Testing Goodness of Fit with Multinomial Data
提出几种新的拟合优度检验方法，通过自适应选择阶数的Neyman平滑型检验，在检测特定备择假设时优于经典方法，模拟和实例验证了其有效性。
Abstract Several new test procedures are proposed for assessing the goodness of fit of a postulated multinomial distribution. The new tests are Neyman smooth-type tests with orders selected adaptively from the data. They are shown, through Fourier and large-sample analyses, to provide potential improvements over classical methods in terms of their ability to detect certain types of alternatives. Simulation results and a real example illustrate the finite-sample validity of the large-sample theory and the practical utility of the proposed methods.