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Least squares generalized inferences in unbalanced two-component normal mixed linear model

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Abstract

In this paper, we make use of least squares idea to construct new fiducial generalized pivotal quantities of variance components in two-component normal mixed linear model, then obtain generalized confidence intervals for two variance components and the ratio of the two variance components. The simulation results demonstrate that the new method performs very well in terms of both empirical coverage probability and average interval length. The newly proposed method also is illustrated by a real data example.

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Acknowledgments

Xuhua Liu’s work was supported by the National Natural Science Foundation of China under Grant No. 11201478 and 11471030. Xingzhong Xu’s work was supported by the National Natural Science Foundation of China under Grant No. 11471035. Jan Hannig’s research was supported in part by the National Science Foundation under Grant No. 1016441. The authors are very grateful to the two reviewers for their valuable comments and suggestions on the earlier versions of this paper.

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Authors and Affiliations

  1. Department of Mathematics, China Agricultural University, Beijing, 100193, China

    Xuhua Liu

  2. College of Mathematics, Beijing Institute of Technology, Beijing, 100081, China

    Xingzhong Xu

  3. Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Jan Hannig

Authors
  1. Xuhua Liu
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  2. Xingzhong Xu
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  3. Jan Hannig
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Correspondence to Xuhua Liu.

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Liu, X., Xu, X. & Hannig, J. Least squares generalized inferences in unbalanced two-component normal mixed linear model. Comput Stat 31, 973–988 (2016). https://doi.org/10.1007/s00180-015-0604-8

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  • DOI: https://doi.org/10.1007/s00180-015-0604-8

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