An Orthogonality-Based Estimation of Moments for Linear Mixed Models

Ping Wu*, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

22 Citations (Scopus)


Estimating higher-order moments, particularly fourth-order moments in linear mixed models is an important, but difficult issue. In this article, an orthogonality-based estimation of moments is proposed. Under only moment conditions, this method can easily be used to estimate the model parameters and moments, particularly those of higher order than the second order, and in the estimators the random effects and errors do not affect each other. The asymptotic normality of all the estimators is provided. Moreover, the method is readily extended to handle non-linear, semiparametric and non-linear models. A simulation study is carried out to examine the performance of the new method.

Original languageEnglish
Pages (from-to)253-263
Number of pages11
JournalScandinavian Journal of Statistics
Issue number2
Publication statusPublished - Jun 2010

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Asymptotic normality
  • Linear mixed models
  • Moment estimator
  • QR decomposition


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