Case-1 and case-2 interval-censored failure time data commonly occur in medical research as well as other fields and many methods have been developed for their analysis under different frameworks. In this paper, we consider regression analysis of such data and present a general class of nonparametric transformation models. One major advantage of these models is their flexibility and generality as they include the linear transformation model as a special case. For estimation of regression parameters, we propose a weighted rank (WR) estimation procedure and establish the consistency and asymptotic normality of the resulting estimator. Furthermore, to estimate the asymptotic covariance matrix of the proposed estimator, a resampling technique, which does not involve nonparametric density estimation or numerical derivatives, is developed. A numerical study is also conducted and suggests that the proposed methodology works well in practice. Finally an application is provided.

案例 1 和案例 2 间隔删失的失效时间数据通常出现在医学研究以及其他领域，并且已经开发了许多方法来在不同的框架下对其进行分析。在本文中，我们考虑对此类数据进行回归分析，并提出一类通用的非参数变换模型。这些模型的一个主要优点是它们的灵活性和通用性，因为它们包括作为特殊情况的线性变换模型。对于回归参数的估计，我们提出了加权秩 (WR) 估计程序，并建立了所得估计量的一致性和渐近正态性。此外，为了估计所提出的估计器的渐近协方差矩阵，开发了一种不涉及非参数密度估计或数值导数的重采样技术。还进行了一项数值研究，并表明所提出的方法在实践中运作良好。最后提供了一个应用程序。