Growth Curve Models and Applications [electronic resource] : Indian Statistical Institute, Giridih, India, March 28-29, 2016 /
Dasgupta, Ratan.editor.
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.  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 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. .
Includes bibliographical references at the end of each chapters.
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.  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 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. .
Statistics .
Biostatistics.
Mathematical statistics.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Biostatistics.
Statistics and Computing/Statistics Programs.
Probability and Statistics in Computer Science.
Statistics for Business, Management, Economics, Finance, Insurance.