A Robust Test for Non-nested Hypotheses
提出一种稳健的Cox型检验统计量，用于在两个非嵌套假设间选择，解决了数据轻微污染对检验决策的严重影响，并通过数值模拟验证了其水平稳定性和渐近分布的良好近似。
Summary We propose a robust version of Cox-type test statistics for the choice between two non-nested hypotheses. We first show that the influence of small amounts of contamination in the data on the test decision can be very large. Secondly, we build a robust test statistic by using the results on robust parametric tests that are available in the literature and show that the level of the robust test is stable. Finally, we show numerically not only the robustness of this new test statistic but also that its asymptotic distribution is a good approximation of its sample distribution, unlike for the classical test statistic. We apply our results to the choice between a Pareto and an exponential distribution as well as between two competing regressors in the simple linear regression model without intercept.