Mathematical modelling of human growth
A comparative study
Bibliographic Data
| ID | 19459780 |
|---|---|
| Authors | Shumei Guo (Wright State University), Roger M Siervogel (Wright State University), Alex F Roche (Wright State University), Wm Cameron Chumlea (Wright State University) |
| Year | 1992 |
| Volume | 4 |
| Issue | 1 |
| Pages | 93-104 |
| Publication date | 1992-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Human Biology (JOURNAL) |
| Journal identifiers | ISSN: 1042-0533 • E-ISSN: 1520-6300 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/ajhb.1310040112 |
| PMID | 28524403 |
| OpenAlex | W2168627228 |
| Language | EN |
| Citations received | 13 |
| References cited | 23 |
Kernel regression is a nonparametric procedure that provides good approximations to individual serial data. The method is useful and flexible when a parametric method is inappropriate due to restricted assumptions on the shape of the curve. In the present study, we compared kernel regression in fitting human stature growth with two models, one of which incorporates the possible existence of the midgrowth spurt while the other does not. Two families of mathematical functions and a nonparametric kernel regression were fitted to serial measures of stature on 227 participants enrolled in the Fels Longitudinal Study. The growth parameters that describe the timing, magnitude, and duration of the growth spurt, such as midgrowth spurt and pubertal spurts, were derived from the fitted models and kernel regression for each participant. The two parametric models and kernel regression were compared in regard to their overall goodness of fit and their capabilities to quantify the timing, rate of increase, and duration of the growth events. The Preece‐Baines model does not describe the midgrowth spurt. The dervied growth parameters from the Preece‐Baines model show an earlier onset and a longer duration of the pubertal spurt, and a slower increase in velocity. The kernel regression with bandwidth 2 years and a second‐order polynomial kernel function yields relatively good fits compared with the triple logistic model. The derived biological parameters for the pubertal spurt are similar between the kernel regression and the triple logistic model. Kernel regression estimates an earlier onset and a more rapid increase of velocity for the midgrowth spurt
Geography · Computer Science · Statistical and numerical algorithms
Adolescent growth in main somatometric traits of Japanese boys
Individual Adolescent Growth of Stature, Body Weight, and Chest Circumference of Girls in Tokyo
Decanalization of weight and stature during childhood and adolescence
Adolescent spurts in body dimensions
Longitudinal analysis of growth in children with idiopathic short stature
The adolescent growth spurt in children with cystic fibrosis
Estimating peak height velocity in individuals
The adolescent spurt and sexual maturation in girls active and not active in sport
Adolescent height growth of girls in Tokyo
Application of the Preece‐Baines growth model to cross‐sectional data
Application of growth models in the analysis of pathological growth data
Assessment of skeletal age in youth female soccer players
Growth in stature in early, average, and late maturing children of the Caracas mixed‐longitudinal study
Time series analysis
Random-Effects Models for Longitudinal Data
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Human Growth
Model fitting to early childhood length and weight data from the fels longitudinal study of growth
Velocity and acceleration of height growth using kernel estimation
An analysis of the mid-growth and adolescent spurts of height based on acceleration
Analysis of the adolescent growth spurt using smoothing spline functions
Shape-invariant modelling of human growth
A new family of mathematical models describing the human growth curve
Human height growth
| Unique citing works | 13 |
|---|---|
| Citations per year | 0,39 |
| Citation span | 1993 - 2022 (30) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 11 |