Bootstrap order selection for SETAR models

In this paper, we investigate the selecting performances of a bootstrapped version of the Akaike information criterion for nonlinear self-exciting threshold autoregressive-type data generating processes. Empirical results will be obtained via Monte Carlo simulations. The quality of our method is assessed by comparison with its non-bootstrap counterpart and through a novel procedure based on artificial neural networks.     

Ranking and estimation of related means in two—way models—a Bayesian approach

A hierarchical Bayesian approach to ranking and selection as well as estimation of related means in two—way models is considered. Using the method of Monte Carlo simulation with importance sampling, we are able to carry out efficiently the three or four dimensional integrations as needed. An example is included to illustrate the methodology.     

Is prenatal sex selection associated with lower female child mortality?

I examine whether prenatal sex selection has substituted postnatal excess female mortality by analysing the dynamics of child sex ratios between 1980 and 2015 using country-level life table data. I decompose changes in child sex ratios into a ‘fertility’ component attributable to prenatal sex selection and a ‘mortality’ component attributable to sex differentials in postnatal survival. Although reductions in numbers of excess female deaths have accompanied increases in missing female births in all countries experiencing the emergence of prenatal sex selection, relative excess female mortality has persisted in some countries but not others. In South Korea, Armenia, and Azerbaijan, mortality reductions favouring girls accompanied increases in prenatal sex selection. In India, excess female mortality was much higher and largely stable as prenatal sex selection emerged, but slight reductions were seen in the 2000s. In China, although absolute measures showed reductions, relative excess female mortality persisted as prenatal sex selection increased.     

Treatment Evaluation in the Presence of Sample Selection

Sample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem into (i) a selective subpopulation (e.g., working—selection on unobservables) and (ii) a binary treatment (e.g., training—selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education.     

Composite quantile regression for massive datasets

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Composite quantile regression (CQR) is a robust and efficient estimation method. In this paper, we extend CQR to massive datasets and propose a divide-and-conquer CQR method. The basic idea is to split the entire dataset into several blocks, applying the CQR method for data in each block, and finally combining these regression results via weighted average. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analysed simultaneously. Moreover, to improve the efficiency of CQR, we propose a weighted CQR estimation approach. To achieve sparsity with high-dimensional covariates, we develop a variable selection procedure to select significant parametric components and prove the method possessing the oracle property. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.     

Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

逆向选择条件下带奖惩金的两期保险契约模型及其Pareto改进性研究

为了更有效的规避影响保险市场交易效率的逆向选择问题，本文分投保人风险类型为两种和多种情形建立了带奖惩金的两期保险契约模型，首次提出可以用奖励金和惩罚金有效甄别投保人的风险类型。该模型根据投保人第一个保险期内的索赔情况在第二个保险期对其进行奖励或惩罚，高风险类型的投保人如果选择为低风险类型投保人设计的保险契约，则其在第二阶段受到惩罚的概率要远远大于得到奖励的概率，即风险越高的投保人越害怕惩罚金，因此所建模型满足斯彭斯-莫里斯分离条件。带奖惩金的两期保险契约模型中保险公司的期望利润仍然为0，并不会给投保人带来额外的经济负担，却能够实现对传统部分保险契约简单重复两次的严格帕累托改进。最后采用一个算例说明了该模型的有效性。     

Production systems and supplier selection: a multi-phase process model

Abstract

Purpose: This paper presents a combined multi-phase supplier selection model. The process repeatedly revisits the criteria and sourcing decision as the development process continues. This enables a structured adoption of product and production system innovation from strategic suppliers, where previously the literature purely focuses on product innovation or cost reduction. Design/methodology/approach: The authors adopted an embedded researcher style, inductive, qualitative case study of an industrial supply cluster comprising a focal automotive company and its interaction with three different strategic stamping suppliers. Findings: Our contribution is the multi-phased production and product innovation process. This is an advance from traditional supplier selection and also an extension of ideas of supplier-located product development as it includes production system development, and complements the literature on working with strategic suppliers. Specifically, we explicitly articulate the previously unreported issue of whether a supplier chosen for its innovation capabilities at the start of the new product development process will also be the most appropriate supplier during the production system development phase, when an ability to work collaboratively may be the most important attribute, or in the large-scale production phase when an ability to manufacture at low unit cost may be most important. Originality/value: The paper identifies a multi-phase approach to tendering within a fixed body of strategic suppliers which seeks to identify the optimum technological and process decisions as well as the traditional supplier sourcing choice. These areas have not been combined before and generate a valuable approach for firms to adopt as well as for researchers to extend our understanding of a highly complex process.