Detection of interactions in experiments on large numbers of factors

One of the main advantages of factorial experiments is the information that they can offer on interactions. When there are many factors to be studied, some or all of this information is often sacrificed to keep the size of an experiment economically feasible. Two strategies for group screening are presented for a large number of factors, over two stages of experimentation, with particular emphasis on the detection of interactions. One approach estimates only main effects at the first stage (classical group screening), whereas the other new method (interaction group screening) estimates both main effects and key two-factor interactions at the first stage. Three criteria are used to guide the choice of screening technique, and also the size of the groups of factors for study in the first-stage experiment. The criteria seek to minimize the expected total number of observations in the experiment, the probability that the size of the experiment exceeds a prespecified target and the proportion of active individual factorial effects which are not detected. To implement these criteria, results are derived on the relationship between the grouped and individual factorial effects, and the probability distributions of the numbers of grouped factors whose main effects or interactions are declared active at the first stage. Examples are used to illustrate the methodology, and some issues and open questions for the practical implementation of the results are discussed.     

Bayesian inference of asymmetric stochastic conditional duration models

This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes and innovations of observed duration process and latent log duration process. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allow comparison of our models and methods to those existing in the literature.     

A case–control study relating railroad worker mortality to diesel exhaust exposure using a threshold regression model

A case–control study of lung cancer mortality in U.S. railroad workers in jobs with and without diesel exhaust exposure is reanalyzed using a new threshold regression methodology. The study included 1256 workers who died of lung cancer and 2385 controls who died primarily of circulatory system diseases. Diesel exhaust exposure was assessed using railroad job history from the US Railroad Retirement Board and an industrial hygiene survey. Smoking habits were available from next-of-kin and potential asbestos exposure was assessed by job history review. The new analysis reassesses lung cancer mortality and examines circulatory system disease mortality. Jobs with regular exposure to diesel exhaust had a survival pattern characterized by an initial delay in mortality, followed by a rapid deterioration of health prior to death. The pattern is seen in subjects dying of lung cancer, circulatory system diseases, and other causes. The unique pattern is illustrated using a new type of Kaplan–Meier survival plot in which the time scale represents a measure of disease progression rather than calendar time. The disease progression scale accounts for a healthy-worker effect when describing the effects of cumulative exposures on mortality.     

Estimation of Causal Odds of Concordance using the Aalen Additive Model

A simple summary of a treatment effect is attractive, which is part of the explanation of the success of the Cox model when analysing time‐to‐event data since the relative risk measure is such a convenient summary measure. In practice, however, the Cox model may fail to give a reasonable fit, very often because of time‐changing treatment effect. The Aalen additive hazards model may be a good alternative as time‐changing effects are easily modelled within this model, but results are then evidently more complicated to communicate. In such situations, the odds of concordance measure (OC) is a convenient way of communicating results, and recently Martinussen & Pipper (2012) showed how a variant of the OC measure may be estimated based on the Aalen additive hazards model. In this study, we propose an estimator that should be preferred in observational studies as it always estimates the causal effect on the chosen scale, only assuming that there are no un‐measured confounders. The resulting estimator is shown to be consistent and asymptotically normal, and an estimator of its limiting variance is provided. Two real applications are provided.     

The Method to Identify a Biomarker and to Evaluate Its Efficiency for Survival by Using the Joint Model of the Accelerate Failure Time and Longitudinal Data

In this article, we discuss how to identify longitudinal biomarkers in survival analysis under the accelerated failure time model and also discuss the effectiveness of biomarkers under the accelerated failure time model. Two methods proposed by Shcemper et al. are deployed to measure the efficacy of biomarkers. We use simulations to explore how the factors can influence the power of a score test to detect the association of a longitudinal biomarker and the survival time. These factors include the functional form of the random effects from the longitudinal biomarkers, in the different number of individuals, and time points per individual. The simulations are used to explore how the number of individuals, the number of time points per individual influence the effectiveness of the biomarker to predict survival at the given endpoint under the accelerated failure time model. We illustrate our methods using a prothrombin index as a predictor of survival in liver cirrhosis patients.     

On the use of partial confounding for the construction of alternative regular two-level blocked fractional factorial designs

In this paper, we consider experimental situations in which a regular fractional factorial design is to be used to study the effects of m two-level factors using n=2^m−k experimental units arranged in 2^p blocks of size 2^m−k−p. In such situations, two-factor interactions are often confounded with blocks and complete information is lost on these two-factor interactions. Here we consider the use of the foldover technique in conjunction with combining designs having different blocking schemes to produce alternative partially confounded blocked fractional factorial designs that have more estimable two-factor interactions or a higher estimation capacity or both than their traditional counterparts.     

陕西省生产性服务业集群效应研究

在工业化和信息化的大背景下,生产性服务业在国家和地区的发展中占有越来越重要的地位,故对陕西省生产性服务业集群效应进行实证研究,研究结果表明：生产性服务业集群对陕西省工业发展的影响明显,影响程度达18%,溢出效应为2.2%,但与中国发达地区相比却差距较大。鉴此,提出针对陕西省发展生产性服务业的具体对策与建议。     

论合伙商事人格登记制度

我国有很多合伙没有进行商事登记就从事商事活动,一旦产生纠纷,是以商事登记的信息还是以客观事实作为法律事实,各个法院看法不一。这对善意第三人信赖利益的保护十分不利。通过研究其他国家和地区对合伙商事人格登记制度的不同规定,并结合我国的立法现状,本文认为立法者应当对合伙的商事登记的法律效力进行完善。商事登记对于个人合伙、联营型合伙和普通合伙的商事人格仅具有公示效力,对于特殊的普通合伙和有限合伙的商事人格不仅具有设权效力,也具有公示效力。     

Replication: Framing effects in intertemporal choice with children

This paper investigates the effects of framing in intertemporal choice by elementary school children. Sutter, Yilmaz, and Oberauer (2015) recently demonstrated that intertemporal choice in children is malleable with respect to simple defaults. Using a similar approach, we replicated their study. We also found that children are more willing to select the immediate option in (standard) control frames than in alternative ones that appear to promote delay of gratification. This finding is consistent with previous experiments on children as well as adults and may have implications for cultivating the ability to delay gratification in individuals.     

陕西省交通基础设施对经济增长的空间溢出效应

为了从外部性角度说明目前陕西省交通基础设施的建设和投资在地域上存在的问题,为陕西省交通基础设施建设方向提供理论帮助,基于陕西省2009~2017年间各市相关数据,在交通基础设施对经济增长效应研究的直接效应和溢出效应基础上,加入空间要素,构建生产函数和空间权重矩阵,通过自相关检验（莫兰指数）、LM检验和 Hausman检验,选取合适的空间面板计量模型对陕西省交通基础设施的经济溢出情况进行研究。研究认为,2009~2017年间陕西省交通基础设施对经济增长的溢出效应（显著）整体为负,但依然存在正溢出的局部地区,即以西安为首的经济聚集区（西安、宝鸡、咸阳）和榆林、延安联合的陕北经济聚集区,资源过于集中和两个高地之间缺乏互联互通是整体呈现负溢出的重要原因。在地域分配的量和质上应充分考虑交通基础设施的溢出效应,避免资源过度集中在西安,在两个经济高地之间打通一条经济要道,并重点扶持相对落后又有发展前景的城市可促使陕西经济发展由总体集聚向总体扩散转变。