Park, Dong Joon; Burdick, Richard K. Performance of confidence intervals in regression models with unbalanced one-fold nested error structures. (English) Zbl 1081.62540 Commun. Stat., Simulation Comput. 32, No. 3, 717-732 (2003). Summary: We consider the problem of constructing confidence intervals for a linear regression model with unbalanced nested error structure. A popular approach is the likelihood-based method employed by PROC MIXED of SAS. We examine the ability of MIXED to produce confidence intervals that maintain the stated confidence coefficient. Our results suggest that intervals for the regression coefficients work well, but intervals for the variance component associated with the primary level cannot be recommended. Accordingly, we propose alternative methods for constructing confidence intervals on the primary level variance component. Computer simulation is used to compare the proposed methods. A numerical example and SAS code are provided to demonstrate the methods. Cited in 1 ReviewCited in 13 Documents MSC: 62J05 Linear regression; mixed models 62F25 Parametric tolerance and confidence regions 62J10 Analysis of variance and covariance (ANOVA) 65C60 Computational problems in statistics (MSC2010) Software:MIXED; SAS PDF BibTeX XML Cite \textit{D. J. Park} and \textit{R. K. Burdick}, Commun. Stat., Simulation Comput. 32, No. 3, 717--732 (2003; Zbl 1081.62540) Full Text: DOI References: [1] DOI: 10.1002/0471725153 · doi:10.1002/0471725153 [2] DOI: 10.1080/03610929408831364 · Zbl 0825.62194 · doi:10.1080/03610929408831364 [3] Eubank, L., Seely, J. and Lee, Y. 2001. Unweighted mean squares for the general two variance component mixed model. Proceedings of Graybill Conference. June2001. pp.281–290. CO: Ft. Collins. [4] DOI: 10.1214/aos/1176343586 · Zbl 0344.62060 · doi:10.1214/aos/1176343586 [5] DOI: 10.1080/00949659008811240 · doi:10.1080/00949659008811240 [6] DOI: 10.2307/2289949 · doi:10.2307/2289949 [7] DOI: 10.2307/2290779 · Zbl 0785.62029 · doi:10.2307/2290779 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.