journal of Medical Colleges of PLA,2003(02):124-128.
Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide[J]. 解放军医学杂志（英文版）, 2003,(2):124-128. DOI：

[1]曹阳,李玫,谢万军,张罗漫,吴宗贵.Bootstrapping estimates of regression analysis for a non-random sample and its application in the research on anti-proliferation effects of triptolide[J].Journal of Medical Colleges of PLA,2003(02):124-128. DOI：

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Abstract

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Objective: To solve the problem of parameter estimate in the regression analysis of non-random sample.Methods: Calculating residuals according to the regression function based on original data. Modifying residuals andcorrecting them with mean. Adding mean-corrected residuals on original response and bootstrapping them to get 1000samples. Fitting regression functions of 1000 resampling samples and calculating the 2.5

percentile and 97.5

percen-tile of corresponding coefficient. Results: The interval estimates deriving from boostrap method had more statistical

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