The loglinear model under product-multinomial sampling with constraints is considered. The asymptotic expansion and normality of the restricted minimum C-divergence estimator （RMDE） which is a generalization of the maximum likelihood estimator is presented. Then various statistics based on C-divergence and RMCDE are used to test various hypothesis test problems under the model considered. These statistics contain the classical loglikelihood ratio test statistics and Pearson chi-squared test statistics. Ia the last section, a simulation study is implemented.
The Cox proportional hazards model is the most used statistical model in the analysis of survival time data.Recently,a random weighting method was proposed to approximate the distribution of the maximum partial likelihood estimate for the regression coefficient in the Cox model.This method was shown not as sensitive to heavy censoring as the bootstrap method in simulation studies but it may not be second-order accurate as was shown for the bootstrap approximation.In this paper,we propose an alternative random weighting method based on one-step linear jackknife pseudo values and prove the second accuracy of the proposed method.Monte Carlo simulations are also performed to evaluate the proposed method for fixed sample sizes.
LI Xiao 1 ,WU YaoHua 2,& TU DongSheng 1 1 Cancer Research Institute,Queen’s University,Kingston,Ontario K 7L 3N6,Canada
考虑了乘积多项抽样下的对数线性模型.在这个模型下,文献[Jin Y H,Wu Y H.Mini mumφ-divergence esti mator and hierarchical testing in log-linear models under product-multinomial sampling.Journal of Statistical Planning and Inference,2009,139:3 488-3 500]用基于-散度和最小-散度估计构造的统计量研究了几类假设检验问题,这其中就有嵌套假设.最小-散度估计是极大似然估计的推广.在上述文献的基础上,给出了其中一类检验的功效函数的渐近逼近公式;另外,还研究了在一列近邻假设下检验统计量的渐近分布.通过模拟研究发现,与Pearson型统计量和对数极大似然比统计量相比,Cressie-Read型检验统计量有差不多的甚至更好的模拟功效和水平.
Based on the empirical likelihood method, the subset selection and hypothesis test for parameters in a partially linear autoregressive model are investigated. We show that the empirical log-likelihood ratio at the true parameters converges to the standard chi-square distribution. We then present the definitions of the empirical likelihood-based Bayes information criteria （EBIC） and Akaike information criteria （EAIC）. The results show that EBIC is consistent at selecting subset variables while EAIC is not. Simulation studies demonstrate that the proposed empirical likelihood confidence regions have better coverage probabilities than the least square method, while EBIC has a higher chance to select the true model than EAIC.
This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration（Log-ACD） model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.
Zhao CHENWei LIUChristina Dan WANGWu-qing WUYao-hua WU
