Research article

Statistical inference of the mixed linear model with incorrect stochastic linear restrictions

  • Published: 19 May 2025
  • MSC : 62H12, 62J05

  • We considered the general mixed linear model $ \mathscr{N} $ subject to two competing stochastic linear restrictions, $ \mathscr{M}_{0} $ and $ \mathscr{M} $, where the restrictions $ \mathscr{M} $ are the correct information whereas restrictions $ \mathscr{M}_{0} $ may be incorrect. Statistical inference conclusions of using the above two competing restrictions are not necessarily the same, so it is prominent to discuss the relationships between incorrect restrictions $ \mathscr{M}_{0} $ and the corresponding correct restrictions $ \mathscr{M} $ in the context of model $ \mathscr{N} $. In this article, we first present some properties on the best linear unbiased predictors (BLUPs) under model $ \mathscr{N} $ with restrictions $ \mathscr{M} $. We then provide necessary and sufficient conditions under which the BLUPs under $ \mathscr{N} $ with the incorrect restrictions $ \mathscr{M}_{0} $ continue to be BLUPs associated with correct restrictions.

    Citation: Xingwei Ren. Statistical inference of the mixed linear model with incorrect stochastic linear restrictions[J]. AIMS Mathematics, 2025, 10(5): 11349-11368. doi: 10.3934/math.2025516

    Related Papers:

  • We considered the general mixed linear model $ \mathscr{N} $ subject to two competing stochastic linear restrictions, $ \mathscr{M}_{0} $ and $ \mathscr{M} $, where the restrictions $ \mathscr{M} $ are the correct information whereas restrictions $ \mathscr{M}_{0} $ may be incorrect. Statistical inference conclusions of using the above two competing restrictions are not necessarily the same, so it is prominent to discuss the relationships between incorrect restrictions $ \mathscr{M}_{0} $ and the corresponding correct restrictions $ \mathscr{M} $ in the context of model $ \mathscr{N} $. In this article, we first present some properties on the best linear unbiased predictors (BLUPs) under model $ \mathscr{N} $ with restrictions $ \mathscr{M} $. We then provide necessary and sufficient conditions under which the BLUPs under $ \mathscr{N} $ with the incorrect restrictions $ \mathscr{M}_{0} $ continue to be BLUPs associated with correct restrictions.



