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ARS Home » Plains Area » Lincoln, Nebraska » Wheat, Sorghum and Forage Research » Research » Publications at this Location » Publication #200759
Title: ANALYSIS OF COVARIANCE WITH SPATIALLY CORRELATED SECONDARY VARIABLES

Author
item HOOKS, TISHA - UNIVERSITY OF NEBRASKA
item MARX, DAVID - UNIVERISTY OF NEBRASKA
item KACHMAN, STEPHEN - UNIVERSITY OF NEBRASKA
item Pedersen, Jeffrey
item Eigenberg, Roger

Submitted to: Columbian Journal of Statistics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/2/2008
Publication Date: 6/1/2008
Citation: Hooks, T., Marx, D.B., Kachman, S.D., Pedersen, J.F., Eigenberg, R.A. 2008. Analysis of covariance with spatially correlated secondary variables. Revista Colombiana de Estad istica 31:95-109. 2008

Interpretive Summary: This paper presents a new method for analysis of covariance with a spatial covariate. Data sets which contain measurements on a spatially referenced response and covariate are analyzed using either cokriging or spatial analysis of covariance. While co-kriging 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 measured at the same spatial location.

Technical Abstract: Data sets which contain measurements on a spatially referenced response and covariate are analyzed using either co-kriging or spatial analysis of covariance. While co-kriging 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 co-located.