Journal ArticleDOI
Robust Regression Approach to Analyzing Fisheries Data
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A robust regression method, least median squares (LMS), is insensitive to atypical values in the dependent and/or independent variables in a regression analysis, and outliers that have significantly different variances from the rest of the data can be identified in a residual analysis.Abstract:
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 analys...read more
Citations
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Journal ArticleDOI
Spatial isolation and fish communities in drainage lakes.
TL;DR: A high concordance was revealed between patterns in fish community composition and lake isolation and lake morphology at the watershed scale, suggesting that insular and habitat-related factors influence the structure of fish communities.
Virtual population analysis - a practical manual for stock assessment
TL;DR: The numerical techniques which can be used to fit the model based on weighted least-squares, which is the basis for the ADAPT approach are described so that they are readily implemented in a spreadsheet.
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Robust principal component analysis and outlier detection with ecological data
Donald A. Jackson,Yong Chen +1 more
TL;DR: This study provides a comparison of a standard method,based on the Mahalanobis distance, used in multivariate approaches to a robust method based on the minimum volume ellipsoid as a means of determining whether data sets contain outliers or not, and suggests that ecologists consider that their data may contain atypical points.
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A stochastic surplus production model in continuous time
TL;DR: In this paper, a stochastic surplus production model in continuous time (SPiCT) is proposed to model the dynamics of the fishery in a state-space model.
Journal ArticleDOI
Impacts of outliers and mis-specification of priors on Bayesian fisheries-stock assessment
Y Chen,P A Breen,N L Andrew +2 more
TL;DR: This work evaluates the robustness of three likelihood functions and two prior-distribution functions, with respect to outliers and mis-specification of priors, for a length-structured stock-assessment model for a New Zealand abalone fishery in 48 different combinations.
References
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Journal ArticleDOI
Least Median of Squares Regression
TL;DR: In this paper, the median of the squared residuals is used to resist the effect of nearly 50% of contamination in the data in the special case of simple least square regression, which corresponds to finding the narrowest strip covering half of the observations.
Journal ArticleDOI
Robust regression using iteratively reweighted least-squares
Paul W. Holland,Roy E. Welsch +1 more
TL;DR: The ROSEPACK (RObust Statistical Estimation PACKAGE) as mentioned in this paper was developed by the authors and Virginia Klema at the Computer Research Center of the National Bureau of Economic Research, Inc. in Cambridge, Mass.
Book ChapterDOI
Robust regression by means of s-estimators
TL;DR: A class of methods for robust regression is developed, based on estimators of scale, that are introduced because of their invulnerability to large fractions of contaminated data and are proposed to be called “S-estimators”.
Book
Regression Analysis: Theory, Methods, and Applications
Ashish Sen,Muni S. Srivastava +1 more
TL;DR: An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications.