ROBUST REGRESSION APPROACH TO ANALYZING FISHERIES DATA

被引：25

作者：

CHEN, Y ^{[1
]}

JACKSON, DA ^{[1
]}

PALOHEIMO, JE ^{[1
]}

机构：

[1] UNIV WESTERN ONTARIO, DEPT ZOOL, LONDON N6A 5B7, ON, CANADA

来源：

CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES | 1994年 / 51卷 / 06期

关键词：

D O I：

10.1139/f94-142

中图分类号：

S9 [水产、渔业];

学科分类号：

0908 ;

摘要：

Fisheries data often contain inaccuracies due to various errors. If such errors meet the Gauss-Markov conditions and the normality assumption, strong theoretical justification can be made for traditional least-squares (LS) estimates. However, these assumptions are not always met. Rather, it is more common that errors do not follow the Gauss-Markov and normality assumptions. Outliers may arise due to heterogenous variabilities. This results in a biased regression analysis. The sensitivity of the LS regression analysis to atypical values in the dependent and/or independent variables makes it difficult to identify outliers in a residual analysis. A robust regression method, least median squares (LMS), is insensitive to atypical values in the dependent and/or independent variables in a regression analysis. Thus, outliers that have significantly different variances from the rest of the data can be identified in a residual analysis. Using simulated and field data, we explore the application of LMS in the analysis of fisheries data. A two-step procedure is suggested in analyzing fisheries data.

引用

页码：1420 / 1429

页数：10