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A New Technique for Estimating Population Distribution of Growth Curve Parameters with Longitudinal and Cross-sectional Data

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Advances in Growth Curve Models

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 46))

Abstract

In this paper, we present a new approach for estimating the population distribution of biological parameters related to individual growth. The most attractive feature of the proposed method is that, while some amount of longitudinal data is required, information contained in sparse longitudinal as well as completely cross-sectional data can also be harnessed. Although the method is not Bayesian, it can be implemented through recursions based on Gibb’s sampling. Computer simulations in the special case of the Preece–Baines growth model show that inclusion of some cross-sectional data indeed reduces the mean squared errors of the estimators. The method is then used to compare the population distribution of human growth parameters among the male and female subjects of a study conducted some years ago by the Indian Statistical Institute.

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Author information

Authors and Affiliations

  1. Applied Statistical Unit, Indian Statistical Institute, Kolkata, India

    Sedigheh Mirzaei Salehabadi & Debasis Sengupta

Authors
  1. Sedigheh Mirzaei Salehabadi
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  2. Debasis Sengupta
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Corresponding author

Correspondence to Debasis Sengupta .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute Professor, Stat-Math Unit, Calcutta, India

    Ratan Dasgupta

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Salehabadi, S.M., Sengupta, D. (2013). A New Technique for Estimating Population Distribution of Growth Curve Parameters with Longitudinal and Cross-sectional Data. In: Dasgupta, R. (eds) Advances in Growth Curve Models. Springer Proceedings in Mathematics & Statistics, vol 46. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6862-2_9

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