Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria

There is currently much discussion about lasso-type regularized regression which is a useful tool for simultaneous estimation and variable selection. Although the lasso-type regularization has several advantages in regression modelling, owing to its sparsity, it suffers from outliers because of using penalized least-squares methods. To overcome this issue, we propose a robust lasso-type estimation procedure that uses the robust criteria as the loss function, imposing L₁-type penalty called the elastic net. We also introduce to use the efficient bootstrap information criteria for choosing optimal regularization parameters and a constant in outlier detection. Simulation studies and real data analysis are given to examine the efficiency of the proposed robust sparse regression modelling. We observe that our modelling strategy performs well in the presence of outliers.     

Piecewise FARIMA models for long-memory time series

We consider the problem of modelling a long-memory time series using piecewise fractional autoregressive integrated moving average processes. The number as well as the locations of structural break points (BPs) and the parameters of each regime are assumed to be unknown. A four-step procedure is proposed to find out the BPs and to estimate the parameters of each regime. Its effectiveness is shown by Monte Carlo simulations and an application to real traffic data modelling is considered.     

高等教育规模扩张何以实现——以“资源稀释模型”为视角

＂资源稀释模型＂认为,在家庭经济资源一定的情况下,家庭里的孩子数量越多,每个孩子分到的经济资源就会相应减少;而且家庭的经济资源对孩子是否有机会接受高等教育的影响非常重要。笔者从＂资源稀释模型（Resource Dilution Model）＂这一视角来分析和探讨,上世纪末我国＂高等教育规模扩张＂政策何以得以顺利实施。     

Incorporating Time‐Dose‐Response into Legionella Outbreak Models

A novel method was used to incorporate in vivo host–pathogen dynamics into a new robust outbreak model for legionellosis. Dose‐response and time‐dose‐response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best‐fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best‐fit TDR from the murine dosing study was the beta‐Poisson with exponential‐reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non‐time‐dependent distributions to explore the performance of the time‐dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host‐pathogen system.     

基于分权视角的城市规划管理体制模式及改革建议

关于我国的城市规划管理权限一直存在＂集中＂与＂放权＂的讨论,并且城市规划管理体制也存在差异。通过对全国部分城市的规划管理体制进行梳理,从市、区（县、市）两级分权视角总结出我国城市规划管理体制中的＂垂直型＂、＂半垂直型＂和＂非垂直型＂三种典型模式,分别以南京、北京和上海为案例分析三种规划管理体制典型模式的主要特点和事权划分,并对其各自的优缺点进行比较,最后提出我国城市规划管理体制改革的若干建议,包括将规划决策权＂上收＂与实施管理重心＂下移＂、建立＂市—区—镇（街）＂三级规划管理机构体系、整合市区两级规划部门内部机构设置形成＂大处室＂和＂大科室＂以及完善城市规划委员会制度等。     

Bivariate symbolic regression models for interval-valued variables

Interval-valued variables have become very common in data analysis. Up until now, symbolic regression mostly approaches this type of data from an optimization point of view, considering neither the probabilistic aspects of the models nor the nonlinear relationships between the interval response and the interval predictors. In this article, we formulate interval-valued variables as bivariate random vectors and introduce the bivariate symbolic regression model based on the generalized linear models theory which provides much-needed exibility in practice. Important inferential aspects are investigated. Applications to synthetic and real data illustrate the usefulness of the proposed approach.     

The Box–Cox Transformation and Non‐Iterative Estimation Methods for Ordinal Log‐Linear Models

Recently Beh and Farver investigated and evaluated three non‐iterative procedures for estimating the linear‐by‐linear parameter of an ordinal log‐linear model. The study demonstrated that these non‐iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box–Cox transformation. We also show that by using this transformation, accurate non‐iterative estimates are achievable even when a contingency table contains sampling zeros.     

Child‐caregiver Attachment Representations in a Non‐Western Context: The Feasibility and Cultural Equivalence of Story Stems in Urban Ghana

Story stem measures are an increasingly popular method for assessing the attachment representations of young children, but little is known of their cross‐cultural applicability. This study aimed to characterise the attachment representations in 73 five‐ to eight‐year‐old children in urban Ghana, West Africa, using the Manchester Child Attachment Story Task (MCAST) to test its feasibility, psychometric characteristics and concurrent associations with caregiver‐ and teacher‐rated child behaviour, and to conduct a qualitative thematic analysis of methodological observations. Among the classifiable cases (92 percent), all attachment classifications were observed, yielding a higher rate of secure attachment than in European samples. Inter‐rater reliability, internal consistency, and internal structure were reasonable and largely similar to European studies, although one structural difference was the separation of ‘child assuagement of distress’ from other secure‐related items. MCAST narratives were associated with teacher‐ and caregiver‐rated hyperactivity, but internal consistency was low in most Strengths and Difficulties Questionnaire scales. Possible culturally‐sensitive explanations for our psychometric and qualitative findings are discussed. Overall, story stems are a promising tool for accessing attachment representations in non‐Western samples, although modifications are likely to improve cross‐cultural equivalence when applied to non‐Western cultures. Further investigation is needed to link MCAST outcomes to parenting and socio‐emotional development.     

Editorial collaborators

A simulation study of the binomial-logit model with correlated random effects is carried out based on the generalized linear mixed model (GLMM) methodology. Simulated data with various numbers of regression parameters and different values of the variance component are considered. The performance of approximate maximum likelihood (ML) and residual maximum likelihood (REML) estimators is evaluated. For a range of true parameter values, we report the average biases of estimators, the standard error of the average bias and the standard error of estimates over the simulations. In general, in terms of bias, the two methods do not show significant differences in estimating regression parameters. The REML estimation method is slightly better in reducing the bias of variance component estimates.