Masking methods that preserve positivity constraints in microdata

Statistical agencies have conflicting obligations to protect confidential information provided by respondents to surveys or censuses and to make data available for research and planning activities. When the microdata themselves are to be released, in order to achieve these conflicting objectives, statistical agencies apply statistical disclosure limitation (SDL) methods to the data, such as noise addition, swapping or microaggregation. Some of these methods do not preserve important structure and constraints in the data, such as positivity of some attributes or inequality constraints between attributes. Failure to preserve constraints is not only problematic in terms of data utility, but also may increase disclosure risk.In this paper, we describe a method for SDL that preserves both positivity of attributes and the mean vector and covariance matrix of the original data. The basis of the method is to apply multiplicative noise with the proper, data-dependent covariance structure.     

Two‐sample empirical likelihood ratio tests for medians in application to biomarker evaluations

The median is a commonly used parameter to characterize biomarker data. In particular, with two vastly different underlying distributions, comparing medians provides different information than comparing means; however, very few tests for medians are available. We propose a series of two‐sample median‐specific tests using empirical likelihood methodology and investigate their properties. We present the technical details of incorporating the relevant constraints into the empirical likelihood function for in‐depth median testing. An extensive Monte Carlo study shows that the proposed tests have excellent operating characteristics even under unfavourable occasions such as non‐exchangeability under the null hypothesis. We apply the proposed methods to analyze biomarker data from Western blot analysis to compare normal cells with bronchial epithelial cells from a case–control study. The Canadian Journal of Statistics 39: 671–689; 2011. © 2011 Statistical Society of Canada     

A linear model-based test for the heterogeneity of conditional correlations

Current methods of testing the equality of conditional correlations of bivariate data on a third variable of interest (covariate) are limited due to discretizing of the covariate when it is continuous. In this study, we propose a linear model approach for estimation and hypothesis testing of the Pearson correlation coefficient, where the correlation itself can be modeled as a function of continuous covariates. The restricted maximum likelihood method is applied for parameter estimation, and the corrected likelihood ratio test is performed for hypothesis testing. This approach allows for flexible and robust inference and prediction of the conditional correlations based on the linear model. Simulation studies show that the proposed method is statistically more powerful and more flexible in accommodating complex covariate patterns than the existing methods. In addition, we illustrate the approach by analyzing the correlation between the physical component summary and the mental component summary of the MOS SF-36 form across a fair number of covariates in the national survey data.     

Combining least-squares and quantile regressions

Least-squares and quantile regressions are method of moments techniques that are typically used in isolation. A leading example where efficiency may be gained by combining least-squares and quantile regressions is one where some information on the error quantiles is available but the error distribution cannot be fully specified. This estimation problem may be cast in terms of solving an over-determined estimating equation (EE) system for which the generalized method of moments (GMM) and empirical likelihood (EL) are approaches of recognized importance. The major difficulty with implementing these techniques here is that the EEs associated with the quantiles are non-differentiable. In this paper, we develop a kernel-based smoothing technique for non-smooth EEs, and derive the asymptotic properties of the GMM and maximum smoothed EL (MSEL) estimators based on the smoothed EEs. Via a simulation study, we investigate the finite sample properties of the GMM and MSEL estimators that combine least-squares and quantile moment relationships. Applications to real datasets are also considered.     

Addressing common forms of child maltreatment: evidence‐informed interventions and gaps in current knowledge

This paper reviews interventions for preventing the occurrence and recurrence of major types of child maltreatment. We begin with an overview of the challenges of establishing evidence‐based interventions to prevent child abuse and neglect in many countries, and underscore the importance of this need with child maltreatment incidence rates in the USA, and how much each type and subtype contribute to child out‐of‐home placement. Next, we identify the well‐supported, supported and promising interventions for each child maltreatment type and subtype, according to their level of research evidence using an evidence‐based clearing house. The paper closes with a discussion of the implications for practice, evaluation, policy and agency management, including intervention knowledge gaps that showcase areas that need additional practice research.     

