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Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight

Received: 5 March 2018     Accepted: 19 March 2018     Published: 2 April 2018
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Abstract

The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant’s growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time.

Published in American Journal of Theoretical and Applied Statistics (Volume 7, Issue 3)
DOI 10.11648/j.ajtas.20180703.12
Page(s) 102-111
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Infants, Weight, Bayesian Hierarchical, Dynamic Linear Model, Gibbs Sampler

References
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    Dereje Danbe Debeko, Ayele Taye Goshu. (2018). Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight. American Journal of Theoretical and Applied Statistics, 7(3), 102-111. https://doi.org/10.11648/j.ajtas.20180703.12

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    ACS Style

    Dereje Danbe Debeko; Ayele Taye Goshu. Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight. Am. J. Theor. Appl. Stat. 2018, 7(3), 102-111. doi: 10.11648/j.ajtas.20180703.12

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    AMA Style

    Dereje Danbe Debeko, Ayele Taye Goshu. Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight. Am J Theor Appl Stat. 2018;7(3):102-111. doi: 10.11648/j.ajtas.20180703.12

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  • @article{10.11648/j.ajtas.20180703.12,
      author = {Dereje Danbe Debeko and Ayele Taye Goshu},
      title = {Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {7},
      number = {3},
      pages = {102-111},
      doi = {10.11648/j.ajtas.20180703.12},
      url = {https://doi.org/10.11648/j.ajtas.20180703.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20180703.12},
      abstract = {The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant’s growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight
    AU  - Dereje Danbe Debeko
    AU  - Ayele Taye Goshu
    Y1  - 2018/04/02
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ajtas.20180703.12
    DO  - 10.11648/j.ajtas.20180703.12
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 102
    EP  - 111
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20180703.12
    AB  - The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant’s growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time.
    VL  - 7
    IS  - 3
    ER  - 

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Author Information
  • School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia

  • School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia

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