    加载中


    [1] X. Ren, The equalities of estimations under a general partitioned linear model and its stochastically restricted model, Commun. Stat. Theor. M., 45 (2016), 6495–6509. https://doi.org/10.1080/03610926.2014.960587 doi: 10.1080/03610926.2014.960587
    [2] J. Xu, H. Yang, Estimation in singular linear models with stochastic linear restrictions, Commun. Stat. Theor. M., 36 (2007), 1945–1951. https://doi.org/10.1080/03610920601126530 doi: 10.1080/03610920601126530
    [3] N. Güler, M. E. Büyükkaya, Statistical inference of a stochastically restricted linear mixed model, AIMS Mathematics, 8 (2023), 24401–24417. https://doi.org/10.3934/math.20231244 doi: 10.3934/math.20231244
    [4] S. K. Mitra, B. J. Moore, Gauss-Markov estimation with an incorrect dispersion matrix, Sankhy$ \bar{a} $, Ser. A, 35 (1973), 139–152.
    [5] C. R. Rao, A note on a previous lemma in the theory of least squares and some further results, Indian Statistical Institute, 1968.
    [6] J. Hauke, A. Markiewicz, S. Puntanen, Comparing the BLUEs under two linear models, Commun. Stat. Theor. M., 41 (2012), 2405–2418. https://doi.org/10.1080/03610926.2011.594541 doi: 10.1080/03610926.2011.594541
    [7] S. J. Haslett, S. Puntanen, Effect of adding regressors on the equality of the BLUEs under two linear models, J. Statist. Plann. Inference, 140 (2010), 104–110. https://doi.org/10.1016/j.jspi.2009.06.010 doi: 10.1016/j.jspi.2009.06.010
    [8] Y. Tian, On equalities for BLUEs under Misspecified Gauss-Markov models, Acta. Math. Sin.-English Ser., 25 (2009), 1907–1920. https://doi.org/10.1007/s10114-009-6375-9 doi: 10.1007/s10114-009-6375-9
    [9] Y. Tian, B. Jiang, A new analysis of the relationships between a general linear model and its mis-specified forms, J. Korean Stat. Soc., 46 (2017), 182–193. https://doi.org/10.1016/j.jkss.2016.08.004 doi: 10.1016/j.jkss.2016.08.004
    [10] S. Gan, Y. Sun, Y. Tian, Equivalence of predictors under real and over-parameterized linear models, Commun. Stat. Theor. M., 46 (2017), 5368–5383. https://doi.org/10.1080/03610926.2015.1100742 doi: 10.1080/03610926.2015.1100742
    [11] R. Yuan, B. Jiang, Y. Tian, A study of the equivalence of inference results in the contexts of true and misspecified multivariate general linear models, AIMS Mathematics, 8 (2023), 21001–21021. https://dx.doi.org/10.3934/math.20231069 doi: 10.3934/math.20231069
    [12] D. K. Guilkey, J. M. Price, On comparing restricted least squares estimators, J. Econometrics, 15 (1981), 397–404. https://doi.org/10.1016/0304-4076(81)90102-0 doi: 10.1016/0304-4076(81)90102-0
    [13] G. Trenkler, Mean square error matrix comparisons among restricted least squares estimators, Sankhya, 49 (1987), 96–104.
    [14] X. Ren, Q. Zhou, Dispersion matrix comparisons among estimators under two competing restricted linear regression models, Indian J. Pure Appl. Math., 2023. https://dx.doi.org/10.1007/s13226-023-00505-z doi: 10.1007/s13226-023-00505-z
    [15] G. Marsaglia, G. P. H. Styan, Equalities and inequalities for ranks of matrices, Linear Multilinear Algebra, 2 (1974), 269–292. https://doi.org/10.1080/03081087408817070 doi: 10.1080/03081087408817070
    [16] Y. Tian, The maximal and minimal ranks of some expressions of generalized inverses of matrices, SEA Bull. Math., 25 (2002), 745–755. https://doi.org/10.1007/s100120200015 doi: 10.1007/s100120200015
    [17] Y. Tian, S. Cheng, The maximal and minimal ranks of A-BXC with applications, New York J. Math., 9 (2003), 345–362. https://dx.doi.org/10.1007/s100120200015 doi: 10.1007/s100120200015
    [18] X. Ren, L. Lin, Some remarks on BLUP under the general linear model with linear equality restrictions, J. Math. Res. Appl., 38 (2018), 496–508. https://doi.org/10.3770/j.issn:2095-2651.2018.05.008 doi: 10.3770/j.issn:2095-2651.2018.05.008
    [19] C. R. Rao, Representations of best linear unbiased estimators in the Gauss-Markoff model with a singular dispersion matrix, J. Multivariate Anal., 3 (1973), 276–292. https://doi.org/10.1016/0047-259X(73)90042-0 doi: 10.1016/0047-259X(73)90042-0
    [20] Y. Yan, D. Cheng, J. E. Feng, H. Li, J. Yue, Survey on applications of algebraic state space theory of logical systems to finite state machines, Sci. China Inf. Sci., 66 (2023), 111201. https://doi.org/10.1007/s11432-022-3538-4 doi: 10.1007/s11432-022-3538-4
    [21] B. Jiang, Y. Tian, Analysis of best linear unbiased predictions in the contexts of a linear mixed model and its six correctly-reduced models, Comm. Statist. Simulation Comput., 2025, 1–26. https://doi.org/10.1080/03610918.2025.2474590
    [22] B. Jiang, Y. Tian, Equivalent analysis of different estimations under a multivariate general linear model, AIMS Mathematics, 9 (2024), 23544–23563. https://doi.org/10.3934/math.20241144 doi: 10.3934/math.20241144
    [23] G. K. Robinson, That BLUP is a good thing: The estimation of random effects, Stat. Sci., 6 (1991), 15–32.
    [24] J. Jiang, T. Nguyen, Linear and generalized linear mixed models and their applications, 2 Eds., New York: Springer, 2021. https://doi.org/10.1007/978-1-0716-1282-8
    [25] H. Yang, H. Ye, K. Xue, A further study of predictions in linear mixed models, Commun. Stat. Theor. M., 43 (2014), 4241–4252. https://doi.org/10.1080/03610926.2012.725497 doi: 10.1080/03610926.2012.725497
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(561) PDF downloads(32) Cited by(0)

Article outline

Figures and Tables

Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog