30年改革开放：天津商业发展管窥

改革开放30年来,我市商业系统通过体制的变迁和改革,促使市场多元主体形成,推动了流通规模持续扩大、现代化程度日益提高和商业功能的有效拓展。天津商业要在实现科学发展、和谐发展、率先发展中有所作为有所贡献,必须以取得的成就基础上进一步深化改革,扩大开放,统筹协调天津商业发展。     

对方案有偏好的区间数多属性灰色关联决策模型

针对属性值以区间数形式给出并且已知方案偏好信息的多属性决策问题,提出了一种灰色关联分析的决策方法。该方法依据一般的灰色关联分析方法的基本思路,给出了该问题的计算步骤,其核心是通过构建并求解一个单目标最优化模型,得到属性权重信息,从而计算出每个方案客观偏好值与主观偏好值的灰色关联系数,进而得到每个方案客观偏好与主观偏好的关联度,根据关联度对所有方案进行排序。最后给出了一个数值例子,结果表明方法简单,有效和易于计算。     

Market‐Based Allocation with Indivisible Bids

We study multiunit double auctions accepting bids with indivisibility constraints. Modeling the auction problem as a Multiple Choice Knapsack Problem and using dynamic programming, we show that incremental computations during bid processing can speed the handling of key auction operations such as clearing and quoting. We propose different price‐quote policies and study their influence on the efficiency of market‐based allocation. Using a reconfigurable manufacturing scenario where agents trade large quantities of multiple goods, we demonstrate potential benefits of supporting indivisibility constraints in bidding. These benefits are highly sensitive to the form of price quote provided, indicating interesting tradeoffs in communication and allocation efficiency.     

Sequential imputation for models with latent variables assuming latent ignorability

Models that involve an outcome variable, covariates, and latent variables are frequently the target for estimation and inference. The presence of missing covariate or outcome data presents a challenge, particularly when missingness depends on the latent variables. This missingness mechanism is called latent ignorable or latent missing at random and is a generalisation of missing at random. Several authors have previously proposed approaches for handling latent ignorable missingness, but these methods rely on prior specification of the joint distribution for the complete data. In practice, specifying the joint distribution can be difficult and/or restrictive. We develop a novel sequential imputation procedure for imputing covariate and outcome data for models with latent variables under latent ignorable missingness. The proposed method does not require a joint model; rather, we use results under a joint model to inform imputation with less restrictive modelling assumptions. We discuss identifiability and convergence‐related issues, and simulation results are presented in several modelling settings. The method is motivated and illustrated by a study of head and neck cancer recurrence. Imputing missing data for models with latent variables under latent‐dependent missingness without specifying a full joint model.     

Class and status: on the misconstrual of the conceptual distinction and a neo‐Bourdieusian alternative

In this article, we address the classical debate about the relationship between the economic and cultural aspects of social stratification, typically cast in terms of Weber’s distinction between class and status. We discuss in particular Chan and Goldthorpe’s influential, yet largely unchallenged, attempt to reinstate a strict version of the class‐status distinction, mounted as an attack on ‘Bourdieusian’ accounts. We argue that this is unconvincing in two respects: There are fundamental problems with their conceptualization of status, producing a peculiar account where one expression of status honour explains the other; in addition, their portrayal of the Bourdieusian approach as one‐dimensional is highly questionable. In contradiction of a reading of Bourdieu as discarding the class‐status distinction, we develop an alternative, neo‐Bourdieusian account that recognizes class and status as distinct aspects of stratification, thereby allowing for a subtle analysis of their empirical entwinement. The fruitfulness of this approach is demonstrated by analysing the homology between the space of lifestyles and the social space through Multiple Correspondence Analysis of unusually rich data about lifestyles. Importantly, we highlight the relative autonomy of these spaces: Although they exhibit a similar structure, they do not overlap completely.     

