Modeling longitudinal data with ordinal response by varying coefficients

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