Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship

Wang Yan-jun; Liu Qun

doi:10.1007/BF02842703

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Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship

OriginalPaper | Updated：2023-05-16

- Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship
- Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship
- 海洋湖沼学报（英文） 1999年17卷第1期页码：70-78
- Affiliations：
  
  Department of Fisheries
- Author bio：
- Funds：
  
  Project 39770584 supported by the NSFC.
- DOI：10.1007/BF02842703
  中图分类号：
- 纸质出版：1999
- Accepted：
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Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship[J]. 海洋湖沼学报（英文）, 1999,17(1):70-78.

Yan-jun, W., Qun, L. Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship., Chin. J. Ocean. Limnol. 17, 70–78 (1999). https://doi.org/10.1007/BF02842703
Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship[J]. 海洋湖沼学报（英文）, 1999,17(1):70-78. DOI： 10.1007/BF02842703.

Yan-jun, W., Qun, L. Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship., Chin. J. Ocean. Limnol. 17, 70–78 (1999). https://doi.org/10.1007/BF02842703 DOI：

摘要

Abstract

Analysis of stock-recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors

which usually result in a biased regression analysis. This paper presents a robust regression method

least median of squared orthogonal distance (LMD)

which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then

the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD-based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.

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references

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