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A generalization of the growth curve model which allows missing data

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  • Kleinbaum, David G.

Abstract

This study presents methods for estimating and testing hypotheses about linear functions of the unknown parameters in a generalization of the growth curve model which allows missing data. The estimators proposed are best asymptotically normal (BAN). A testing method for large samples is described which uses a test criterion given in general form by Wald. The asymptotic null distribution of the test statistic is a central chi-square variable. A BAN estimator of a linear vector function of the unknown parameters of the expectation model and consistent estimators of the variance-covariance parameters are required for computation.

Suggested Citation

  • Kleinbaum, David G., 1973. "A generalization of the growth curve model which allows missing data," Journal of Multivariate Analysis, Elsevier, vol. 3(1), pages 117-124, March.
    • Handle: RePEc:eee:jmvana:v:3:y:1973:i:1:p:117-124
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    Cited by:

    1. Fulya Gokalp Yavuz & Olcay Arslan, 2018. "Linear mixed model with Laplace distribution (LLMM)," Statistical Papers, Springer, vol. 59(1), pages 271-289, March.
    2. James Algina & Stephen F. Olejnik, 1982. "Multiple Group Time-Series Design," Evaluation Review, , vol. 6(2), pages 203-232, April.
    3. Kakizawa, Yoshihide, 2009. "Third-order power comparisons for a class of tests for multivariate linear hypothesis under general distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 473-496, March.
    4. Gwowen Shieh & Jack Lee, 2002. "Bayesian Prediction Analysis for Growth Curve Model Using Noninformative Priors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 324-337, June.

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