@article{(Open Science Index):https://publications.waset.org/pdf/14433,
	  title     = {Human Growth Curve Estimation through a Combination of Longitudinal and Cross-sectional Data},
	  author    = {Sedigheh Mirzaei S. and  Debasis Sengupta},
	  country	= {},
	  institution	= {},
	  abstract     = {Parametric models have been quite popular for
studying human growth, particularly in relation to biological
parameters such as peak size velocity and age at peak size velocity.
Longitudinal data are generally considered to be vital for fittinga
parametric model to individual-specific data, and for studying the
distribution of these biological parameters in a human population.
However, cross-sectional data are easier to obtain than longitudinal
data. In this paper, we present a method of combining longitudinal
and cross-sectional data for the purpose of estimating the distribution
of the biological parameters. We demonstrate, through simulations in
the special case ofthePreece Baines model, how estimates based on
longitudinal data can be improved upon by harnessing the
information contained in cross-sectional data.We study the extent of
improvement for different mixes of the two types of data, and finally
illustrate the use of the method through data collected by the Indian
Statistical Institute.},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {6},
	  number    = {7},
	  year      = {2012},
	  pages     = {760 - 765},
	  ee        = {https://publications.waset.org/pdf/14433},
	  url   	= {https://publications.waset.org/vol/67},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 67, 2012},
	}