Robust regression approach to analyzing fisheries data

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. It is more common that errors do not follow the Gauss-Markov and normality assumptions. Outliers may arise due to heterogeneous 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, the authors explore the application of LMS in the analysis of fisheries data. -from Authors