Work-related drinking and processes of social integration and marginalization in two Norwegian workplaces

The aim of this study was to investigate the role of drinking in two Norwegian workplaces, focusing on how the drinking may contribute to both social integration and marginalization of employees. The two workplaces were a division of a multinational oil company and a public library both located in one of Norway's larger cities. Data were collected through ethnographic fieldwork, including using participant observation, qualitative interviews, and brief surveys. Work-related drinking was found to be common in both workplaces and played a significant role in a wide range of social situations in the twilight zone between normal working hours and leisure. Drinking was regulated by dominant workplace alcohol cultural ideals, but also involved opposition to these ideals. The results of this study have demonstrated that different forms of drinking (and non-drinking) may lead to both social integration and marginalization – or even exclusion of groups of employees or individual employees.     

菲利普斯曲线在中国的有效性研究

从基本的菲利普斯曲线理论出发,提出了新的适应性预期模型,并以此为基础建立附加预期的菲利普斯曲线方程,进而估计出我国近年来的自然失业率水平,由此证明了菲利普斯曲线在我国的有效性,并描述了预期因素对实际通货膨胀水平的影响。最后结合实证分析结果提出解决我国通货膨胀和失业问题的建议,如保证货币政策的连贯性、加强信息披露、解决结构性失业问题等。     

Multivariate Return Decomposition: Theory and Implications

In this paper, we propose a model based on multivariate decomposition of multiplicative – absolute values and signs – components of asset returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting bond returns illustrates the usefulness of the proposed 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.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.     

The myth of price convergence under economic integration: A proposed explanation for the difference in food prices across European countries

Conventional globalization theory states that regional economic integration will precede price standardization across participating countries due to the increased buying power of large retail groups and parallel imports. The resulting price corridor should merely reflect differences in logistic costs and short-term, local competitive actions. Yet, this study uses panel data from 25 European countries to examine how hefty food and beverage price differentials between regions remained constant over the last decade. Income, store productivity, and market concentration all contribute to the explanation for regional price differences. These findings suggest that, contrary to straight-line globalization theory, large European retailers’ decisions can influence price convergence and maintain important price dispersion between economically integrated countries. We provide recommendations to enhance market integration.     

Discriminant coordinates analysis for multivariate functional data

Abstract

One of the basic statistical methods of dimensionality reduction is analysis of discriminant coordinates given by Fisher (1936 Fisher, R. A. 1936. The use of multiple measurements in taxonomic problem. Annals of Eugenics 7 (2):179–88. doi:10.1111/j.1469-1809.1936.tb02137.x.[Crossref] , [Google Scholar]) and Rao (1948). The space of discriminant coordinates is a space convenient for presenting multidimensional data originating from multiple groups and for the use of various classification methods (methods of discriminant analysis). In the present paper, we adapt the classical discriminant coordinates analysis to multivariate functional data. The theory has been applied to analysis of textural properties of apples of six varieties, measured over a period of 180?days, stored in two types of refrigeration chamber.     

从教师专业实践能力发展反观高校与幼儿园的双向融合

当前，打通高校教师理论研究与基层一线教师专业发展瓶颈是总体提升我国教育质量的高效之举，教育的实践性本质也决定了高校教师的理论研究与一线教师的教育实践必须坚持双向融合的道路。实现校园双向融合既要积极构建教师发展共同体，还要积极探索有深度和广度的教研机制，并搭建开放、多元、创新的资源平台。     

Numerical evaluation of tests based on different heteroskedasticity-consistent covariance matrix estimators

This article considers the issue of performing tests in linear heteroskedastic models when the test statistic employs a consistent variance estimator. Several different estimators are considered, namely: HC0, HC1, HC2, HC3, and their bias-adjusted versions. The numerical evaluation is performed using numerical integration methods; the Imhof algorithm is used to that end. The results show that bias-adjustment of variance estimators used to construct test statistics delivers more reliable tests when they are performed for the HC0 and HC1 estimators, but the same does not hold for the HC3 estimator. Overall, the most reliable test is the HC3-based one.