Shrinkage of Variance for Minimum Distance Based Tests
在最小距离估计框架下，利用广义熵最大化得到的隐含概率来改进方差估计，从而提升得分检验的有限样本性质，并通过理论展开和蒙特卡洛模拟验证其优于传统方法和自助法。
This paper promotes information theoretic inference in the context of minimum distance estimation. Various score test statistics differ only through the embedded estimator of the variance of estimating functions. We resort to implied probabilities provided by the constrained maximization of generalized entropy to get a more accurate variance estimator under the null. We document, both by theoretical higher order expansions and by Monte-Carlo evidence, that our improved score tests have better finite-sample size properties. The competitiveness of our non-simulation based method with respect to bootstrap is confirmed in the example of inference on covariance structures previously studied by Horowitz (1998).