Growth Curve Models and Applications

1. Front Matter

   Pages i-xvii

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2. Growth Curve of Elephant-Foot-Yam, Plant Stress and Mann-Whitney U-Statistics

   • Ratan Dasgupta

   Pages 1-41

3. Protein Structure Modeling of Abnormal Genes Associated with PARK 1 and PARK 8 Loci Related to Autosomal Dominant Parkinson’s Disease and Docking the Protein(s) with Appropriate Ligands

   • Sanchari Roy, T. S. Vasulu

   Pages 43-89

4. Time Detection for Ovulation in a Cycle in Presence of Polycystic Ovary Syndrome

   • Ratan Dasgupta

   Pages 91-104

5. Growth Model for Micro-Particles Towards Indistinguishability and Dirichlet Prior

   • Ratan Dasgupta

   Pages 105-113

6. Coconut Plant Growth, Mahalanobis Distance, and Jeffreys’ Prior

   • Ratan Dasgupta

   Pages 115-125

7. Growth Rate of Primary School Children in Kolkata, India

   • Susmita Bharati, Manoranjan Pal, Madhuparna Srivastava, Premananda Bharati

   Pages 127-149

8. Growth Curve Estimation of a Bulb Crop from Incomplete Data

   • Ratan Dasgupta

   Pages 151-167

9. Tackling Poverty Through Balanced Growth: A Study on India

   • Sattwik Santra, Samarjit Das

   Pages 169-181

10. Model Selection and Validation in Agricultural Context: Extended Uniform Distribution and Some Characterization Theorems

    • Ratan Dasgupta

    Pages 183-197

11. Longitudinal Growth Curve of Elephant Foot Yam Under Extreme Stress and Plant Sensitivity II

    • Ratan Dasgupta

    Pages 199-213

12. An In-Depth Analysis of Population Ageing for Selected States in India in the Perspective of Economic Development

    • Prasanta Pathak

    Pages 215-233

13. Growth Curve of Yam from Incomplete Data in Saline Soil of Sunderban

    • Ratan Dasgupta

    Pages 235-253

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 app
lied work, and these contributions have been externally refereed to the high quality standards of leading journals in the field. 

• growth curve
• modeling
• model validation
• poverty
• Parkinsons disease
• plant sensitivity
• longitudinal studies
• biostatistics
• population
• economics
• sociology
• biotechnology
• nano
• fluid mechanics
• biomass
• fungal growth

• Theoretical Statistics and Mathematics Unit, Indian Statistical Institute Stat-Math Unit, Kolkata, India

  Ratan Dasgupta

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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