Best method to create growth charts I have to create charts (similar to growth charts) for children of ages 5 to 15 years (only 5,6,7 etc; there are no fractional values like 2.6 years) for a health variable which is non-negative, continuous and in the range of 50-150 (with only a few values outside this range). I have to create 90th, 95th and 99th percentile curves and also create tables for these percentiles. The sample size is about 8000. 
I checked and found following possible ways:


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*Find quantiles and then use loess method to get a smooth curve from these quantiles. The degree of smoothness can be adjusted by 'span' parameter. 

*Use LMS (Lambda-Mu-Sigma) method (e.g. using gamlss or VGAM packages in R). 

*Use quantile regression.

*Use mean and SD of each age group to estimate percentile for that age and create percentile curves.
What is the best way to do it?  By 'best' I mean either the ideal method which is the standard method for creation of such growth curves and would be acceptable to all. Or an easier and simpler to implement method, which may have some limitations, but is an acceptable, quicker method. (For example using loess on percentile values is much faster than using LMS of gamlss package). 
Also what will be the basic R code for that method. 
Thanks for your help.
 A: There is a large literature on growth curves.  In my mind there are three "top" approaches.  In all three, time is modeled as a restricted cubic spline with a sufficient number of knots (e.g., 6).  This is a parametric smoother with excellent performance and easy interpretation.


*

*Classical growth curve models (generalized least squares) for longitudinal data with a sensible correlation pattern such as continuous-time AR1.  If you can show that residuals are Gaussian you can get MLEs of the quantiles using the estimated means and the common standard deviation.  

*Quantile regression.  This is not efficient for non-large $n$.  Even though precision is not optimal, the method makes minimal assumptions (because estimates for one quantile are not connected to estimates of a different quantile) and is unbiased.

*Ordinal regression.  This treats continuous $Y$ as ordinal in order to be robust, using semi-parametric models such as the proportional odds model.  From ordinal models you can estimate the mean and any quantiles, the latter only if $Y$ is continuous.

A: Gaussian process regression. Start with the squared exponential kernel and try and tune the parameters by eye. Later, if you want to do things properly, experiment with different kernels and use the marginal likelihood to optimize the parameters.
If you want more detail than the tutorial linked above provides, this book is great.
