Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response
有序预测变量单调性约束下的比例优势累积 logit 模型推断
The proportional odds cumulative logit model (POCLM) is a standard regression model for an ordinal response. Ordinality of predictors can be incorporated by monotonicity constraints for the corresponding parameters. It is shown that estimators defined by optimization, such as maximum likelihood estimators, for an unconstrained model and for parameters in the interior set of the parameter space of a constrained model are asymptotically equivalent. This is used in order to…