Growth Curve Models and Applications

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas.
There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of applied work, and these contributions have been externally refereed to the high quality standards of leading journals in the field.

Theoretical findings and case studies are reported from a wide range of fundamental and applied work across the broad range of natural sciences that comprise Growth Curve Modeling Methodology is particularly relevant to health care, prediction of crop yield, child nutrition, poverty measurements, and estimation of growth rate in any given scenario All papers feature original, peer-reviewed content Includes supplementary material: sn.pub/extras

Autorentext
Ratan Dasgupta, Ph.D., is Professor at the Indian Statistical Institute, Kolkata. Apart from his Ph.D topic on rates of convergence in CLT, his areas of research interest include applications of Statistics in Quality Control, fluid mechanics, environment, physics, and other areas of applied statistics. He has published roughly 70 research papers.

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1. Ratan Dasgupta, Indian Statistical Institute: Plant sensitivity under severe injury and growth curve of yam.

2. Sanchari Roy and T.S.Vasulu, Indian Statistical Institute: Modeling of abnormal proteins related to PARK1 and PARK 8 loci involved in autosomal dominant Parkinson's disease and docking the proteins with appropriate ligands.

3. Ratan Dasgupta and Anwesha Pan, Indian Statistical Institute: Ovulation time detection in presence of Polycystic Ovary Syndrome.

4. Monoranjan Ghosh, Indian Statistical Institute: Ecology, Conservation and Utilization of Endangered Species of Rattan Palms in Northeast India.

5. Ratan Dasgupta, Indian Statistical Institute: Some recent results on coconut tree growth in saline soil.

6. Susmita Bharati, Manoranjan Pal, Madhuparna Srivastava and Premananda Bharati, Indian Statistical Institute: Growth rates of height, weight, MUAC and body fat of Primary School Children in Kolkata, India.
   Ratan Dasgupta, Indian Statistical Institute: Growth Curve estimation from incomplete data.
   Sattwik Santra and Samarjit Das, Centre for Studies in Social Sciences and Indian Statistical Institute: Tackling poverty through balanced growth: A study on India.
   Ratan Dasgupta, Indian Statistical Institute: Model selection and validation in agricultural context of growth curve.
   Ratan Dasgupta, Indian Statistical Institute: Growth curve of Elephant foot yam under different level of stress: A technique to increase yield.
   Prasanta Pathak, Indian Statistical Institute: Temporal variation in ageing of population & replacement by young dependents in developed and developing states in India.
   Ratan Dasgupta, Indian Statistical Institute: Growth Curve of Elephant foot yam in Sunderban.