Flexible methods for analysing longitudinal data using piecewise cubic polynomials

DOI10.1080/00949659508811657zbMath0842.62091OpenAlexW2032168876WikidataQ126250323 ScholiaQ126250323MaRDI QIDQ4869589

Yongxiao Wang

Publication date: 26 March 1996

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949659508811657

zbMATH Keywords

repeated measuresapproximationsAIDSsimulation studyclinical trialmaximum penalized likelihoodsmoothing parameterpiecewise cubic polynomialstreatment groupsleave-out-one-subject weighted cross-validation schemepenelized likelihood estimates

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Random-Effects Models for Longitudinal Data
• Spline smoothing: The equivalent variable kernel method
• Asymptotic integrated mean square error using least squares and bias minimizing splines
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• An Approach to the Analysis of Repeated Measurements
• Kernel Regression Estimation Using Repeated Measurements Data
• Flexible Parsimonious Smoothing and Additive Modeling
• Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters

This page was built for publication: Flexible methods for analysing longitudinal data using piecewise cubic polynomials