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Asymptotics for testing hypothesis in some multivariate variance components model under non-normality

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  • Gupta, Arjun K.
  • Harrar, Solomon W.
  • Fujikoshi, Yasunori

Abstract

We consider the problem of deriving the asymptotic distribution of the three commonly used multivariate test statistics, namely likelihood ratio, Lawley-Hotelling and Bartlett-Nanda-Pillai statistics, for testing hypotheses on the various effects (main, nested or interaction) in multivariate mixed models. We derive the distributions of these statistics, both in the null as well as non-null cases, as the number of levels of one of the main effects (random or fixed) goes to infinity. The robustness of these statistics against departure from normality will be assessed. Essentially, in the asymptotic spirit of this paper, both the hypothesis and error degrees of freedom tend to infinity at a fixed rate. It is intuitively appealing to consider asymptotics of this type because, for example, in random or mixed effects models, the levels of the main random factors are assumed to be a random sample from a large population of levels. For the asymptotic results of this paper to hold, we do not require any distributional assumption on the errors. That means the results can be used in real-life applications where normality assumption is not tenable. As it happens, the asymptotic distributions of the three statistics are normal. The statistics have been found to be asymptotically null robust against the departure from normality in the balanced designs. The expressions for the asymptotic means and variances are fairly simple. That makes the results an attractive alternative to the standard asymptotic results. These statements are favorably supported by the numerical results.

Suggested Citation

  • Gupta, Arjun K. & Harrar, Solomon W. & Fujikoshi, Yasunori, 2006. "Asymptotics for testing hypothesis in some multivariate variance components model under non-normality," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 148-178, January.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:1:p:148-178
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    References listed on IDEAS

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    1. Yasunori Fujikoshi, 1975. "Asymptotic formulas for the non-null distributions of three statistics for multivariate linear hypothesis," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 27(1), pages 99-108, December.
    2. Mathew, Thomas, 1989. "MANOVA in the multivariate components of variance model," Journal of Multivariate Analysis, Elsevier, vol. 29(1), pages 30-38, April.
    3. Schott, James R. & Saw, John G., 1984. "A multivariate one-way classification model with random effects," Journal of Multivariate Analysis, Elsevier, vol. 15(1), pages 1-12, August.
    4. Magnus, J.R. & Neudecker, H., 1979. "The commutation matrix : Some properties and applications," Other publications TiSEM d0b1e779-7795-4676-ac98-1, Tilburg University, School of Economics and Management.
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    Cited by:

    1. Teng, Suyan & Lee, Loo Hay & Chew, Ek Peng, 2010. "Integration of indifference-zone with multi-objective computing budget allocation," European Journal of Operational Research, Elsevier, vol. 203(2), pages 419-429, June.
    2. Güven, Bilgehan, 2015. "A mixed model for complete three or higher-way layout with two random effects factors," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 45-55.
    3. Solomon Harrar & Arne Bathke, 2012. "A modified two-factor multivariate analysis of variance: asymptotics and small sample approximations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 135-165, February.
    4. Chun-Lung Su, 2021. "Bayesian multi-way balanced nested MANOVA models with random effects and a large number of the main factor levels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 663-692, July.
    5. Trent Gaugler & Michael G. Akritas, 2011. "Testing for Interaction in Two-Way Random and Mixed Effects Models: The Fully Nonparametric Approach," Biometrics, The International Biometric Society, vol. 67(4), pages 1314-1320, December.
    6. Liu, Chunxu & Bathke, Arne C. & Harrar, Solomon W., 2011. "A nonparametric version of Wilks' lambda--Asymptotic results and small sample approximations," Statistics & Probability Letters, Elsevier, vol. 81(10), pages 1502-1506, October.
    7. Bathke, Arne C. & Harrar, Solomon W. & Wang, Haiyan & Zhang, Ke & Piepho, Hans-Peter, 2010. "Series of randomized complete block experiments with non-normal data," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1840-1857, July.
    8. Solomon Harrar & Arjun Gupta, 2007. "Asymptotic Expansion for the Null Distribution of the F-statistic in One-way ANOVA under Non-normality," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(3), pages 531-556, September.
    9. Harrar, Solomon W. & Kong, Xiaoli, 2022. "Recent developments in high-dimensional inference for multivariate data: Parametric, semiparametric and nonparametric approaches," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

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