Sequential Estimation of a Continuous Distribution Function from Delayed Observations

The problem of sequentially estimating a continuous distribution function is considered in the case when the observations become available at random times. A certain class of sequential estimation procedures which are composed of optimal stopping time and sequential minimum risk invariant estimator of a continuous distribution function is obtained under a nonparametric invariant loss function and with the observation cost determined by a convex function of the moment of stopping and the number of observations up to this moment.     

Economic design of continuous sampling plan under linear inspection cost

The article explores the problem of an economically based type I continuous sampling plan (CSP-1 plan) under linear inspection cost. By assuming that the per unit inspection cost is linearly proportional to the average number of inspections per inspection cycle, and by solving the modified Cassady et al.'s model, we not only have the required level of product quality but also obtain the minimum total expected cost per unit produced.     

Spatially Balanced Sampling of Continuous Populations

When sampling from a continuous population (or distribution), we often want a rather small sample due to some cost attached to processing the sample or to collecting information in the field. Moreover, a probability sample that allows for design‐based statistical inference is often desired. Given these requirements, we want to reduce the sampling variance of the Horvitz–Thompson estimator as much as possible. To achieve this, we introduce different approaches to using the local pivotal method for selecting well‐spread samples from multidimensional continuous populations. The results of a simulation study clearly indicate that we succeed in selecting spatially balanced samples and improve the efficiency of the Horvitz–Thompson estimator.     

Sample size calculation for a proportional hazards mixture cure model with nonbinary covariates

Sample size calculation is a critical issue in clinical trials because a small sample size leads to a biased inference and a large sample size increases the cost. With the development of advanced medical technology, some patients can be cured of certain chronic diseases, and the proportional hazards mixture cure model has been developed to handle survival data with potential cure information. Given the needs of survival trials with potential cure proportions, a corresponding sample size formula based on the log-rank test statistic for binary covariates has been proposed by Wang et al. [25]. However, a sample size formula based on continuous variables has not been developed. Herein, we presented sample size and power calculations for the mixture cure model with continuous variables based on the log-rank method and further modified it by Ewell's method. The proposed approaches were evaluated using simulation studies for synthetic data from exponential and Weibull distributions. A program for calculating necessary sample size for continuous covariates in a mixture cure model was implemented in R.     

A copula model for marked point processes

Many chronic diseases feature recurring clinically important events. In addition, however, there often exists a random variable which is realized upon the occurrence of each event reflecting the severity of the event, a cost associated with it, or possibly a short term response indicating the effect of a therapeutic intervention. We describe a novel model for a marked point process which incorporates a dependence between continuous marks and the event process through the use of a copula function. The copula formulation ensures that event times can be modeled by any intensity function for point processes, and any multivariate model can be specified for the continuous marks. The relative efficiency of joint versus separate analyses of the event times and the marks is examined through simulation under random censoring. An application to data from a recent trial in transfusion medicine is given for illustration.     

Stratified Double Sampling with Continuous Outcomes: Design and Analysis

We provide a method for estimating the sample mean of a continuous outcome in a stratified population using a double sampling scheme. The stratified sample mean is a weighted average of stratum specific means. It is assumed that the fallible and true outcome data are related by a simple linear regression model in each stratum. The optimal stratified double sampling plan, i.e. , the double sampling plan that minimizes the cost of sampling for fixed variances, or alternatively, minimizes the variance for fixed costs, is found and compared to a standard sampling plan. The design parameters are the total sample size and the number of doubly sampled units in each stratum. We show that the optimal double sampling plan is a function of the between-strata and within-strata cost and variance ratios. The efficiency gains, relative to standard sampling plans, under broad set of conditions, are considerable.     

Asymptotically pointwise optimal and asymptotically optimal stopping times in the Bayesian inference

The paper deals with the problem of determining asymptotically pointwise optimal and asymptotically optimal stopping times in the Bayesian inference. The sufficient conditions are given for a family of stopping times to be asymptotically pointwise optimal and asymptotically optimal with respect to a continuous time process. As an example a sequential estimation of the intensity of the Poisson process is considered. Under a gamma prior distribution, an asymptotically pointwise optimal and asymptotically optimal rule is given using a LINEX loss function and the cost c per unit time.     

Assessing accuracy of a continuous screening test in the presence of verification bias

Summary.  In studies to assess the accuracy of a screening test, often definitive disease assessment is too invasive or expensive to be ascertained on all the study subjects. Although it may be more ethical or cost effective to ascertain the true disease status with a higher rate in study subjects where the screening test or additional information is suggestive of disease, estimates of accuracy can be biased in a study with such a design. This bias is known as verification bias. Verification bias correction methods that accommodate screening tests with binary or ordinal responses have been developed; however, no verification bias correction methods exist for tests with continuous results. We propose and compare imputation and reweighting bias-corrected estimators of true and false positive rates, receiver operating characteristic curves and area under the receiver operating characteristic curve for continuous tests. Distribution theory and simulation studies are used to compare the proposed estimators with respect to bias, relative efficiency and robustness to model misspecification. The bias correction estimators proposed are applied to data from a study of screening tests for neonatal hearing loss.     

