Conditional growth charts

By Ying Wei, Xuming He. 2006

140 citations recorded by the source corpus.

Growth charts are often more informative when they are customized per subject, taking into account prior measurements and possibly other covariates of the subject. We study a global semiparametric quantile regression model that has the ability to estimate conditional quantiles without the usual distributional assumptions. The model can be estimated from longitudinal reference data with irregular measurement times and with some level of robustness against outliers, and it is also flexible for including covariate information. We propose a rank score test for large sample inference on covariates, and develop a new model assessment tool for longitudinal growth data. Our research indicates that the global model has the potential to be a very useful tool in conditional growth chart analysis.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cd8708505914a2b8fc2b11f5046fee1544511f2a

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

VARIABLE SELECTION IN QUANTILE REGRESSION (2009, methodology): “It has been applied in many different areas: economics (Hendricks and Koenker (1992) and Koenker and Hallock (2001)), survival analysis (Yang (1999) and Koenker and Geling (2001)), microarray study (Wang and He (2007)), growth chart (Wei et al. (2006) and Wei and He (2006)), and so on.”
An Approach to Multivariate Covariate-Dependent Quantile Contours With Application to Bivariate Conditional Growth Charts (2008, methodology): “Hence using a Gaussian model may lead to nonignorable bias, and joint transformation is usually difficult (Wei and He 2006).”
Norm- and Criterion-Referenced Student Growth. (2009, methodology): “In particular, if τ = 0.5, then the estimated conditional quantile line is the median regression line.6 Following Wei & He (2006), we parameterize the conditional quantile functions as a linear combination of B-spline cubic basis functions.. B-splines are employed to accommodate non-linearity, heter”
Partially linear additive quantile regression in ultra-high dimension (2016, methodology): “In the following, we consider a simulation-based diagnostic plot to help visually assess the overall lack-of-fit for the quantile regression model [Wei and He (2006)] to assess the overall lack-of-fit for the quantile regression model.”
Quantile regression for dynamic panel data with fixed effects (2011, methodology): “The basic technique used to show uniform convergence is similar to Wei and He (2006) where we divide the growing parameter space into small cubes.”
Quantile Correlations and Quantile Autoregressive Modeling (2012, unknown): “(6.2) This, together with the estimation method in Wei and He (2006), allows us to generalize our proposed procedure to this model.”
Conditional quantile analysis when covariates are functions, with application to growth data (2012, methodology): “Another approach, which was previously used in Wei and He (2006), is to compare the empirical distribution of Y with the simulated distribution from the conditional distribution model.”
A Technical Overview of the Student Growth Percentile Methodology: Student Growth Percentiles and Percentile Growth Projections/Trajectories. (2011, methodology): “In particular, if τ = 0.5, then the estimated conditional quantile line is the median regression line.10 Following Wei & He (2006), we parametrize the conditional quantile functions as a linear combination of B-spline cubic basis functions.. B-splines are employed to accommodate non-linearity,…”
Model Selection via Bayesian Information Criterion for Quantile Regression Models (2014, methodology): “The appropriateness of the linear quantile regression model for these data was checked in Wang, Wu, and Li (2012) via the simulation-based graphical method of Wei and He (2006).”