A characteristic function-based approach to approximate maximum likelihood estimation

The choice of the summary statistics in approximate maximum likelihood is often a crucial issue. We develop a criterion for choosing the most effective summary statistic and then focus on the empirical characteristic function. In the iid setting, the approximating posterior distribution converges to the approximate distribution of the parameters conditional upon the empirical characteristic function. Simulation experiments suggest that the method is often preferable to numerical maximum likelihood. In a time-series framework, no optimality result can be proved, but the simulations indicate that the method is effective in small samples.     

NF-κB在糖尿病大鼠动脉粥样硬化作用机制的研究

目的探讨NF-κB在糖尿病大鼠动脉粥样硬化病变形成中的作用。方法Wistar大鼠随机分三组：链脲佐菌素诱导的糖尿病组（DM组）、NAC治疗组（NAC组）、对照组（C组）。分别于4w、8w、12w、16w检测血糖、胆固醇（TC）、甘油三酯（TG）、糖化血红蛋白（HbA1C）水平。于相应时间分别处死大鼠并分离和摘取主动脉,采用SP免疫组化或凝胶电泳迁移率（elec-trophoretic mobility Shift assay,EMSA）方法测定主动脉内皮NF-κB的DNA结合活性。结果①DM组、NAC组的血糖及HbA1C水平显著高于C组。②DM及NAC二组的TC、TG在4w、8w无显著变化,TC、TG在12w、16w时显著高于C组。③在12w、16w时,NF-κB结合活性在DM大鼠主动脉较对照组明显增强,NAC组较DM组减弱。④HE染色在DM组于12w、16w时出现AS的早期病理改变,而在NAC和C组未发现AS的病理改变。结论①DM大鼠大血管存在NF-κB的激活,并启动下游靶基因粘附分于ICAM-1的表达,进而导致DM大鼠动脉粥样硬化的发生与发展。因此NF-κB的激活可能是导致DM大鼠AS的始动因子之一。②高血糖是NF-κB不适当激活的又一因素,因此早期有效的控制血糖是防治DM鼠AS的基本策略。     

k点可删的ID-因子临界图的度条件

研究了κ点可删的ID-因子临界图的度条件，得到使图G是κ点可删的ID-因子临界图的度的下界，同时说明该结果是严格的．     

Classification of Higher-order Data with Separable Covariance and Structured Multiplicative or Additive Mean Models

Although devised in 1936 by Fisher, discriminant analysis is still rapidly evolving, as the complexity of contemporary data sets grows exponentially. Our classification rules explore these complexities by modeling various correlations in higher-order data. Moreover, our classification rules are suitable to data sets where the number of response variables is comparable or larger than the number of observations. We assume that the higher-order observations have a separable variance-covariance matrix and two different Kronecker product structures on the mean vector. In this article, we develop quadratic classification rules among g different populations where each individual has κth order (κ ≥2) measurements. We also provide the computational algorithms to compute the maximum likelihood estimates for the model parameters and eventually the sample classification rules.