Understanding the relationship between social change and its impacts: The experience of rural land use change in south-eastern Australia

This study investigated socio-economic impacts of land use change, giving explicit attention to the relationships between independently observed land use change and associated socio-economic changes, perceived land use change and socio-economic change, attributed cause of change, and experienced impacts of change. Using a case study region in south-east Australia, we examined the impacts of growth in use of land for dairy farming, cropping, blue gum plantations and rural residential development on (i) rural population trends, and (ii) the amount and nature of employment available in the study region. Perceptions and impacts of change were assessed using multiple qualitative and quantitative methods. Results demonstrate that local residents were not always aware of the extent and nature of land use change, and had difficulty attributing social changes and their impacts to the land use changes that underlie them. Furthermore, the felt impacts of land use change appeared dependent on a person's awareness of that change, and on their beliefs about the causes of social change. These findings highlight avenues for theoretical development to better specify the processes by which social change processes are experienced as human impacts. The findings also have implications for land use policy and social impact assessment, illustrating the importance of understanding both perceived and actual social change.     

中国城市“居住分异”模式的问题与对策

当前,我国城市化进程逐渐加快,导致城市发展出现一系列城市病问题,城市居住区呈现出越来越明显的"居住分异"现象,整个城市形成一种居住空间分化甚至相互隔离的状况,给社会健康发展带来一系列负面影响。混合居住是目前解决居住分异的一种重要途径。混合居住模式以消除居民的社会差异为住宅建设的社会意义和目标,是解决不同社会阶层隔离、促进不同居民交往的有效方法,有利于营造和谐的居住模式。     

Evolution of product quality in European,Japanese and US automotive firms: An exploratory longitudinal analysis

For years we have been hearing that US automobile manufacturers have been losing market share to their Japanese rivals who are reputed to make better quality vehicles. Most such reports are based on the initial quality surveys on new automobiles. In this paper we address two exploratory questions: (1) how does the quality of an automobile change with its age, and, (2) can firm level variables help explain differences quality. To answer these questions, we collected Consumer Reports’ reliability ratings on approximately 300 automobile models made by European, Japanese and US automotive firms during the 1998–2007; and approximately 240 models made by these firms over period of 2008–2015. For both periods we found that not only do automobiles made by Japanese firms have higher initial quality, but, as automobiles get older the difference in the product quality between Japanese versus European and US firms increases. We also found that the more generalist a European or US automobile firm, i.e., the wider is the firm׳s product offering in the marketplace, the lower its overall automobile quality during the 1998–2007 period. Conversely, Japanese generalist firms were found to have higher quality than specialist firms over the same period. The result is partly explained by the fact that Japanese firms have taken a different path to broadening their product variety – they have ensured a high level of quality of their initial offerings before entering newer market segments. The rate of reliability decline was found to be slower for all firms, and the differences in reliability across the 3 groups of firms were much less pronounced during the 2008–2015 period. This improvement may be as result of restructuring done by US automobile firms.     

On generalised estimating equations for vector regression

Generalised estimating equations (GEE) for regression problems with vector‐valued responses are examined. When the response vectors are of mixed type (e.g. continuous–binary response pairs), the GEE approach is a semiparametric alternative to full‐likelihood copula methods, and is closely related to Prentice & Zhao's mean‐covariance estimation equations approach. When the response vectors are of the same type (e.g. measurements on left and right eyes), the GEE approach can be viewed as a ‘plug‐in’ to existing methods, such as the vglm function from the state‐of‐the‐art VGAM package in R. In either scenario, the GEE approach offers asymptotically correct inferences on model parameters regardless of whether the working variance–covariance model is correctly or incorrectly specified. The finite‐sample performance of the method is assessed using simulation studies based on a burn injury dataset and a sorbinil eye trial dataset. The method is applied to data analysis examples using the same two datasets, as well as to a trivariate binary dataset on three plant species in the Hunua ranges of Auckland.     

Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

Regularization through variable selection and conditional MLE with application to classification in high dimensions

It is often the case that high-dimensional data consist of only a few informative components. Standard statistical modeling and estimation in such a situation is prone to inaccuracies due to overfitting, unless regularization methods are practiced. In the context of classification, we propose a class of regularization methods through shrinkage estimators. The shrinkage is based on variable selection coupled with conditional maximum likelihood. Using Stein's unbiased estimator of the risk, we derive an estimator for the optimal shrinkage method within a certain class. A comparison of the optimal shrinkage methods in a classification context, with the optimal shrinkage method when estimating a mean vector under a squared loss, is given. The latter problem is extensively studied, but it seems that the results of those studies are not completely relevant for classification. We demonstrate and examine our method on simulated data and compare it to feature annealed independence rule and Fisher's rule.     

Another look at Huber's estimator: A new minimax estimator in regression with stochastically bounded noise

Huber's estimator has had a long lasting impact, particularly on robust statistics. It is well known that under certain conditions, Huber's estimator is asymptotically minimax. A moderate generalization in rederiving Huber's estimator shows that Huber's estimator is not the only choice. We develop an alternative asymptotic minimax estimator and name it regression with stochastically bounded noise (RSBN). Simulations demonstrate that RSBN is slightly better in performance, although it is unclear how to justify such an improvement theoretically. We propose two numerical solutions: an iterative numerical solution, which is extremely easy to implement and is based on the proximal point method; and a solution by applying state-of-the-art nonlinear optimization software packages, e.g., SNOPT. Contribution: the generalization of the variational approach is interesting and should be useful in deriving other asymptotic minimax estimators in other problems.     

基于多变量动态模型的产出缺口估算

 本文运用多变量动态模型系统下的Beveridge-Nelson分解方法和贝叶斯Gibbs抽样估计,估算了1985年1季度至2008年2季度期间中国的产出缺口,并且与传统的单变量估计方法测算的结果在统计属性和对货币政策调节的预测效果方面进行了比较。实证结果表明,不同产出缺口的统计属性存在差别,并且只有基于多变量系统测算的产出缺口对货币政策具有显著预测效果。这说明多变量模型估计出的产出缺口更全面地考虑了经济产出与其他相关变量的互动效应,含有的信息更为丰富,从而对宏观政策调整具有更重要的参考价值。     

Modified inference about the mean of the exponential distribution using moving extreme ranked set sampling

The maximum likelihood estimator (MLE) and the likelihood ratio test (LRT) will be considered for making inference about the scale parameter of the exponential distribution in case of moving extreme ranked set sampling (MERSS). The MLE and LRT can not be written in closed form. Therefore, a modification of the MLE using the technique suggested by Maharota and Nanda (Biometrika 61:601–606, 1974) will be considered and this modified estimator will be used to modify the LRT to get a test in closed form for testing a simple hypothesis against one sided alternatives. The same idea will be used to modify the most powerful test (MPT) for testing a simple hypothesis versus a simple hypothesis to get a test in closed form for testing a simple hypothesis against one sided alternatives. Then it appears that the modified estimator is a good competitor of the MLE and the modified tests are good competitors of the LRT using MERSS and simple random sampling (SRS).     

Estimating a sensitive proportion through randomized response procedures based on auxiliary information

Randomized response techniques are widely employed in surveys dealing with sensitive questions to ensure interviewee anonymity and reduce nonrespondents rates and biased responses. Since Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering work, many ingenious devices have been suggested to increase respondent’s privacy protection and to better estimate the proportion of people, π _A , bearing a sensitive attribute. In spite of the massive use of auxiliary information in the estimation of non-sensitive parameters, very few attempts have been made to improve randomization strategy performance when auxiliary variables are available. Moving from Zaizai’s (Model Assist Stat Appl 1:125–130, 2006) recent work, in this paper we provide a class of estimators for π _A , for a generic randomization scheme, when the mean of a supplementary non-sensitive variable is known. The minimum attainable variance bound of the class is obtained and the best estimator is also identified. We prove that the best estimator acts as a regression-type estimator which is at least as efficient as the corresponding estimator evaluated without allowing for the auxiliary variable. The general results are then applied to Warner and Simmons’ model.