Consultant's forum: Assessing homeostasis through circadian patterns

Biometrics

Volumn 57, Issue 4, 2001, Pages 1228-1237

  Irizarry, Rafael A ^a   Tankersley, Clarke ^b   Frank, Robert ^b   Flanders, Susan ^b  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b The Johns Hopkins University   (United States)

Author keywords

Circadian patterns; Correlated errors; Mixed effects models; Nested curves

Indexed keywords

BEHAVIORAL SYSTEMS; CIRCADIAN PATTERN; CORRELATED ERRORS; HOMOEOSTASIS; MIXED EFFECTS MODELS; NESTED CURVE; PHYSIOLOGICAL SYSTEMS; POINT VARIABLES; REGULATORY PROCESS; SETPOINTS;

PHYSIOLOGY;

BIOMETRY; CIRCADIAN RHYTHM; HOMEOSTASIS;

BODY TEMPERATURE; CIRCADIAN RHYTHM; HOMEOSTASIS; MOUSE; NONHUMAN; REVIEW; STATISTICS; THERMOREGULATION; VARIANCE; ANIMAL; ARTICLE; BIOMETRY; METHODOLOGY; MOTOR ACTIVITY; PHYSIOLOGY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ANIMAL; BIOMETRY; BODY TEMPERATURE; CIRCADIAN RHYTHM; DATA INTERPRETATION, STATISTICAL; HOMEOSTASIS; MICE; MODELS, STATISTICAL; MOTOR ACTIVITY;

EID: 0035653804     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2001.01228.x     Document Type: Review

References (13)

1. Smoothing spline models for the analysis of nested and crossed samples of curves
2. Maximum likelihood from incomplete data via the EM algorithm
3. Fitting nonlinear models with ARMA errors to biological rhythm data
4. The circadian rhythm of body temperature
5. That BLUP is a good thing: The estimation of random effects
6. Mixed effects smoothing spline analysis of variance
7. A flexible model for human circadian rhythms
8. Semiparametric stochastic mixed models for longitudinal data
9. Semiparametric stochastic mixed models for longitudinal data