Diverse students and literacy in social work education: pedagogies for a new tertiary landscape

In the Western policy landscape, students from diverse cultural, linguistic, economic and other backgrounds are increasingly represented in an expanded tertiary education system. Structural and technological changes have challenged traditional pedagogies designed for more elite cohorts, and shifted the boundaries of textual practices in both universities and professional worlds. In spite of these momentous trends, university literacies often remain tacit and inaccessible to those new to them, and pedagogies rarely explicitly engage with the complexities of these shifting boundaries. University students unfamiliar with these tacit literacies can become marginalised and ultimately struggle with the literacy demands of their academic and professional worlds. This is problematic in social work education, as professional practice demands complex written and oral communication, with potentially significant social and legal repercussions. This paper reports on pedagogies initiated by action research in a Bachelor of Social Work programme in an Australian University. Pedagogies were introduced to make elite codes explicit, use students’ literacies as assets for learning and encourage ‘code switching’ between literacy practices to resist colonising into more powerful literacies. Students’ capacities in academic and professional literacies were significantly enhanced by these practices, and their cultural inheritances valorised, encouraging their contribution to the social work discipline.     

Evaluation of multiple-imputation procedures for three-mode component models

Three-mode analysis is a generalization of principal component analysis to three-mode data. While two-mode data consist of cases that are measured on several variables, three-mode data consist of cases that are measured on several variables at several occasions. As any other statistical technique, the results of three-mode analysis may be influenced by missing data. Three-mode software packages generally use the expectation–maximization (EM) algorithm for dealing with missing data. However, there are situations in which the EM algorithm is expected to break down. Alternatively, multiple imputation may be used for dealing with missing data. In this study we investigated the influence of eight different multiple-imputation methods on the results of three-mode analysis, more specifically, a Tucker2 analysis, and compared the results with those of the EM algorithm. Results of the simulations show that multilevel imputation with the mode with the most levels nested within cases and the mode with the least levels represented as variables gives the best results for a Tucker2 analysis. Thus, this may be a good alternative for the EM algorithm in handling missing data in a Tucker2 analysis.     

Impact of the non-distinctness and non-ignorability on the inference by multiple imputation in multivariate multilevel data: a simulation assessment

Multiple imputation (MI) is an increasingly popular method for analysing incomplete multivariate data sets. One of the most crucial assumptions of this method relates to mechanism leading to missing data. Distinctness is typically assumed, which indicates a complete independence of mechanisms underlying missingness and data generation. In addition, missing at random or missing completely at random is assumed, which explicitly states under which conditions missingness is independent of observed data. Despite common use of MI under these assumptions, plausibility and sensitivity to these fundamental assumptions have not been well-investigated. In this work, we investigate the impact of non-distinctness and non-ignorability. In particular, non-ignorability is due to unobservable cluster-specific effects (e.g. random-effects). Through a comprehensive simulation study, we show that MI inferences suggest that nonignoriability due to non-distinctness do not immediately imply dismal performance while non-ignorability due to missing not at random leads to quite subpar performance.     

Stopping rules for long-term clinical trials based on two consecutive rejections of the null hypothesis

A strategy for stopping long-term randomized clinical trials with time-to-event as a primary outcome measure has been considered using the criteria requiring multiple consecutive (or non consecutive) rejections at a specified α-level that controls against elevation of type I error. The procedure using two consecutive rejections is presented in this work along with the corresponding α-levels for the interim tests. The boundary cutoff values for these interim levels were determined based on an overall prespecified test size and were calculated using multidimensional integration and/or simulations. The reduction in the interim α-level values that is required to maintain the experiment-wise error rate is found to be modest. The power of the test is evaluated under various alternative accrual and hazard patterns. This procedure provides a more realistic stopping rule in large multi-center trials where it may be undesirable to terminate a trial unless a sustained effect has been demonstrated.     

Multiple non linear regression model for the maximum number of migratory bird types during migration years

Since many years scientists have carried out researches about bird migration, but due to the large number of bird types estimated at around 10,000 in the world, it is not easy to predict the maximum number of migratory bird types through limited number of migration years. In this study, a multiple non linear regression model of maximum number probability function of migratory bird types has been obtained and can be used in the prediction of the maximum number of migratory bird types during any migration years.