Using the EM algorithm to weight data sets of unknown precision when modeling fish stocks

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Stocks of commercial fish are often modelled using sampling data of various types, of unknown precision, and from various sources assumed independent. We want each set to contribute to estimates of the parameters in relation to its precision and goodness of fit with the model. Iterative re-weighting of the sets is proposed for linear models until the weight of each set is found to be proportional to (relative weighting) or equal to (absolute weighting) the set-specific residual invariances resulting from a generalised least squares fit. Formulae for the residual variances are put forward involving fractional allocation of degrees of freedom depending on the numbers of independent observations in each set, the numbers of sets contributing to the estimate of each parameter, and the number of weights estimated. To illustrate the procedure, numbers of the 1984 year-class of North Sea cod (a) landed commercially each year, and (b) caught per unit of trawling time by an annual groundfish survey are modelled as a function of age to estimate total mortality, Z, relative catching power of the two fishing methods, and relative precision of the two sets of observations as indices of stock abundance. It was found that the survey abundance indices displayed residual variance about 29 times higher than that of the annual landings. Crown Copyright (C) 2004 Published by Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalMathematical Biosciences
Volume190
Issue number1
DOIs
Publication statusPublished - Jul 2004

Keywords

  • iteratively weighted least squares
  • generalised least squares
  • fish stock assessment models
  • degrees of freedom
  • MAXIMUM-LIKELIHOOD

Fingerprint

Dive into the research topics of 'Using the EM algorithm to weight data sets of unknown precision when modeling fish stocks'. Together they form a unique fingerprint.

Cite this