This paper introduces a novel goodness-of-fit test technique for parametric conditional distributions. The proposed tests are based on a residual marked empirical process, for which we develop a conditional Principal Component Analysis. The obtained components provide a basis for various types of new tests in addition to the omnibus one. Component tests that based on each component serve as experts in detecting certain directions. Smooth tests that assemble a few components are also of great use in practice. To further improve testing efficiency, we introduce a component selection approach, aiming to identify the most contributory components. The finite sample performance of the proposed tests is illustrated through Monte Carlo experiments.

翻译：本文提出一种新的参数化条件分布拟合优度检验技术。所提检验基于残差标记经验过程，并为其发展了条件主成分分析。获得的成分除可用于构建综合检验外，还为各类新型检验提供了基础。基于各成分的单一成分检验如同专家系统，可灵敏检测特定方向的偏离。而在实践中，整合少量成分的平滑检验同样具有重要应用价值。为提升检验效率，本文进一步引入成分选择方法，旨在识别最具贡献性的成分。通过蒙特卡洛实验验证了所提检验在有限样本下的表现性能。