Regression for overdetermined systems: A fisheries example

Abstract In trying to establish the relationship between a yearly fisheries recruitment series and meteorological or oceanographic variables such as air pressure or sea surface temperature, we are often faced with the situation where the number of regressors exceeds the number of observations. In th...

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Published in:Canadian Journal of Statistics
Main Authors: Manchester, L., Field, C. A., Mcdougall, A.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 1999
Subjects:
Online Access:http://dx.doi.org/10.2307/3315488
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spelling crwiley:10.2307/3315488 2023-12-03T10:18:59+01:00 Regression for overdetermined systems: A fisheries example Manchester, L. Field, C. A. Mcdougall, A. 1999 http://dx.doi.org/10.2307/3315488 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.2307%2F3315488 https://onlinelibrary.wiley.com/doi/pdf/10.2307/3315488 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Canadian Journal of Statistics volume 27, issue 1, page 25-39 ISSN 0319-5724 1708-945X Statistics, Probability and Uncertainty Statistics and Probability journal-article 1999 crwiley https://doi.org/10.2307/3315488 2023-11-09T13:54:39Z Abstract In trying to establish the relationship between a yearly fisheries recruitment series and meteorological or oceanographic variables such as air pressure or sea surface temperature, we are often faced with the situation where the number of regressors exceeds the number of observations. In this paper we use the techniques of penalized least squares and principal‐components regression to determine whether air pressure over the North Atlantic can be used to predict two North Atlantic cod recruitment series. The results suggest that penalized least squares can be very effective in these situations. Article in Journal/Newspaper atlantic cod North Atlantic Wiley Online Library (via Crossref) Canadian Journal of Statistics 27 1 25 39
institution Open Polar
collection Wiley Online Library (via Crossref)
op_collection_id crwiley
language English
topic Statistics, Probability and Uncertainty
Statistics and Probability
spellingShingle Statistics, Probability and Uncertainty
Statistics and Probability
Manchester, L.
Field, C. A.
Mcdougall, A.
Regression for overdetermined systems: A fisheries example
topic_facet Statistics, Probability and Uncertainty
Statistics and Probability
description Abstract In trying to establish the relationship between a yearly fisheries recruitment series and meteorological or oceanographic variables such as air pressure or sea surface temperature, we are often faced with the situation where the number of regressors exceeds the number of observations. In this paper we use the techniques of penalized least squares and principal‐components regression to determine whether air pressure over the North Atlantic can be used to predict two North Atlantic cod recruitment series. The results suggest that penalized least squares can be very effective in these situations.
format Article in Journal/Newspaper
author Manchester, L.
Field, C. A.
Mcdougall, A.
author_facet Manchester, L.
Field, C. A.
Mcdougall, A.
author_sort Manchester, L.
title Regression for overdetermined systems: A fisheries example
title_short Regression for overdetermined systems: A fisheries example
title_full Regression for overdetermined systems: A fisheries example
title_fullStr Regression for overdetermined systems: A fisheries example
title_full_unstemmed Regression for overdetermined systems: A fisheries example
title_sort regression for overdetermined systems: a fisheries example
publisher Wiley
publishDate 1999
url http://dx.doi.org/10.2307/3315488
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.2307%2F3315488
https://onlinelibrary.wiley.com/doi/pdf/10.2307/3315488
genre atlantic cod
North Atlantic
genre_facet atlantic cod
North Atlantic
op_source Canadian Journal of Statistics
volume 27, issue 1, page 25-39
ISSN 0319-5724 1708-945X
op_rights http://onlinelibrary.wiley.com/termsAndConditions#vor
op_doi https://doi.org/10.2307/3315488
container_title Canadian Journal of Statistics
container_volume 27
container_issue 1
container_start_page 25
op_container_end_page 39
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