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Asymptotic Inferences in a Doubly-Semi-Parametric Linear Longitudinal Mixed Model

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  • Brajendra C. Sutradhar

    (Memorial University)

  • R. Prabhakar Rao

    (Sri Sathya Sai Institute of Higher Learning)

Abstract

Warriyar and Sutradhar (Brazilian J. Probab. Stat., 28, 561–586, 2014) studied a semi-parametric linear model in a longitudinal setup with Gaussian errors, where the main regression parameters were estimated using an efficient GQL (generalized quasi-likelihood) estimation approach, and the efficiency properties of the estimators were examined through a simulation study. In this paper we provide a generalization of their linear semi-parametric regression model to a wider setup where the error distributions are relaxed and errors are assumed to follow a four-moments based semi-parametric structure leading to a doubly semi-parametric model. On top of regression parameters and nonparametric function estimation, this doubly semi-parametric nature of the model makes the four-moments based variance and correlation parameters estimation quite challenging. We resolve this computational issue analytically by developing exact formulas for all necessary higher order moments. As the longitudinal studies involve large number of independent individuals providing repeated responses, we study the asymptotic properties of the estimators and make sure that the estimators including the estimator of nonparametric function are consistent.

Suggested Citation

  • Brajendra C. Sutradhar & R. Prabhakar Rao, 2023. "Asymptotic Inferences in a Doubly-Semi-Parametric Linear Longitudinal Mixed Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 214-247, February.
    • Handle: RePEc:spr:sankha:v:85:y:2023:i:1:d:10.1007_s13171-020-00239-8
    DOI: 10.1007/s13171-020-00239-8
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    1. Bun, Maurice J.G. & Carree, Martin A., 2005. "Bias-Corrected Estimation in Dynamic Panel Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 200-210, April.
    2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643.
    3. Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
    4. Naisyin Wang & Raymond J. Carroll & Xihong Lin, 2005. "Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 147-157, March.
    5. Sneddon, Gary & Sutradhar, Brajendra C., 2004. "On semiparametric familial-longitudinal models," Statistics & Probability Letters, Elsevier, vol. 69(3), pages 369-379, September.
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