The declining influence of family background on educational attainment in Australia: The role of measured and unmeasured influences

The paper examines changes in the influence of family background, including socioeconomic and social background variables on educational attainment in Australia for cohorts born between 1890 and 1982. We test hypotheses from modernization theory on sibling data using random effects models and find: (i) substantial declines in the influence of family background on educational attainment (indicated by the sibling intraclass correlations); (ii) declines in the effects of both economic and cultural socioeconomic background variables; (iii) changes in the effects of some social background variables (e.g., family size); (iv) and declines in the extent that socioeconomic and social background factors account for variation in educational attainment. Unmeasured family background factors are more important, and proportionally increasingly so, for educational attainment than the measured socioeconomic and social background factors analyzed. Fixed effects models showed steeper declines in the effects of socioeconomic background variables than in standard analyses suggesting that unmeasured family factors associated with socioeconomic background obscure the full extent of the decline.     

Shippers and freight operators perceptions of sustainable initiatives

The purpose of this paper is to document the extent to which Brazilian companies in retail channels have committed to adopting environmental sustainability measures, goals and strategies for waste disposal in the evaluation and planning of sustainable transportation. This paper also aims to examine the different viewpoints of sustainability issues and the preventive actions taken by companies in terms of controlling carbon dioxide emissions and proper disposal of tires, lubricant oils, accessories and spare parts. Finally, taking the perspective of these companies, this paper examines their difficulties in meeting environmental regulations. The research involved a survey completed by 185 representatives of different types of companies, including shippers (represented by manufacturers), LSPs (logistics service providers) and carriers. The non-linear canonical correlation was calculated to verify the opinions of these representatives from different companies regarding issues that impact on the environment, the preventive actions they adopt to reduce their environmental impact and their difficulties in meeting environmental regulations. The results show that shippers and LSPs have the same perceptions regarding these sustainability issues and preventive actions. In addition, the companies perceive high costs and the lack of training for their partners as the major challenges experienced in addressing these issues and undertaking preventive actions. Therefore they need to plan their transportation activities.     

Unconditional analogues of Cochran–Mantel–Haenszel tests

The Cochran–Mantel–Haenszel tests are a suite of tests that are usually defined as conditional tests, tests that assume all marginal totals are known before sighting the data. Here unconditional analogues of these tests are defined for the more usual situation when the marginal totals are not known before sighting the data.     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

机器中的幽灵 ——解读文本内外的《宣叙》

莫里森的批评论著《在黑暗中演奏：白色与文学想象》是作家对美国经典文学长期审视和反思的结果。通过分析白人经典作品中的非裔在场,我们可以揭示出“美国性”的本质和美国“文学白色”神话的炮制过程。如果将短篇小说《宣叙》与《演奏》并置,我们可以看到故事中的麦基其实是对后者所评述的美国经典文学中非裔在场的影射。麦基这个人物的再现是反射性的,与美国经典文学对非裔人物的再现如出一辙。二者的不同在于,故事揭示并最终反拨了人物再现的内在机制。《宣叙》对美国经典文学的观照,实质上是以隐晦的手法介入美国文学的经典辩论,传达了作者文化修正的愿望。     

ADE-4: a multivariate analysis and graphical display software

We present ADE-4, a multivariate analysis and graphical display software. Multivariate analysis methods available in ADE-4 include usual one-table methods like principal component analysis and correspondence analysis, spatial data analysis methods (using a total variance decomposition into local and global components, analogous to Moran and Geary indices), discriminant analysis and within/between groups analyses, many linear regression methods including lowess and polynomial regression, multiple and PLS (partial least squares) regression and orthogonal regression (principal component regression), projection methods like principal component analysis on instrumental variables, canonical correspondence analysis and many other variants, coinertia analysis and the RLQ method, and several three-way table (k-table) analysis methods. Graphical display techniques include an automatic collection of elementary graphics corresponding to groups of rows or to columns in the data table, thus providing a very efficient way for automatic k-table graphics and geographical mapping options. A dynamic graphic module allows interactive operations like searching, zooming, selection of points, and display of data values on factor maps. The user interface is simple and homogeneous among all the programs; this contributes to making the use of ADE-4 very easy for non- specialists in statistics, data analysis or computer science.     

Elliptically contoured model and factorization of wllks1 A

In a series of papers, Kshirsagar (1964, 1971) and McHenry and Kshirsagar (1977), factorize Wilks' A into a number of factors and find the independent null multivariate beta densities of these factors. These factors are the likelihood ratio test criteria for testing the goodness of fit of certain assigned discriminant functions or canonical variables either in the space of independent or dependent variables. Essentially the factors of Wilks' A are the factors of certain multivariate beta distributed matrix or its determinant. The Bartlett decomposition of the underlying multivariate beta distribution into independent factors determines the distribution of these factors. The present paper generalizes Kshirsagar's (1971) normal theory to the elliptically contoured model, and shows that his results are null robust for the elliptically contoured model.     

Inference in Targeted Group‐Sequential Covariate‐Adjusted Randomized Clinical Trials

This article is devoted to the construction and asymptotic study of adaptive, group‐sequential, covariate‐adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group‐sequentially, a sampling design that targets a user‐supplied optimal covariate‐adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group‐sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis‐specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.     

Bayesian variable selection with spherically symmetric priors

We propose that Bayesian variable selection for linear parametrizations with Gaussian iid likelihoods should be based on the spherical symmetry of the diagonalized parameter space. Our r-prior results in closed forms for the evidence for four examples, including the hyper-g prior and the Zellner–Siow prior, which are shown to be special cases. Scenarios of a single-variable dispersion parameter and of fixed dispersion are studied, and asymptotic forms comparable to the traditional information criteria are derived. A simulation exercise shows that model comparison based on our r-prior gives good results comparable to or better than current model comparison schemes.     

A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations

We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data and the model conditional covariance matrix is ensured to be positive definite. We study the empirical performance of our model in two applications using U.S. stock and bond market data. In both applications our model has, in terms of statistical and economic significance, higher forecasting power than several other multivariate GARCH models for conditional correlations.