Tests of Independence in Parametric Models With Applications and Illustrations
利用联合分布的级数展开理论，推导出适用于离散和连续变量的独立性检验方法，并通过蒙特卡洛模拟和澳大利亚医疗利用数据验证其性能。
Tests of independence between variables in a wide variety of discrete and continuous bivariate and multivariate regression equations are derived using results from the theory of series expansions of joint distributions in terms of marginal distributions and their related orthonormal polynomials. The tests are conditional moment tests based on covariances between pairs of orthonormal polynomials. Examples include tests of serial independence against bilinear and/or autoregressive conditional heteroscedasticity alternatives, tests of dependence in multivariate normal regression models, and dependence in count-data models. Monte Carlo simulation based on bivariate count models is used to evaluate the. size and power properties of the proposed tests. A multivariate count-data model for Australian health-care-utilization data is used to empirically illustrate the tests.