Environmental and Ecological Statistics

, Volume 24, Issue 1, pp 39–68 | Cite as

Composite likelihood approach to the regression analysis of spatial multivariate ordinal data and spatial compositional data with exact zero values

  • Xiaoping FengEmail author
  • Jun Zhu
  • Pei-Sheng Lin
  • Michelle M. Steen-Adams


In many environmental and ecological studies, it is of interest to model compositional data. One approach is to consider positive random vectors that are subject to a unit-sum constraint. In landscape ecological studies, it is common that compositional data are also sampled in space with some elements of the composition absent at certain sampling sites. In this paper, we first propose a practical spatial multivariate ordered probit model for multivariate ordinal data, where the response variables can be viewed as the discretized non-negative compositions without the unit-sum constraint. We then propose a novel two-stage spatial mixture Dirichlet regression model. The first stage models the spatial dependence and the presence of exact zero values, and the second stage models all the non-zero compositional data. A maximum composite likelihood approach is developed for parameter estimation and inference in both the spatial multivariate ordered probit model and the two-stage spatial mixture Dirichlet regression model. The standard errors of the parameter estimates are computed by an estimate of the Godambe information matrix. A simulation study is conducted to evaluate the performance of the proposed models and methods. A land cover data example in landscape ecology further illustrates that accounting for spatial dependence can improve the accuracy in the prediction of presence/absence of different land covers as well as the magnitude of land cover compositions.


Dirichlet regression model Gaussian latent variable Godambe information Mixture model Multivariate ordered probit model Spatial prediction 



Funding has been provided for this research from a USDA Cooperative State Research, Education and Extension Service (CSREES) McIntire-Stennis project and the National Science Foundation PalEON MacroSystems Biology under grant no. DEB1241868. The authors thank Dr. Mark D.O. Adams for database development assistance. We also thank the co-editor, an associate editor, and three anonymous referees for constructive comments that improved the content and presentation of this paper.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Xiaoping Feng
    • 1
    Email author
  • Jun Zhu
    • 1
    • 2
  • Pei-Sheng Lin
    • 3
  • Michelle M. Steen-Adams
    • 4
  1. 1.Department of StatisticsUniversity of WisconsinMadisonUSA
  2. 2.Department of EntomologyUniversity of WisconsinMadisonUSA
  3. 3.Division of Biostatistics and BioinformaticsNational Health Research InstitutesZhunanTaiwan
  4. 4.Department of Environmental StudiesUniversity of New EnglandBiddefordUSA

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