Modeling of Hormone Secretion‐Generating Mechanisms with Splines: A Pseudo‐Likelihood Approach

DOI10.1111/J.1541-0420.2006.00672.XzbMath1122.62091OpenAlexW2014896404WikidataQ51916810 ScholiaQ51916810MaRDI QIDQ5427418

Anna Liu, Yuedong Wang

Publication date: 20 November 2007

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2006.00672.x

zbMATH Keywords

circadian rhythmsweighted least squaresdepressionendocrinology

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Physiology (general) (92C30)

Related Items (1)

A Bayesian Approach to Modeling Associations Between Pulsatile Hormones

Uses Software

• gss
• assist

Cites Work

• Unnamed Item
• Unnamed Item
• Robust estimation in heteroscedastic linear models
• Profile likelihood and conditionally parametric models
• Smoothing spline ANOVA for exponential families, with application to the Wisconsin epidemiological study of diabetic retinopathy. (The 1994 Neyman Memorial Lecture)
• Optimal smoothing in nonparametric mixed-effect models
• Bayesian Deconvolution Analysis of Pulsatile Hormone Concentration Profiles
• Shape‐Invariant Modeling of Circadian Rhythms with Random Effects and Smoothing Spline ANOVA Decompositions
• Deconvolution of Episodic Hormone Data: An analysis of the Role of Season on the Onset of Puberty in Cows
• Variance Function Estimation
• Smoothing Spline Models with Correlated Random Errors
• A Flexible Model for Human Circadian Rhythms
• Smoothing Spline Nonlinear Nonparametric Regression Models
• Bootstrap confidence intervals for smoothing splines and their comparison to bayesian confidence intervals
• Detecting Pulsatile Hormone Secretions Using Nonlinear Mixed Effects Partial Spline Models

This page was built for publication: Modeling of Hormone Secretion‐Generating Mechanisms with Splines: A Pseudo‐Likelihood Approach