A computationally efficient nonparametric approach for changepoint detection

In this paper we build on an approach proposed by Zou et al. (2014) for nonparametric changepoint detection. This approach defines the best segmentation for a data set as the one which minimises a penalised cost function, with the cost function defined in term of minus a non-parametric log-likelihood for data within each segment. Minimising this cost function is possible using dynamic programming, but their algorithm had a computational cost that is cubic in the length of the data set. To speed up computation, Zou et al. (2014) resorted to a screening procedure which means that the estimated segmentation is no longer guaranteed to be the global minimum of the cost function. We show that the screening procedure adversely affects the accuracy of the changepoint detection method, and show how a faster dynamic programming algorithm, pruned exact linear time (PELT) (Killick et al. 2012), can be used to find the optimal segmentation with a computational cost that can be close to linear in the amount of data. PELT requires a penalty to avoid under/over-fitting the model which can have a detrimental effect on the quality of the detected changepoints. To overcome this issue we use a relatively new method, changepoints over a range of penalties (Haynes et al. 2016), which finds all of the optimal segmentations for multiple penalty values over a continuous range. We apply our method to detect changes in heart-rate during physical activity.     

Sequential precedence tests

In this paper we introduce the sequential precedence test (SPT) for testing the equality of two continuous distribution functions, against the stochastically ordered alternative. This procedure replaces the classical precedence test (PT), used in life-testing experiments, by a sequence of tests which are applied at the failure times of one of the samples. This allows the possibility of stopping the experiment earlier than the PT. By means of extensive Monte Carlo simulations and real data, we show that the proposed methodology results in substantial saving of experimental time and cost, without compromising in power. Algorithms for the implementation of the SPT are included.     

劳动力成本上升、加工贸易转移与企业出口附加值率攀升

中国出口国内附加值率（DVAR）持续攀升,伴随其上升的是用工成本的明显提高和加工贸易占比的显著下降,中国DVAR的提升究竟源于已有研究所指出的,投资自由化所带来的国内中间品对国外中间品的替代,即“主动”的提升？还是成本上升引致加工贸易不断退出市场的一个客观结果,即“被动”的提升？本文基于2000--2006年的微观数据,测算了企业层面的DVAR,从加工贸易退出的角度刻画了企业DVAR的动态演进过程,并进一步考察了最低工资所代理的劳动力成本、加工贸易与企业出口DVAR之间的关系。实证研究表明：最低工资所代理的劳动力成本上升会促使中国企业DVAR的攀升,这一结果在多个维度的稳健性检验中均是高度稳健的。最低工资上升对中国企业DVAR的促进作用通过引致加工贸易退出这一机制来实现。拓展性的研究还表明,最低工资上升对高生产率企业、外资企业和资本相对密集企业DVAR的正面促进作用更强。我们的发现意味着,中国DVAR的上升至少部分是加工贸易产业转移的结果,鼓励进口,同时促进国内中间品质量提升是我国现阶段发展的重要政策方向。     

巨灾、巨灾保险与中国模式

灾害是伴随人类社会发展而不断发展的异常现象,巨灾更以其巨大的破坏力及惨烈后果而给人类社会带来严重的威胁。巨灾不可避免,巨灾损失也不可避免,因此,必须正视巨灾发生及其发展规律,对巨灾损失除建立正常的补偿机制,还有必要建立专门的巨灾保险制度。中国的巨灾保险制度应当根据巨灾种类及相关国情要素体现出自己的特点。     

Efficient ascertainment schemes for maximum likelihood estimation

While well chosen sampling schemes may substantially increase efficiency of observational studies, some sampling schemes may instead decrease efficiency. Rules of thumb how to choose sampling schemes are only available for some special cases. In this paper we provide tools to compare efficiencies, and cost adjusted efficiencies, of different sampling schemes, in order to facilitate this choice. The method can be used for both categorical and continuous outcome variables. Some examples are presented, focusing on data from ascertainment sampling schemes. A Monte Carlo method is used to overcome computational issues wherever needed. The results are illustrated in graphs.     

中国经济持续增长的动力、格局与面临的重要挑战

主导产业及产业群的发展与转换是中国经济增长的内在动力。改革开放以来,几个时期经济的快速增长都有相应的一组主导产业的带动,几经波动到2006年初经济增长在新水平上达到了相对均衡。但2007年以来,中国经济持续增长面临着成本推动型通货膨胀、可持续发展长效机制建立、有效控制资产泡沫等方面的压力与挑战,因此必须通过提高劳动生产率化解要素涨价的压力;通过构建外部成本内部化的市场机制解决资源的高消耗和环境破坏问题;要汲取国际上先行者的经验教训,利用好后发优势,比较平稳地渡过资本化进程中最不稳定的时期,以进一步深化改革推动经济发展方式的转型。     

Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion

It is well known that statistical classifiers trained from imbalanced data lead to low true positive rates and select inconsistent significant variables. In this article, an improved method is proposed to enhance the classification accuracy for the minority class by differentiating misclassification cost for each group. The overall error rate is replaced by an alternative composite criterion. Furthermore, we propose an approach to estimate the tuning parameter, the composite criterion, and the cut-point simultaneously. Simulations show that the proposed method achieves a high true positive rate on prediction and a good performance on variable selection for both continuous and categorical predictors, even with highly imbalanced data. An illustrative example of the analysis of the suboptimal health state data in traditional Chinese medicine is discussed to show the reasonable application of the proposed method.     

