本页提供题录、可核实摘要与阅读线索。AI 解读未完成全文核验，可能有误；具体假设、证明和结论请核对原文。仅依据书目生成的内容属于阅读导航，不是对论文结论的确认。
摘要保留来源文本，未经模型改写。材料不足时保留待解读状态；引用采用 OpenAlex 单条关联记录，不合计版本，缺失不视为零，也不代表论文质量。
正在核对对应版本的资源…
SUMMARY In this paper it is shown how ridge estimators can be used in logistic regression to improve the parameter estimates and to diminish the error made by further predictions. Different ways to choose the unknown ridge parameter are discussed. The main attention focuses on ridge parameters obtained by cross-validation. Three different ways to define the prediction error are considered: classification error, squared error and minus log-likelihood. The use of ridge regression is illustrated by developing a prognostic index for the two-year survival probability of patients with ovarian cancer as a function of their deoxyribonucleic acid (DNA) histogram. In this example, the number of covariates is large compared with the number of observations and modelling without restrictions on the parameters leads to overfitting. Defining a restriction on the parameters, such that neighbouring intervals in the DNA histogram differ only slightly in their influence on the survival, yields ridge-type parameter estimates with reasonable values which can be clinically interpreted. Furthermore the model can predict new observations more accurately.