Semi- and Nonparametric Modeling of Ordinal Data
Semi- and Nonparametric Modeling of Ordinal Data
复制标题
序数数据的半参数和非参数建模
DOI：
10.1198/1061860031310
复制
发表时间：
2003
影响因子：
2.4
通讯作者：
G. Tutz
中科院分区：
文献类型：
--
作者：
G. Kauermann;G. Tutz
分类有序响应变量的参数模型，如比例赔率模型或延续比模型，假设预测值是由协变量的线性形式给出的。在这篇文章中，参数模型被扩展为包括半参数或部分参数方式的平滑分量。协变量的一部分由此被线性建模，而其他协变量被建模为未指定但光滑的函数。估计是基于局部似然和轮廓似然的组合，并导出了估计的渐近性质。在一项模拟研究中，证明了轮廓似然方法比反拟合法更可取。两个数据实例验证了模型的适用性。
Parametric models for categorical ordinal response variables, like the proportional odds model or the continuation ratio model, assume that the predictor is given by a linear form of covariates. In this article the parametric models are extended to include smooth components in a semiparametric or partially parametric fashion. Parts of the covariates are thereby modeled linearly while other covariates are modeled as unspecified but smooth functions. Estimation is based on a combination of local likelihood and profile likelihood and asymptotic properties of the estimates are derived. In a simulation study it is demonstrated that the profile likelihood approach is to be preferred over a backfitting procedure. Two data examples demonstrate the applicability of the models.