Non-Parametric Logistic and Proportional Odds Regression
提出加性非参数逻辑斯蒂回归模型和比例优势模型，用于探索协变量效应形式或构建模型，适用于二元和序数响应数据。
SUMMARY We describe the additive non-parametric logistic regression model of the form logit[P(x)] ==a+ -fj(xj), where P(x) = P(y = 1 1 x) for a 0-1 variable y, x is a vector of p covariates, and the f; are general real-valued functions. Each of the f; can be chosen to be either linear, general non-linear (estimated by a scatterplot smoother) or step functions for discrete covariates. The functions are estimated simultaneously using the local scoring algorithm. The model can be used as an exploratory tool for uncovering the form of covariate effects or it can be used in a more formal manner in model building. We also describe the additive proportional odds model logit[yk(x)] = Ik)-fj(X1) for ordinal response data. Here Yk is the probability of the response being at most k: yk(X) = P( Y ? k I x). Both these models are motivated and described in detail, and several examples are given.