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Analysis of institutional authors

Bielza, ConchaAuthorLarranaga, PedroAuthor

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June 9, 2019
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Lazy lasso for local regression

Publicated to: COMPUTATIONAL STATISTICS. 27 (3): 531-550 - 2012-09-01 27(3), DOI: 10.1007/s00180-011-0274-0

Authors:

Vidaurre, D; Bielza, C; Larrañaga, P
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Affiliations

Univ Politecn Madrid, Dept Inteligencia Artificial, Computat Intelligence Grp, Madrid, Spain - Author

Abstract

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios.
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Keywords

L1-regularizationLassoLazy lassoLeast angleLocally weighted regressionLoessModel selectionNonparametric variable selectionShrinkageSparseSparse modelsVariable selectionWeighted regression

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal COMPUTATIONAL STATISTICS, and although the journal is classified in the quartile Q4 (Agencia WoS (JCR)), its regional focus and specialization in Statistics & Probability, give it significant recognition in a specific niche of scientific knowledge at an international level.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-24:

  • Google Scholar: 21
  • WoS: 9
  • Scopus: 10
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 35.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 35 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/11002/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 527
  • Downloads: 1,015
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Vidaurre, Diego) and Last Author (LARRAÑAGA MUGICA, PEDRO MARIA).

the author responsible for correspondence tasks has been Vidaurre, Diego.

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Awards linked to the item

Research partially supported by the Spanish Ministry of Science and Innovation project TIN2010-20900-C04-04, Consolider Ingenio 2010-CSD2007-00018 and Cajal Blue Brain. We thank the anonymous referees and the associated editor for valuable comments about nonparametric variable selection and functional data analysis, which have definitely contributed to the improvement of this paper.
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