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Models for Intensive Longitudinal Data$
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Theodore A. Walls and Joseph L. Schafer

Print publication date: 2006

Print ISBN-13: 9780195173444

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780195173444.001.0001

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Fitting Curves with Periodic and Nonperiodic Trends and Their Interactions with Intensive Longitudinal Data

Fitting Curves with Periodic and Nonperiodic Trends and Their Interactions with Intensive Longitudinal Data

Chapter:
(p.109) 5 Fitting Curves with Periodic and Nonperiodic Trends and Their Interactions with Intensive Longitudinal Data
Source:
Models for Intensive Longitudinal Data
Author(s):

Carlotta Ching Ting Fok

James O. Ramsay

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195173444.003.0005

In longitudinal series with only a few waves of measurement, growth or change over time can often be well estimated by non-complex time-graded polynomials. However, with more intensive schedules of measurement, fluctuation patterns tend to be more complex, and modeling them in a parsimonious and flexible way becomes tough. Looking upon ideas from functional data analysis, the authors prove how to build multilevel regression models for intensive longitudinal data that manifest a mix of periodic and nonperiodic trends. In this chapter, they apply the multilevel regression models characterized by Walls et al., but in a different approach. They account for complex patterns of change in intensive longitudinal data by a mix of period effects, nonperiodic effects, and autocorrelated noise.

Keywords:   fluctuation patterns, multilevel regression, period effects, nonperiodic effects, autocorrelated noise

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