Abstract
The paper presents a smooth regression model for ordinal data with longitudinal dependence structure. A marginal model with cumulative logit link (McCullagh 1980) is applied to cope for 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. A data example demonstrates the exploratory flavor of the smooth model. In a second step, the longitudinal dependence of the observations is considered. Cumulative log odds ratios are fitted locally which provides insight how the dependence of the ordinal observations changes with time.
Item Type: | Paper |
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Keywords: | Kernel smoothing, local estimating equations, longitudinal data, marginal model, ordinal data, varying coefficient models |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386 Special Research Fields > Special Research Field 386 |
Subjects: | 500 Science > 510 Mathematics |
URN: | urn:nbn:de:bvb:19-epub-1533-6 |
Language: | English |
Item ID: | 1533 |
Date Deposited: | 04. Apr 2007 |
Last Modified: | 04. Nov 2020, 12:45 |