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), 2024. Published by Science Publishing Group |
Infants, Weight, Bayesian Hierarchical, Dynamic Linear Model, Gibbs Sampler
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APA Style
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
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
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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Download@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}
}
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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