Determination of a new mixed variable lot-size multiple dependent state sampling plan based on the process capability index

This article proposes a new mixed variable lot-size multiple dependent state sampling plan in which the attribute sampling plan can be used in the first stage and the variables multiple dependent state sampling plan based on the process capability index will be used in the second stage for the inspection of measurable quality characteristics. The proposed mixed plan is developed for both symmetric and asymmetric fraction non conforming. The optimal plan parameters can be determined by considering the satisfaction levels of the producer and the consumer simultaneously at an acceptable quality level and a limiting quality level, respectively. The performance of the proposed plan over the mixed single sampling plan based on C_pk and the mixed variable lot size plan based on C_pk with respect to the average sample number is also investigated. Tables are constructed for easy selection of plan parameters for both symmetric and asymmetric fraction non conforming and real world examples are also given for the illustration and practical implementation of the proposed mixed variable lot-size plan.     

2m − p compromise plans with strongly clear specified main effects and two-factor interactions

This article studies two-level strongly clear compromise plans. We derive some necessary conditions for the existence of four classes of two-level strongly clear compromise plans which allow the estimations of some specified main effects and two-factor interactions, with the assumption that the fourth- or higher-order effects are negligible. Also, some methods for constructing 2^{m ? p} strongly clear compromise plans are introduced.     

WLAD-LASSO method for robust estimation and variable selection in partially linear models

This paper focuses on robust estimation and variable selection for partially linear models. We combine the weighted least absolute deviation (WLAD) regression with the adaptive least absolute shrinkage and selection operator (LASSO) to achieve simultaneous robust estimation and variable selection for partially linear models. Compared with the LAD-LASSO method, the WLAD-LASSO method will resist to the heavy-tailed errors and outliers in the parametric components. In addition, we estimate the unknown smooth function by a robust local linear regression. Under some regular conditions, the theoretical properties of the proposed estimators are established. We further examine finite-sample performance of the proposed procedure by simulation studies and a real data example.     

Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data

In this paper, we consider the weighted composite quantile regression for linear model with left-truncated data. The adaptive penalized procedure for variable selection is proposed. The asymptotic normality and oracle property of the resulting estimators are also established. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.     

Likelihood estimation for longitudinal zero-inflated power series regression models

In this paper, a zero-inflated power series regression model for longitudinal count data with excess zeros is presented. We demonstrate how to calculate the likelihood for such data when it is assumed that the increment in the cumulative total follows a discrete distribution with a location parameter that depends on a linear function of explanatory variables. Simulation studies indicate that this method can provide improvements in obtaining standard errors of the estimates. We also calculate the dispersion index for this model. The influence of a small perturbation of the dispersion index of the zero-inflated model on likelihood displacement is also studied. The zero-inflated negative binomial regression model is illustrated on data regarding joint damage in psoriatic arthritis.     

On the choice of instruments in mixed frequency specification tests

Abstract

Time averaging has been the traditional approach to handle mixed sampling frequencies. However, it ignores information possibly embedded in high frequency. Mixed data sampling (MIDAS) regression models provide a concise way to utilize the additional information in high-frequency variables. In this paper, we propose a specification test to choose between time averaging and MIDAS models, based on a Durbin-Wu-Hausman test. In particular, a set of instrumental variables is proposed and theoretically validated when the frequency ratio is large. As a result, our method tends to be more powerful than existing methods, as reconfirmed through the simulations.     

Enhancing a Randomized Response Model to Estimate Population Means to Sensitive Questions

ABSTRACT

Candidate parametric mortality models are tested graphically. If the model is correct, the transformed data are distributed exponentially. The graphical test is based on scale and space inference. It involves smoothing of the hazard rate and simultaneous confidence intervals. It is applied to a frailty model used to set annuity reserves. Simulation and the comparison with other non-graphical tests shows that the graphical test helps localize discrepancies of empirical data with respect to the tested model.     

可变产能的按订单装配系统库存和生产决策研究

本文基于一个ATO 生产商的实践,考虑一个需求不确定并具有子装配件的单周期ATO 系统,为了应对较长的采购提前期和有限产能的挑战,生产商可以在得到确定的需求之前提前采购零件,装配子装配件以至于最终产品。由于产品物料清单结构中存在子装配件作为可选的中间件,因此完全使用零件和使用子装配件装配最终产品的不同,造成装配能力在时间上具有可变性。我们对此问题建立了利润最大化模型,分析在上述ATO 环境中的最优库存和生产决策。通过一系列最优解的性质,我们得到了该问题的有效解决方法。     

一种基于不确定语言变量的房地产投资风险评价方法

房地产风险评价是房地产项目管理的重要组成部分,对降低企业风险,提高企业核心竞争力具有重要作用。采用专家打分法,基于房地产项目开发阶段建立房地产投资风险评价指标体系,并根据评价指标的模糊性和不确定多准则决策理论,建立基于不确定语言变量的房地产投资风险评价方法。实证研究表明,该评价指标体系和评价方法是有效的,为房地产企业开展投资风险评估提供了一种可行的方法。     

外商在华直接投资区位选择影响因素实证分析

当前,外商直接投资在我国存在着明显的地区分布失衡问题,为此重点研究吸引外资选择投资区位的决定性因素。运用回归模型,对我国各省区2009年的截面数据进行了实证分析,结果表明:市场规模、工资水平、基础设施是吸引外商直接投资的主要因素,而劳动力素质在外商直接投资区位选择中的影响效果较小。这些因素对我国有效吸引外商直接投资具有重大启示。