Caroni Chrys, Crowder Martin, Kimber Alan
Department of Mathematics, School of Mathematical and Physical Sciences, 157 80 Athens, Greece.
Lifetime Data Anal. 2010 Jul;16(3):374-84. doi: 10.1007/s10985-010-9151-3. Epub 2010 Jan 29.
We extend proportional hazards frailty models for lifetime data to allow a negative binomial, Poisson, Geometric or other discrete distribution of the frailty variable. This might represent, for example, the unknown number of flaws in an item under test. Zero frailty corresponds to a limited failure model containing a proportion of units that never fail (long-term survivors). Ways of modifying the model to avoid this are discussed. The models are illustrated on a previously published set of data on failures of printed circuit boards and on new data on breaking strengths of samples of cord.
我们将用于生存时间数据的比例风险脆弱性模型进行扩展，以使脆弱性变量服从负二项分布、泊松分布、几何分布或其他离散分布。例如，这可能代表被测物品中未知的缺陷数量。零脆弱性对应于一个有限失败模型，其中包含一定比例永远不会失败的单元（长期存活者）。文中讨论了修改模型以避免这种情况的方法。这些模型通过之前发表的一组印刷电路板故障数据以及关于绳索样本断裂强度的新数据进行了说明。
