Handling incomplete Quality-of-Life Data

Incomplete data sets are often encountered in theanalysis of quality-of-life (QOL) data. The incompleteness arisesfrom two major sources, namely, missing responses and artificialquantification of response categories. Shen and Lai (1998a)propose using Optimal Scaling (OS) to tackle the problem. The OSmethod based on numerical iterative approach attempts to restorethe continuous property of the measurements and provide estimatesfor missing responses. However, the OS leads to convergenceproblem when there are many missing values in the data set; andit incorporates no mechanisms to provide the standard errors ofthe mean estimates when missing values are filled. Hot-deckimputation is therefore suggested. This paper presents asimulation study to show that the random hot-deck imputationyields reasonable estimates for the population mean and generallypreserves the distribution of the population. In addition, whenapplying the random hot-deck imputation, valid estimates for thestandard error of the mean estimate can be obtained using thevariance formula due to Lai (1998). With hot-deck imputationtaking care of the missing responses and OS quantifying theresponse categories, it is postulated that the problem of dataincompleteness can be more satisfactorily handled. By applyingthe proposed techniques to real survey data, this paper alsopresents the change of the QOL of Hong Kong residents in the lastdecade leading to the turning point of the metropolis in 1997.     

论个人信息网络侵权责任多元归责原则之确立基础

从侵权责任归责原则的发展历史看,技术社会形态、法哲学理论和正义观念构成其确立基础。网络社会里,由于多种技术社会形态并存发展、行为主体与权利人关系多样化、侵害后果状态和归责事由多样化、价值需求和责任形式多样化发展等要求,要实现网络中个人信息权益保护与行为自由的平衡,应当确立个人信息网络侵权责任的多元归责原则：过错责任原则、过错推定责任原则与无过错责任原则。     

Spatial modelling of the two‐party preferred vote in Australian federal elections: 2001–2016

We examine the relationships between electoral socio‐demographic characteristics and two‐party preferences in the six Australian federal elections held between 2001 and 2016. Socio‐demographic information is derived from the Australian Census which occurs every 5 years. Since a census is not directly available for each election, an imputation method is employed to estimate census data for the electorates at the time of each election. This accounts for both spatial and temporal changes in electoral characteristics between censuses. To capture any spatial heterogeneity, a spatial error model is estimated for each election, which incorporates a spatially structured random effect vector. Over time, the impact of most socio‐demographic characteristics that affect electoral two‐party preference do not vary, with age distribution, industry of work, incomes, household mobility and relationships having strong effects in each of the six elections. Education and unemployment are among those that have varying effects. All data featured in this study have been contributed to the eechidna R package (available on CRAN).     

On the hidden truncated bivariate Pareto (IV) model and associated inferential issues

Income and wealth data are typically modelled by some variant of the classical Pareto distribution. Often, in practice, the observed data are truncated with respect to some unobserved covariate. In this paper, a hidden truncation formulation of this scenario is proposed and analysed. For this purpose, a bivariate Pareto (IV) distribution is assumed for the variable of interest and the unobserved covariate. Some important distributional properties of the resulting model as well as associated inferential methods are studied. An example is used finally to illustrate the results developed here. In this case, it is noted that hidden truncation on the left does not result in any new model, but the hidden truncation on the right does. The properties and fit of such a model pose a challenging problem and that is what is focused here in this work.     

Residual plots for linear regression models with censored outcome data: A refined method for visualizing residual uncertainty

Residual plots are a standard tool for assessing model fit. When some outcome data are censored, standard residual plots become less appropriate. Here, we develop a new procedure for producing residual plots for linear regression models where some or all of the outcome data are censored. We implement two approaches for incorporating parameter uncertainty. We illustrate our methodology by examining the model fit for an analysis of bacterial load data from a trial for chronic obstructive pulmonary disease. Simulated datasets show that the method can be used when the outcome data consist of a variety of types of censoring.     

基于ARFIMA模型的黄金价格预测

研究发现上海黄金交易所黄金收益序列有长期记忆性,进而从另外一个角度证实了上海黄金市场尚未达到弱式有效的结论。应用ARFIMA模型对黄金收益序列进行预测,并与用于定量预测的ARMA模型对比,结果表明分整的ARFIMA模型提高了黄金收益序列长期预测的可靠性。     

Treatment and reporting of item-level missing data in social science research

Most quantitative studies in the social sciences suffer from missing data. However, despite the large availability of documents and software to treat such data, it appears that many social scientists do not apply good practices regarding missing data. We analyzed quantitative papers published in 2017 in six top-level social science journals. Item-level missing data was found in at least 69.5% of the papers, but their presence was explicitly reported in only 44.4% of all analyzed papers. Moreover, in the majority of cases, the treatments applied to missing data were incorrect, with many uses of deletion methods that are known to produce biased results and to reduce statistical power. The impact of missing data and of their treatment on results was barely discussed. Results show that social scientists underestimate the impact of missing data on their research and that they should pay more attention to the way such data are treated.