Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

A comparative study of confidence intervals to assess biosimilarity from analytical data

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods.     

极值理论高频VaR区间估计模型的构建

为了对在险值的估计精度进行度量,更为精确和有效地衡量极值VaR（value at risk）的估计风险,基于广义极值理论构建了极值VaR的区间估计模型,并进一步利用高频数据重点考察了不同置信水平和不同样本容量分块下的极值VaR区间估计结果的精度和模型的有效性。结果表明,极值VaR的动态区间估计模型与参数法和非参数法区间估计模型相比,不仅能够更为有效地捕获极端条件下收益率时间序列的动态特征,而且具有很好的估计精度,VaR估计风险的精确度更高。     

"中国信心":当代中华民族凝聚力的现实解读

"中国信心"不仅仅是一个新闻词汇或政治口号,而是引领当代中国社会发展的民族精神和时代精神的精华.在内涵上,"中国信心"是民族性与时代性的辩证统一,是中国特色社会主义理论体系的重要内容;在本质上,"中国信心"是成就感与使命感的高度结合,是社会主义精神文明建设的时代展示;在功能定位上,"中国信心"是当代中华民族凝聚力增强的鲜明亮点,对提升国家文化软实力具有重要意义.     

论实现“中国梦”的路径自信

自近代以来,中华民族和中国人民就萌生了实现民族振兴、国家富强、人民幸福的“中国梦”。历经170多年的上下求索,特别是在中国共产党领导下的革命、建设与改革开放实践,中国人民有了实现“中国梦”的路径自信：第一,道路自信,即坚定不移地走中国特色社会主义道路;第二,精神自信,即大力弘扬民族精神和时代精神;第三,力量自信,即坚定不移走和平发展道路,凝聚中国与世界人民的历史性合力。当下,全党全国人民要坚定这样的路径自信,实现中国梦。     

消费者网络购买认可度影响因素分析

文章以具有网络购物行为的网民为样本,利用SPSS软件对影响网络购买认可度的相关因素进行了效度分析、信度分析和相关性检验;分析这些因素对于购物网站认可度的影响作用,并提出了提高网站购买认可度的措施。     

指数分布参数基于不完全数据的区间估计

对不完全样本观测数据,讨论了指数分布总体参数的区间估计;给出了构造置信区间的一种方法并推导出了相应的分布密度函数表达式;并说明了该方法在样本中可能存在异常值时的应用。     

A new confidence interval in measurement error model with the reliability ratio known

For the slope parameter of the measurement error model with the reliability ratio known, this article constructs a fiducial generalized confidence interval (FGCI) which is proved to have correct asymptotic coverage. Simulation results demonstrate that the FGCI often outperforms the existing intervals in terms of empirical coverage probability, average interval length, and false parameter coverage rate. Two examples are also provided to illustrate our approach.     

The performance of model averaged tail area confidence intervals

We investigate the exact coverage and expected length properties of the model averaged tail area (MATA) confidence interval proposed by Turek and Fletcher, CSDA, 2012, in the context of two nested, normal linear regression models. The simpler model is obtained by applying a single linear constraint on the regression parameter vector of the full model. For given length of response vector and nominal coverage of the MATA confidence interval, we consider all possible models of this type and all possible true parameter values, together with a wide class of design matrices and parameters of interest. Our results show that, while not ideal, MATA confidence intervals perform surprisingly well in our regression scenario, provided that we use the minimum weight within the class of weights that we consider on the simpler model.