Modeling Longitudinal Data with Ordinal Response by Varying Coefficients

Göran Kauermann

Type: Article

Publication Date: 2000-09-01

Citations: 24

DOI: https://doi.org/10.1111/j.0006-341x.2000.00692.x

Abstract

This paper presents a smooth regression model for ordinal data with longitudinal dependence structure. A marginal model with cumulative logit link is applied to cope with the ordinal scale and the main and covariate effects in the model are allowed to vary with time. Local fitting is pursued and asymptotic properties of the estimates are discussed. In a second step, the longitudinal dependence of the observations is considered. Cumulative log odds ratios are fitted locally, which allows investigation of how the longitudinal dependence of the ordinal observations changes with time.

Locations

Open access LMU (Ludwid Maxmilian's Universitat Munchen) - View - PDF
PubMed - View
Biometrics - View

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+	Additive Extensions to Generalized Estimating Equation Methods	1996	C. Wild Thomas W. Yee
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+	Correlated Binary Regression with Covariates Specific to Each Binary Observation	1988	Ross L. Prentice
+	Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters	1994	Scott L. Zeger Peter J. Diggle
+	Nonparametric Function Estimation for Clustered Data When the Predictor is Measured without/with Error	2000	Xihong Lin Raymond J. Carroll
+	Kernel Regression Estimation Using Repeated Measurements Data	1986	Jeffrey D. Hart Thomas E. Wehrly
+	Local Estimating Equations	1998	Raymond J. Carroll David Ruppert A. H. Welsh
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