Análisis de covarianzas con variables secundarias correlacionadas espacialmente

Translated title of the contribution: Analysis of covariance with spatially correlated secondary variables

Tisha Hooks, David Marx, Stephen Kachman, Jeffrey Pedersen, Roger Eigenberg

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Advances in precision agriculture allow researchers to capture data more frequently and in more detail. For example, it is typical to collect "on-the-go" data such as soil electrical conductivity readings. This creates the opportunity to use these measurements as covariates for the primary response variable to possibly increase experimental precision. Moreover, these measurements are also spatially referenced to one another, creating the need for methods in which spatial locations play an explicit role in the analysis of the data. Data sets which contain measurements on a spatially referenced response and covariate are analyzed using either cokriging or spatial analysis of covariance. While cokriging accounts for the correlation structure of the covariate, it is purely a predictive tool. Alternatively, spatial analysis of covariance allows for parameter estimation yet disregards the correlation structure of the covariate. A method is proposed which both accounts for the correlation in and between the response and covariate and allows for the estimation of model parameters; also, this method allows for analysis of covariance when the response and covariate are not colocated.

Translated title of the contributionAnalysis of covariance with spatially correlated secondary variables
Original languageSpanish
Pages (from-to)95-109
Number of pages15
JournalRevista Colombiana de Estadistica
Volume31
Issue number1
StatePublished - Jun 2008

Keywords

  • Cokriging
  • Covariance Analysis
  • Covariate
  • Spatial Analysis

ASJC Scopus subject areas

  • Statistics and Probability

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