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通过马尔可夫链蒙特卡洛随机逼近计算空间模型的最大似然估计AI 解读

Summary We propose a two-stage algorithm for computing maximum likelihood estimates for a class of spatial models. The algorithm combines Markov chain Monte Carlo methods such as the Metropolis–Hastings–Green algorithm and the Gibbs sampler, and stochastic approximation methods such as the off-line average and adaptive search direction. A new criterion is built into the algorithm so stopping is automatic once the desired precision has been set. Simulation studies and…

时间序贯删失秩统计量的弱收敛及临床试验序贯检验应用AI 解读

A general weak convergence theory is developed for time-sequential censored rank statistics in the two-sample problem of comparing time to failure between two treatment groups, such as in the case of a clinical trial in which patients enter serially and, after being randomly allocated to one of two treatments, are followed until they fail or withdraw from the study or until the study is terminated. Applications of the theory to time-sequential tests based on these censored…