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。     

论青年女性流动人口初育间隔与人口控制——来自厦门流动人口家庭问卷调查的启示

青年女性流动人口实际生育行为的控制对于人口规模与结构的控制,起着至关重要的作用。而生育时间的选择作为影响流动人口生育实践的一个重要因素,体现在对各孩次生育间隔的控制上。本文在描述分析的基础上,应用Cox比例风险模型,主要对厦门市青年女性流动人口初育间隔的影响因素进行估计和检验,并在总结分析的基础上对流动人口控制问题提出几点对策思考。     

经济周期、模型再造与中国经济增长波动的论证——中国“十一五“经济增长速度与波动区间

发达国家经济学家经济增长周期理论虽然基本没有完全脱离实际,但都存在片面性,因此必须依据马克思的思维方式才能建立起科学的经济增长与周期分析模型。周期性研究目的在于理论联系实际指导中国的具体经济增长,通过分析影响中国一定时期内经济的适度速度、波动区间及持续时间,规划时期内投资、消费与外贸,才能推进经济适度快速增长和时间的持续。     

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。     

New evidence about effects of reproductive variables on child mortality in sub-Saharan Africa

There is still considerable uncertainty about how reproductive factors affect child mortality. This study, based on Demographic and Health Survey data from 28 countries in sub-Saharan Africa, shows that mortality is highest for firstborn children with very young mothers. Other children with young mothers, or of high birth order, also experience high mortality. Net of maternal age and birth order, a short preceding birth interval is associated with above average mortality. These patterns change, however, if time-invariant unobserved mother-level characteristics of importance for both mortality and fertility are controlled for in a multilevel–multiprocess model. Most importantly, there are smaller advantages associated with longer birth intervals and being older at first birth. The implications of alternative reproductive ‘strategies’ are discussed, taking into account that if the mother is older at birth, the child will also be born in a later calendar year, when mortality may be lower.     

On construction of asymptotically correct confidence intervals

In this paper we discuss constructing confidence intervals based on asymptotic generalized pivotal quantities (AGPQs). An AGPQ associates a distribution with the corresponding parameter, and then an asymptotically correct confidence interval can be derived directly from this distribution like Bayesian or fiducial interval estimates. We provide two general procedures for constructing AGPQs. We also present several examples to show that AGPQs can yield new confidence intervals with better finite-sample behaviors than traditional methods.     

Exact average coverage probabilities and confidence coefficients of confidence intervals for discrete distributions

For a confidence interval (L(X),U(X)) of a parameter θ in one-parameter discrete distributions, the coverage probability is a variable function of θ. The confidence coefficient is the infimum of the coverage probabilities, inf  _θ P _θ (θ∈(L(X),U(X))). Since we do not know which point in the parameter space the infimum coverage probability occurs at, the exact confidence coefficients are unknown. Beside confidence coefficients, evaluation of a confidence intervals can be based on the average coverage probability. Usually, the exact average probability is also unknown and it was approximated by taking the mean of the coverage probabilities at some randomly chosen points in the parameter space. In this article, methodologies for computing the exact average coverage probabilities as well as the exact confidence coefficients of confidence intervals for one-parameter discrete distributions are proposed. With these methodologies, both exact values can be derived.     

Enabling robust assessment of QTc prolongation in early phase clinical trials

Since the implementation of the International Conference on Harmonization (ICH) E14 guideline in 2005, regulators have required a “thorough QTc” (TQT) study for evaluating the effects of investigational drugs on delayed cardiac repolarization as manifested by a prolonged QTc interval. However, TQT studies have increasingly been viewed unfavorably because of their low cost effectiveness. Several researchers have noted that a robust drug concentration‐QTc (conc‐QTc) modeling assessment in early phase development should, in most cases, obviate the need for a subsequent TQT study. In December 2015, ICH released an “E14 Q&As (R3)” document supporting the use of conc‐QTc modeling for regulatory decisions. In this article, we propose a simple improvement of two popular conc‐QTc assessment methods for typical first‐in‐human crossover‐like single ascending dose clinical pharmacology trials. The improvement is achieved, in part, by leveraging routinely encountered (and expected) intrasubject correlation patterns encountered in such trials. A real example involving a single ascending dose and corresponding TQT trial, along with results from a simulation study, illustrate the strong performance of the proposed method. The improved conc‐QTc assessment will further enable highly reliable go/no‐go decisions in early phase clinical development and deliver results that support subsequent TQT study waivers by regulators.     

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.