Statistical inferences by semiparametric empirical likelihood methods

Hitomi通过蒙特卡罗模拟研究了经验似然估计的偏差，其中使用标准线性模型设置作为DGP，并与ET和GMM估计进行了比较。Hitomi和Nishiyama（2005）研究了在半参数设置中如何出现渐进方差方面的令人困惑的情况。通常情况下，如果估计和插入干扰参数，感兴趣的参数的渐近方差往往很大，但也有相反的情况。它们提供了发生时的充分必要条件。Nishiyama和Robinson（2005）获得了半参数平均导数的二阶有效Edgeworth展开式，并检查了Bootstrap分布的差异。Liu和Nishiyama通过模拟研究了具有跳跃的扩散过程的经验似然估计的性能。结果表明，对于某些参数，EL算法的性能优于GMM算法。Nishiyama， Liu和Sueishi（2005）比较了在矩条件下包含非参数泛函的半参数模型的EL、GMM估计。Morimune和Hoshino建议在数据包含异质观测时使用Bootstrap。Tanizaki出版社。研究了Cressie-Read功率发散测试的小样本功率特性。

Hitomi studied the bias of the empirical likelihood estimators by a Monte Carlo simulation, where standard linear model setup is used as the DGP, also comparing with the ET and GMM estimators.Hitomi and Nishiyama (2005) investigated how a puzzling situation in terms of asymptotic variance occurs in semiparametric settings. It is normally the case that if nuisance parameters are estimated and plugged in, the asymptotic variance of the parameters of interest tends to be large, but there are cases the opposite is true. They provided a necessary and sufficient condition when it happens.Nishiyama and Robinson (2005) obtained a valid Edgeworth expansion of order two for the semiparametric averaged derivatives and checked the difference from the Bootstrap distribution.Liu and Nishiyama studied the performance of empirical likelihood estimators of diffusion processes with jumps by simulation. It is shown that EL performs better than the GMM for some parameters.Nishiyama, Liu and Sueishi (2005) compared EL, GMM estimators for semiparametric models which include nonparametric functionals in the moment condition.Morimune and Hoshino proposed to use Bootstrap when data includes heterogeneous observations.Tanizaki et.al. studied small sample power properties of Cressie-Read power divergence test.