A Semi‐parametric Transformation Frailty Model for Semi‐competing Risks Survival Data

In the analysis of semi‐competing risks data interest lies in estimation and inference with respect to a so‐called non‐terminal event, the observation of which is subject to a terminal event. Multi‐state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non‐terminal and terminal events specified, in part, by a unit‐specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1.0 and variance, say, σ². When the frailty distribution is misspecified, however, the resulting estimator is not guaranteed to be consistent, with the extent of asymptotic bias depending on the discrepancy between the assumed and true frailty distributions. In this paper, we propose a novel class of transformation models for semi‐competing risks analysis that permit the non‐parametric specification of the frailty distribution. To ensure identifiability, the class restricts to parametric specifications of the transformation and the error distribution; the latter are flexible, however, and cover a broad range of possible specifications. We also derive the semi‐parametric efficient score under the complete data setting and propose a non‐parametric score imputation method to handle right censoring; consistency and asymptotic normality of the resulting estimators is derived and small‐sample operating characteristics evaluated via simulation. Although the proposed semi‐parametric transformation model and non‐parametric score imputation method are motivated by the analysis of semi‐competing risks data, they are broadly applicable to any analysis of multivariate time‐to‐event outcomes in which a unit‐specific shared frailty is used to account for correlation. Finally, the proposed model and estimation procedures are applied to a study of hospital readmission among patients diagnosed with pancreatic cancer.     

An algorithm for estimating Box–Cox transformation parameter in ANOVA

In this study, we construct a feasible region, in which we maximize the likelihood function, by using Shapiro–Wilk and Bartlett's test statistics to obtain Box–Cox power transformation parameter for solving the issues of non-normality and/or heterogeneity of variances in analysis of variance (ANOVA). Simulation studies illustrate that the proposed approach is more successful in attaining normality and variance stabilization, and is at least as good as the usual maximum likelihood estimation (MLE) in estimating the transformation parameter for different conditions. Our proposed method is illustrated on two real-life datasets. Moreover, the proposed algorithm is released under R package AID under the name of “boxcoxfr” for implementation.     

A profile Godambe information of power transformations for ARCH time series

Due to Godambe (1985 Godambe, V.P. (1985). The foundation of finite sample estimation in stochastic processes. Biometrika 72:419–428.[Crossref], [Web of Science ®] , [Google Scholar]), one can obtain the Godambe optimum estimating functions (EFs) each of which is optimum (in the sense of maximizing the Godambe information) within a linear class of EFs. Quasi-likelihood scores can be viewed as special cases of the Godambe optimum EFs (see, for instance, Hwang and Basawa, 2011 Hwang, S.Y., Basawa, I.V. (2011). Godambe estimating functions and asymptotic optimal inference. Stat. Probab. Lett. 81:1121–1127.[Crossref], [Web of Science ®] , [Google Scholar]). The paper concerns conditionally heteroscedastic time series with unknown likelihood. Power transformations are introduced in innovations to construct a class of Godambe optimum EFs. A “best” power transformation for Godambe innovation is then obtained via maximizing the “profile” Godambe information. To illustrate, the KOrea Stock Prices Index is analyzed for which absolute value transformation and square transformation are recommended according to the ARCH(1) and GARCH(1,1) models, respectively.     

Alpha power Weibull distribution: Properties and applications

In this paper, a new lifetime distribution is defined and studied. We refer to the new distribution as alpha power Weibull distribution. The importance of the new distribution comes from its ability to model monotone and non monotone failure rate functions, which are quite common in reliability studies. Various properties of the proposed distribution are obtained including moments, quantiles, entropy, order statistics, mean residual life function, and stress-strength parameter. The maximum likelihood estimation method is used to estimate the parameters. Two real data sets are used to illustrate the importance of the proposed distribution.     

道路转换与村寨社会变迁——以汶川县羌锋村的民族志考察为例

阿坝州汶川县羌锋村为“西羌第一村”,途经该村寨的道路数次转变。长时段来看,羌锋村经历了灌松古道、现代道路两种不同的道路类型,其中现代道路又可分为成阿公路、国道213线、都汶高速三个不同时期出现的三种现代道路。不同阶段道路的演变均发生在特定时代背景下,继而引起了村落不同层面的社会变迁。道路的连通疏通了地区间的隔离,打开了羌锋村与外界交流的大门,扩大了羌锋村与外界沟通的范围,村寨社会发生变迁的涉及面也愈发广泛,村落重塑的效果也逐步显著。     

Exponentiated generalized Pareto distribution: Properties and applications towards extreme value theory

The GPD is a central distribution in modelling heavy tails in many applications. Applying the GPD to actual datasets however is not trivial. In this paper we propose the Exponentiated GPD (exGPD), created via log-transform of the GPD variable, which has less sample variability. Various distributional quantities of the exGPD are derived analytically. As an application we also propose a new plot based on the exGPD as an alternative to the Hill plot to identify the tail index of heavy tailed datasets, and carry out simulation studies to compare the two.     

Regression analysis of informative current status data with the semiparametric linear transformation model

Many methods have been developed in the literature for regression analysis of current status data with noninformative censoring and also some approaches have been proposed for semiparametric regression analysis of current status data with informative censoring. However, the existing approaches for the latter situation are mainly on specific models such as the proportional hazards model and the additive hazard model. Corresponding to this, in this paper, we consider a general class of semiparametric linear transformation models and develop a sieve maximum likelihood estimation approach for the inference. In the method, the copula model is employed to describe the informative censoring or relationship between the failure time of interest and the censoring time, and Bernstein polynomials are used to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established, and an extensive simulation study is conducted and indicates that the proposed approach works well for practical situations. In addition, an illustrative example is provided.     

Interdisciplinary and Interorganizational Research: Establishing the Science of Enterprise Networks

We review and discuss the evolution of interdisciplinary and interorganizational research in operations management and suggest directions for future investigations. The proposed operations management research focus is one that embraces a more holistic view of an “extended enterprise” which involves working with a new business model—the organization as a network. This methodology starts by treating the organization as a system that is enabled by information technology and is characterized by ubiquitous information sharing across traditional enterprise. Proper integration of technology, business processes and people factors needs to be developed to create higher value from networked enterprises. Operations management research future lies in establishing this science from an interdisciplinary perspective. We analyze this perspective in the context of papers published in the first 50 issues of Production and Operations Management and the related literature.     

正确理解隐喻引起的词汇变化

本文首先分析了隐喻对词汇的变化所产生的影响,包括:一词多义、词性转换、复合造词和成语,然后提出了提高隐喻认知能力的有效对策:扩大基本词汇量、掌握隐喻产生的基础以及重视语境对隐喻理解的作用。     

关于公共行政变革与行政行为理论发展的探索

随着公共行政变革的逐步深入,原有的行政行为理论已经不再适应现实发展的需要。因此,有必要突破原有思维的影响,进一步界定行政行为的概念,扩展其范围,以改造和发展现有的行政行为理论,促进行政法学和行政法治的发展,适应公共行政变革的要求。