论文检索

Edwards与Lauritzen的TM算法收敛速率AI 解读

Edwards & Lauritzen (2001) have recently proposed the TM algorithm for finding the maximum likelihood estimate when the likelihood can be truly or artificially regarded as a conditional likelihood, and the full likelihood is more easily maximised. They have presented a proof of convergence, provided that the algorithm is supplemented by a line search. In this note a simple expression, in terms of observed information matrices, is given for the convergence rate of the…

抽样调查精度估计：方差估计量的选择准则AI 解读

We advocate the 'mean squared error of predicted squared error' as a universal criterion for the choice between variance estimators in sample survey inference. The predictive nature of the measure rewards variance estimators adapted to the amount of information in the actual sample. This makes the new measure more satisfactory than the simpler mean squared error of the variance estimator. The criterion turns out to be the same for design-based as for model-based inference,…