Minimum distance inference in unilateral autoregressive lattice processes

Marc Genton, Hira L. Koul

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


This paper discusses two classes of minimum distance estimators of the underlying parameters and their robust variants in unilateral autoregressive lattice models. The paper also contains an asymptotically distribution free test for symmetry of the error distribution and a goodness-of-fit test for fitting an error distribution. A lack-of-fit test for the hypothesis that the given process is doubly geometric based on the least absolute deviation residuals is also briefly analyzed. A simulation study that investigates some small sample properties of the proposed estimators and their robustness is included. It shows that some of the proposed estimators are more efficient than the least squares estimator at non-normal error distributions. We also study the empirical level and power of the test of a doubly geometric process at various error distributions. The proposed methodology is then applied to a data set of yields from an agricultural experiment.

Original languageEnglish (US)
Pages (from-to)617-631
Number of pages15
JournalStatistica Sinica
Issue number2
StatePublished - Apr 1 2008


  • Doubly geometric process
  • Efficiency
  • Non-Gaussian spatial model
  • Pickard process
  • Quadrant autoregressive process
  • Robustness
  • Weighted empirical processes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Minimum distance inference in unilateral autoregressive lattice processes'. Together they form a unique fingerprint.

Cite this