椭圆轮廓的评定方法及其计算机模拟

给出了一个正确的数据处理方法,对已有的机械零件的椭圆轮廓的几何参数和线轮廓度误差评定理论的不足作了修正,并用计算机模拟方法对其进行了验证。     

Monitoring the parameter vector of regression models with time-to-event response in phase II processes

In most of the existing specialized literature, monitoring regression models are a special case of profile monitoring. However, not every regression model always represents appropriately a profile data structure. This is clearly the case of the Weibull regression model (WRM) with common shape parameter γ. Even though it might be thought that existing methodologies (especially likelihood-ratio (LRT)-based methods) for monitoring generalized linear profiles can also be successfully applied to monitoring regression models with time-to-event response, it will be shown in this paper that those methodologies work fairly acceptable just for data structures with 1000 observations at least approximately. It was found out that some corrections, often referred to as Bartlett's adjustments, are needed to be implemented in order to improve the accuracy of using the asymptotic distributional properties of the LRT statistic for carrying out the monitoring of WRM with relatively small and moderate dimensions of the available datasets. Simulation studies suggest that the use of the aforementioned corrections make the resulting charts work quite acceptable when available data structures contain 30 observations at least. Detection abilities of the proposed schemes improve as dataset dimension increases.     

Local estimation for varying-coefficient models with longitudinal data

Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient models for longitudinal data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality for the proposed estimators of varying-coefficient functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.     

Exploring patterns of personal growth initiative and posttraumatic stress: A latent profile analysis

Objective: This study examined the inconsistent relationship found between personal growth initiative (PGI) and posttraumatic stress symptoms (PTSS) by exploring potential subgroups. In addition, after identifying the subgroups, potential predictors of these subgroups were examined. Participants: Participants were 534 undergraduate students who experienced a potentially traumatic event (PTE). Data collection occurred from March 2013 to December 2014. Methods: Because this was a preliminary study, archival data was used. Participants consisted of students who experienced a PTE within the past three years. Results: Latent profile analysis indicated a five-class model as the best-fitting model. Direct exposure and intentionality of the event were significant predictors of class membership; length of time since event and gender did not predict class membership. Conclusions: The current study provides a potential explanation of the inconsistent results between PGI and PTSS and also highlights the importance of acknowledging subgroups when utilizing PGI after a PTE.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

中美快消品行业企业简介体裁对比分析

文章根据专门用途英语学派的体裁分析理论,构建了商务体裁研究理论框架,对30篇中美快消品行业企业简介进行了体裁对比分析。结果表明,中美快消品行业企业简介语步结构基本一致,但二者在语步和步骤的选择频率、介绍性语步和劝说性语步的实现形式上存在差异。      

The Experience-Earnings Profile: Productivity-Augmenting or Purely Contractual? Evidence from the UK

We test the human capital interpretation of the experience-earnings profile. Does the upward sloping portion of the experience-earnings profile reflect on-the-job training which in turn causes the experience-productivity profile to slope upwards, or do purely contractual factors determine the nature of life-cycle earnings. Herein, we provide additional evidence on the relationship between productivity and earnings by examining earnings differentials in the UK academic labor market for economists. Using a test first suggested by Mincer, we find that the empirical results are consistent with human capital theory. We find that, although the positive relationship between earnings and experience persists when individual productivity measures are included in the salary equations for lecturers and senior lecturers, the positive relationship becomes statistically insignificant when the same productivity measures are included in the salary equations for professors. For lecturers and senior lecturers, the experience-salary profile properly reflects the structure of the national pay scale rather than variations in individual research productivity. At the professor level, where individual salaries are not determined by a pay scale, the data support the human capital explanation of the positive experience-earnings profile.

Construction of Confidence Intervals for the IVIean of a Population Containing Many Zero Values

The likelihood ratio method is used to construct a confidence interval for a population mean when sampling from a population with certain characteristics found in many applications, such as auditing. Specifically, a sample taken from this type of population usually consists of a very large number of zero values, plus a small number of nonzero values that follow some continuous distribution. In this situation, the traditional confidence interval constructed for the population mean is known to be unreliable. This article derives confidence intervals based on the likelihood-ratio-test approach by assuming (1) a normal distribution (normal algorithm) and (2) an exponential distribution (exponential algorithm). Because the error population distribution is usually unknown, it is important to study the robustness of the proposed procedures. We perform an extensive simulation study to compare the percentage of confidence intervals containing the true population mean using the two proposed algorithms with the percentage obtained from the traditional method based on the central limit theorem. It is shown that the normal algorithm is the most robust procedure against many different distributional error assumptions.     

Comparing Two Population Means and Variances: A Parametric Robust Way

Abstract

This article introduces a parametric robust way of comparing two population means and two population variances. With large samples the comparison of two means, under model misspecification, is lesser a problem, for, the validity of inference is protected by the central limit theorem. However, the assumption of normality is generally required, so that the inference for the ratio of two variances can be carried out by the familiar F statistic. A parametric robust approach that is insensitive to the distributional assumption will be proposed here. More specifically, it will be demonstrated that the normal likelihood function can be adjusted for asymptotically valid inferences for all underlying distributions with finite fourth moments. The normal likelihood function, on the other hand, is itself robust for the comparison of two means so that no adjustment is needed.     

Towards a job competency profile for agricultural extension instructors: A survey of views of experts

A project is carried out for instructors in the agricultural extension service in Esfahan. Part of the project is study, conducted to inform the process of competency profile development for the instructors. The study, reported in this contribution, focuses on views of experts about competencies needed by the instructors. The survey is based on the methodology for developing models of HRD, in which 257 key experts participated. A mix of methods was used for collecting data. The most important competencies found in this study are subject matter and business understanding, and presentation skill. The most essential outputs are presentation of instructional materials, feedback to learners and equipping learners with new competencies after the course. In this contribution, competencies and outputs are elaborated. Further research is needed regarding performance improvement as a result of the efforts of the instructors, and the views of other stakeholders on the development of the agri-clusters.