AI 中文总结
该研究提出一种基于密度的遍历马尔可夫过程拟合优度检验方法，无需指定备择假设、无需估计平滑参数，对1/√n局部备择假设有效，为计量与金融建模问题提供新视角。
AI 中文摘要
我们提出了一种新的基于密度的遍历马尔可夫过程拟合优度检验方法。该方法将数据与原假设指定的模型类进行比较，若该类中没有模型能产生与数据匹配的平稳密度，则拒绝原假设；实施该检验无需指定备择假设。尽管该检验比较密度，但无需估计平滑参数，且对1/√n局部备择假设具有非平凡功效，为计量与金融建模中的若干现有问题提供了新视角。
英文摘要
We introduce a new density-based goodness of fit test for ergodic Markov processes. Our test compares the data against the class of models specified in the null hypothesis, and rejects if no model in the class yields a stationary density that matches with the data. No alternative needs to be specified in order to implement the test. Although our test compares densities, estimation of smoothing parameters is not required, and the test has nontrivial power against $1/\sqrt{n}$ local alternatives. The test provides new perspectives on some existing problems in econometric and financial modeling.
Comments40 pages, 3 figures
