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A Bayesian sensitivity analysis of the effect of different random effects distributions on growth curve models
Growth curve data consist of repeated measurements of a contin uous growth process of human, animal, plant, microbial or bacterial genetic data over time in a population of individuals. A classical approach for analyzing such data is the use of non-linear mixed effects models under normality assumpt...
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| Format: | Article |
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2020
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/13095 | ||
| 042 | |a dc | ||
| 720 | |a Ganjali, M. |e author | ||
| 720 | |a Baghfalaki, T. |e author | ||
| 720 | |a Fagbamigbe, A. F. |e author | ||
| 260 | |c 2020 | ||
| 520 | |a Growth curve data consist of repeated measurements of a contin uous growth process of human, animal, plant, microbial or bacterial genetic data over time in a population of individuals. A classical approach for analyzing such data is the use of non-linear mixed effects models under normality assumption for the responses. But, sometimes the underlying population that the sample is extracted from is an abnormal population or includes some homogeneous sub-samples. So, detection of original properties of the population is an important scientific question of interest. In this paper, a sensitivity analysis of using different parametric and non-parametric distributions for the random effects on the results of applying non-linear mixed models is proposed for emphasizing the possible heterogeneity in the population. A Bayesian MCMC procedure is developed for parameter estimation and inference is performed via a hierarchical Bayesian framework. The methodology is illustrated using a real data set on study of influence of menarche on changes in body fat accretion. | ||
| 024 | 8 | |a 2316-090X | |
| 024 | 8 | |a ui_art_ganjali_bayesian_2020 | |
| 024 | 8 | |a Afrika Statistika 15(3), pp. 2387–2393 | |
| 024 | 8 | |a https://repository.ui.edu.ng/handle/123456789/13095 | |
| 653 | |a Bayesian paradigm | ||
| 653 | |a Dirichlet process | ||
| 653 | |a growth curve models | ||
| 653 | |a mixed effects model | ||
| 653 | |a repeated measurements data | ||
| 653 | |a sensitivity analysis | ||
| 245 | 0 | 0 | |a A Bayesian sensitivity analysis of the effect of different random effects distributions on growth curve models |