Exploring Parameter Relations for Multi‐Stage Models in Stage‐Wise Constant and Time Dependent Hazard Rates

Single cohort stage‐frequency data are considered when assessing the stage reached by individuals through destructive sampling. For this type of data, when all hazard rates are assumed constant and equal, Laplace transform methods have been applied in the past to estimate the parameters in each stage‐duration distribution and the overall hazard rates. If hazard rates are not all equal, estimating stage‐duration parameters using Laplace transform methods becomes complex. In this paper, two new models are proposed to estimate stage‐dependent maturation parameters using Laplace transform methods where non‐trivial hazard rates apply. The first model encompasses hazard rates that are constant within each stage but vary between stages. The second model encompasses time‐dependent hazard rates within stages. Moreover, this paper introduces a method for estimating the hazard rate in each stage for the stage‐wise constant hazard rates model. This work presents methods that could be used in specific types of laboratory studies, but the main motivation is to explore the relationships between stage maturation parameters that, in future work, could be exploited in applying Bayesian approaches. The application of the methodology in each model is evaluated using simulated data in order to illustrate the structure of these models.     

关于“非遗”保护三个基本问题的话语分析

话语研究不仅关心人们如何言说,而且更为重视分析决定言说方式的深层动因。“非遗”保护既是一个社会各阶层权力角逐的场域,又是一个可通过不断协商达成共识的平台。人们对于“非遗”保护的认识人言言殊的根本原因在于参与“非遗”保护的社会各阶层各自的利益诉求并不完全一致。“非遗”保护工作正是在争论中逐渐确立了自身的工作目标、指导原则、实施方案,体现了极强的话语建构性。“非遗”作为一种传统生活方式的隐形载体,活态传承性是其精髓与灵魂;“非遗”保护必须以人为本,只有让“非遗”与现实的人民群众的日常生活发生联系和耦合,“非遗”保护才可能取得实效。     

Empirical Likelihood for the Additive Hazards Model with Current Status Data

We investigate empirical likelihood for the additive hazards model with current status data. An empirical log-likelihood ratio for a vector or subvector of regression parameters is defined and its limiting distribution is shown to be a standard chi-squared distribution. The proposed inference procedure enables us to make empirical likelihood-based inference for the regression parameters. Finite sample performance of the proposed method is assessed in simulation studies to compare with that of a normal approximation method, it shows that the empirical likelihood method provides more accurate inference than the normal approximation method. A real data example is used for illustration.     

New Ridge Regression Estimator in Semiparametric Regression Models

In the context of ridge regression, the estimation of shrinkage parameter plays an important role in analyzing data. Many efforts have been put to develop the computation of risk function in different full-parametric ridge regression approaches using eigenvalues and then bringing an efficient estimator of shrinkage parameter based on them. In this respect, the estimation of shrinkage parameter is neglected for semiparametric regression model. Not restricted, but the main focus of this approach is to develop necessary tools for computing the risk function of regression coefficient based on the eigenvalues of design matrix in semiparametric regression. For this purpose the differencing methodology is applied. We also propose a new estimator for shrinkage parameter which is of harmonic type mean of ridge estimators. It is shown that this estimator performs better than all the existing ones for the regression coefficient. For our proposal, a Monte Carlo simulation study and a real dataset analysis related to housing attributes are conducted to illustrate the efficiency of shrinkage estimators based on the minimum risk and mean squared error criteria.     

In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

On the Joint Estimation of Heterogeneous Technologies,Technical, and Allocative Inefficiency

In this article, we provide a semiparametric approach to the joint measurement of technical and allocative inefficiency in a way that the internal consistency of the specification of allocative errors in the objective function (e.g., cost function) and the derivative equations (e.g., share or input demand functions) is assured. We start from the Cobb–Douglas production and shadow cost system. We show that the shadow cost system has a closed-form likelihood function contrary to what was previously thought. In turn, we use the method of local maximum likelihood applied to a system of equations to obtain firm-specific parameter estimates (which reveal heterogeneity in production) as well as measures of technical and allocative inefficiency and its cost. We illustrate its practical application using data on U.S. electric utilities.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.     

1980年代“译介热”与中国审美话语的本体自律

20世纪80年代的“译介热”通过引进西方现代文论资源,实现了对政治意识形态“工具论”“反映论”的反拨与对抗,与此同构的是审美自律的进程和审美本体的建构.“审美自律论”和“方法多元论”成为“译介热”的两个主要支点,共同参与现代性人学启蒙,促使了现代派文学的文本发现与形式自律.“审美自律论”促使了审美活动从外部的“他律”转向了审美的“自律”,形式主义文论、结构主义文论等在译介热中引入中国,纷纷促使了美学和文学批评从外部转向内部、从内容转向形式、从思想内涵转向话语革命.同时也直接启发了中国现代派文学和先锋小说的创作,激活了新时期以来潜藏的“形式化”冲动,从而使得形式和语言实验在80年代中后期的文学创作中焕发出愈加重要的作用.     

口述史的复调价值及其实现

布罗代尔认为,理想的历史应该是多声部的。现代意义上的口述史的出现使基于文献书写的历史的单声部特征显露无遗。口述史不再是现代社会“沉默的大多数”发出的声音。以口头陈述为基础的口述史的叙事和以文献为基础的历史的书写都同样是有关历史事实的观念的反映。各具独立旋律的叙事与书写理应建立起和谐的关系而形成复调历史。这种复调历史并非杜赞奇基于话语分析方法,以解构民族国家单线叙事为目的的复线历史。口述史与历史二者之间互补互益的差异使反思性知识与批判性知识可以对过去的历史进行检讨并对历史事实进行更为深刻的理解。借鉴历史研究方法及滥觞于研究无文字民族的民族志方法是解决口述史实践中存在的诸多问题的重要路径。     

上市公司存货计量方法选择与决策相关性分析

通过分析新存货准则中引入的存货计量属性,即历史成本计量、公允价值计量、可变现净值和未来现金流量的现值的本质特征,分析了其与决策的相关性,并通过对我国部分上市公司存货计价方法选择的深层分析,揭示了决策相关性在存货计价方法应用中所传递的信息和表达的目的。