We develop a systematic, omnibus approach to goodness-of-fit testing for parametric distributional models when the variable of interest is only partially observed due to censoring and/or truncation. In many such designs, tests based on the nonparametric maximum likelihood estimator are hindered by nonexistence, computational instability, or convergence rates too slow to support reliable calibration under composite nulls. We avoid these difficulties by constructing a regular (pathwise differentiable) Neyman-orthogonal score process indexed by test functions, and aggregating it over a reproducing kernel Hilbert space ball. This yields a maximum-mean-discrepancy-type supremum statistic with a convenient quadratic-form representation. Critical values are obtained via a multiplier bootstrap that keeps nuisance estimates fixed. We establish asymptotic validity under the null and local alternatives and provide concrete constructions for left-truncated right-censored data, current status data, and random double truncation; in particular, to the best of our knowledge, we give the first omnibus goodness-of-fit test for a parametric family under random double truncation in the composite-hypothesis case. Simulations and an empirical illustration demonstrate size control and power in practically relevant incomplete-data designs.

翻译：本文针对变量因删失和/或截断而仅被部分观测的情形，提出了一种系统性的、通用的参数分布模型拟合优度检验方法。在此类设计中，基于非参数最大似然估计的检验常因估计量不存在、计算不稳定或收敛速度过慢而难以在复合原假设下实现可靠的校准。为克服这些困难，我们构建了一个由检验函数索引的正则（路径可微）Neyman正交得分过程，并在再生核希尔伯特空间球上对其进行聚合。由此得到一个具有便捷二次型表示的最大均值差异型上确界统计量。临界值通过固定多余参数估计的乘子自助法获得。我们证明了该方法在原假设和局部备择假设下的渐近有效性，并针对左截断右删失数据、现时状态数据及随机双重截断数据给出了具体构造方案；特别地，据我们所知，本文首次在复合假设情形下为随机双重截断数据提供了针对参数族模型的通用拟合优度检验。模拟实验与实证分析表明，该方法在实际相关的不完全数据设计中具有良好的尺寸控制能力与检验功效。