本文建立了弱鞅的一些极大值不等式,这些不等式推广和改进了Christofides在Maximalinequalities for demimartingale and astrong law of large numbers.Statist ProbabLett,50:357–363(2000)中的结果.利用得到的极大值不等式,可以得到其它一些结果,例如弱鞅的Doob型极大值不等式、弱鞅和PA序列的强大数定律和强收敛速度.最后,还给出了弱半鞅一致可积性的一个等价条件.
对多维自适应设计广义线性模型中形如sum from i=1 to n xi(yi-μ(x′iβ))=0的拟似然方程,在limn→∞■^(3/4)/■=0和其他一些正则性的假定之下,论文证明了上述拟似然方程的解,即极大拟似然估计的渐近正态性,此结果推广和改善了文[4]中的相关结果,其中■和■分别为sum from i=1 to n xix′i的最小特征根和最大特征根,x是有界的p×q阶设计矩阵.
Let {X n , n ≥ 1} be an arbitrary sequence of random variables. Some convergence results for the partial sums of arbitrary sequence of random variables are obtained, which generalize the known results for independent sequences, NA sequences, ρ-mixing sequences and φ-mixing sequences, and so on.
Linear mixed models (LMMs) have become an important statistical method for analyzing cluster or longitudinal data. In most cases, it is assumed that the distributions of the random effects and the errors are normal. This paper removes this restrictions and replace them by the moment conditions. We show that the least square estimators of fixed effects are consistent and asymptotically normal in general LMMs. A closed-form estimator of the covariance matrix for the random effect is constructed and its consistent is shown. Based on this, the consistent estimate for the error variance is also obtained. A simulation study and a real data analysis show that the procedure is effective.