Double sampling for exact values in the normal discriminant model with application to binary regression

Increasing attention is being given to problems involving binary outcomes with covariates subject to measurement error. Here, we consider the two group normal discriminant model where a subset of the continuous variates are subject to error and will typically be replaced by a vector of surrogates, perhaps of different dimension. Correcting for the measurement error is made possible by a double sampling scheme in which the surrogates are collected on all units and true values are obtained on a random subset of units. Such a scheme allows us to consider a rich set of measurement error models which extend the traditional additive error model. Maximum likelihood estimators and their asymptotic properties are derived under a variety of models for the relationship between true values and the surrogates. Specific attention is given to the coefficients in the resulting logistic regression model. Optimal allocations are derived which minimize the variance of the estimated slope subject to cost constraints for the case where there is a univariate covariate but a possibly multivariate surrogate.     

Optimal control of the surplus in an insurance policy

A classical continuous time surplus process is modified by adding two actions. If the level of the surplus goes below $\tau \ge 0$, we increase the level of the surplus up to initial level $u>\tau$ by injecting capital to the surplus. Meanwhile, the excess amount of the surplus over $V>u$ is invested continuously to other business. After assigning several costs related to managing the surplus, we obtain the long-run average cost per unit time and illustrate a numerical example to show how to find an optimal investment policy minimizing the cost.     

社会保险缴费对企业雇佣工资和规模的影响研究

社会保险与劳动力市场就业和工资的关系备受关注,其本质在于社保缴费对企业雇佣工资和雇佣规模的影响。本文基于企业薪酬调查与第三次经济普查对接形成的“企业-员工”匹配数据,从企业雇佣决策的整体性出发,将缴费负担及其影响置于我国缴费监管的特殊制度背景之下,运用系统估计法模拟社保缴费对企业雇佣工资和雇佣规模的影响。研究发现：企业实际承担的社保缴费费率越高,其雇佣工资显著越低、雇佣规模显著越小,社会保险以降低工资和减少就业的方式对劳动力市场运行存在双重影响;从标准化回归系数看,缴费负担对雇佣工资的影响强于对雇佣规模的影响,但雇佣规模缩减不仅是劳动者就业机会的损失更是企业经营风险的积累,对劳动力市场的影响尤其需要关注;从雇佣工资影响的程度看,缴费负担极大部分由企业承担,雇佣规模因缴费负担而缩减是用工成本上升的直接体现;从雇佣工资影响的结构看,缴费负担对职工福利有显著负影响、对奖金津贴形式的工资部分有更强的负影响,进一步反映出缴费负担形成的成本压力不可轻视,成本压力持续积累对劳动力市场运行的影响将是深远的。鉴于我国社会保险制度费率偏高同时逃避费行为较普遍的事实,鉴于未来劳动工资上涨、劳动供给结构性乃至整体性相对不足的基本趋势,建议合理缴费负担,同时严格缴费执法,以此谋求社会保险制度与劳动力市场运行的有效协同。     

Optimization of continuous and discrete scheduled times for a cumulative damage system with age-dependent maintenance

Abstract

This paper presents a preventive replacement problem when a system is operating successive works with random times and suffering stochastic shocks. The works cause random amount additive damage to the system, and the system fails whenever the cumulative damage reaches a failure level threshold. As an external shock occurs, the system experiences one of the two types of shocks with age-dependent maintenance mechanism: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. To control the deterioration process, preventive replacement is scheduled to replace the system at a continuous age T or at a discrete number N of working cycles, whichever occurs first, and corrective replacement is performed immediately whenever the system fails due to either shock or damage. The optimal preventive replacement schedule that minimizes the expected cost rate is discussed analytically and computed numerically. The proposed model provides a general framework for analyzing maintenance policies and extends several existing results.     

政府统计时间成本研究

现有的政府统计成本控制往往注重于业务费用的消耗上,而忽略时间的消耗（即时间成本）。本文从时间维度研究政府统计成本问题;给出了政府统计时间成本的定义;并将政府统计时间成本划分为事前时间成本、事中时间成本、事后时间成本（包括事后内部损失时间成本和事后外部损失时间成本）;具体分析了政府统计时间成本控制存在的问题;并提出了相应的控制策略。无疑,本项研究能指导实务界更好地认识与控制政府统计时间成本。