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Effective Sample Size for Line Transect Sampling Models with an Application to Marine Macroalgae

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  • Jonathan Acosta

    (Universidad Técnica Federico Santa María)

  • Felipe Osorio

    (Pontificia Universidad Católica de Valparaíso)

  • Ronny Vallejos

    (Universidad Técnica Federico Santa María)

Abstract

This paper provides a framework for estimating the effective sample size in a spatial regression model context when the data have been sampled using a line transect scheme and there is an evident serial correlation due to the chronological order in which the observations were collected. We propose a linear regression model with a partially linear covariance structure to address the computation of the effective sample size when spatial and serial correlations are present. A recursive algorithm is described to separately estimate the linear and nonlinear parameters involved in the covariance structure. The kriging equations are also presented to explore the kriging variance between our proposal and a typical spatial regression model. An application in the context of marine macroalgae, which motivated the present work, is also presented.

Suggested Citation

  • Jonathan Acosta & Felipe Osorio & Ronny Vallejos, 2016. "Effective Sample Size for Line Transect Sampling Models with an Application to Marine Macroalgae," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 407-425, September.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:3:d:10.1007_s13253-016-0252-7
    DOI: 10.1007/s13253-016-0252-7
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    References listed on IDEAS

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    1. Faes, Christel & Molenberghs, Geert & Aerts, Marc & Verbeke, Geert & Kenward, Michael G., 2009. "The Effective Sample Size and an Alternative Small-Sample Degrees-of-Freedom Method," The American Statistician, American Statistical Association, vol. 63(4), pages 389-399.
    2. Crujeiras, Rosa M. & Van Keilegom, Ingrid, 2010. "Least squares estimation of nonlinear spatial trends," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 452-465, February.
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    Cited by:

    1. J. Mateu & E. Porcu, 2016. "Guest Editors’ Introduction to the Special Issue on “Seismomatics: Space–Time Analysis of Natural or Anthropogenic Catastrophes”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 403-406, September.
    2. Letícia Ellen Dal Canton & Luciana Pagliosa Carvalho Guedes & Miguel Angel Uribe-Opazo, 2021. "Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size," Journal of Agricultural Studies, Macrothink Institute, vol. 9(1), pages 357-376, June.

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