A Smooth Bootstrap Procedure towards Deriving Confidence Intervals for the Relative Risk

Given a pair of sample estimators of two independent proportions, bootstrap methods are a common strategy towards deriving the associated confidence interval for the relative risk. We develop a new smooth bootstrap procedure, which generates pseudo-samples from a continuous quantile function. Under a variety of settings, our simulation studies show that our method possesses a better or equal performance in comparison with asymptotic theory based and existing bootstrap methods, particularly for heavily unbalanced data in terms of coverage probability and power. We illustrate our procedure as applied to several published data sets.     

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Global regression assumes that a single model adequately describes all parts of a study region. However, the heterogeneity in the data may be sufficiently strong that relationships between variables can not be spatially constant. In addition, the factors involved are often sufficiently complex that it is difficult to identify them in the form of explanatory variables. As a result Geographically Weighted Regression (GWR) was introduced as a tool for the modeling of non-stationary spatial data. Using kernel functions, the GWR methodology allows the model parameters to vary spatially and produces non-parametric surfaces of their estimates. To model count data with overdispersion, it is more appropriate to use a negative binomial distribution instead of a Poisson distribution. Therefore, we propose the Geographically Weighted Negative Binomial Regression (GWNBR) method for the modeling of data with overdispersion. The results obtained using simulated and real data show the superiority of this method for the modeling of non-stationary count data with overdispersion compared with competing models, such as global regressions, e.g., Poisson and negative binomial and Geographically Weighted Poisson Regression (GWPR). Moreover, we illustrate that these competing models are special cases of the more robust model GWNBR.     

Analysis and Implications of Retraction Period and Coauthorship of Fraudulent Publications

Studies have indicated that the number and frequency of fraudulent publications being retracted are not subsiding even with greater awareness of such incidents in the recent decades. In this study, the trends of retraction period, number of citations and coauthors of 5 selected researchers who had ≥ 15 fraudulent publications retracted were analyzed. The obtained results shows that the retraction period is 48.96 ± 32.16 months for the 113 publications affiliated to the 5 studied researchers. There are a total of 180 coauthors with 6.40 ± 3.26 coauthors per researcher's retracted publication. The linear regression analysis indicates that there is limited correlation (R² = .008) between the citation counts and retraction period. The p value for multiple F-tests to assess the number of coauthors to a fraudulent publication on an interresearcher basis is found to be ranging from < .001 to .458. It is also found that a better correlation (R² = .592) exists between the likelihood of a researcher to involve different individuals for isolated fraudulent publications while only selecting very few to be their frequent coauthors of their mischievous acts. With this study, the possible use of coauthors as a strategy for publishing fraudulent work and a potential approach to tighten coauthorship are discussed.     

Model‐based Prediction In Ecological Surveys Including Those with Incomplete Detection

This paper explores and develops model‐based predictors for surveys of plants and wildlife including those with incomplete detection. The methodology allows for estimating a detection function to account for objects which were not detected at the time of the survey. The model‐based theory utilises generalized linear models (GLMs) and is either new or adapted from other areas of sampling. A simulation study is used to validate the estimators and comparisons are made with an integrated likelihood approach. An aerial survey of kangaroos in western New South Wales is used to illustrate the theory. The area within 50m of the aircraft is treated as a strip transect and mark‐recapture methods are used to estimate the detection function.     

一个早期的世界网络:欧亚宗教

横亘欧亚大陆大部分地区的宗教在公元前4000年到公元200年间得到了创造性的蓬勃发展,这一时期的思想、艺术、实践得以集中共享。由于历史原因,存在于西方文化长廊末端的基督教与东方佛教之间的联系在大约5-15世纪间开始大量减少,15世纪以后,这种关系进入了重建期。基督教和佛教这两种彼此“分离”的宗教看起来十分不同。尽管每种宗教都有其自身的历史,但就其本质而言,诸宗教之间则存在着许许多多的关联。信仰间的对话不是必须的,但欧亚宗教最好被视为一个错综复杂的网络,在这个网络里,制度化的“宗教”是这个复杂网络的服务器。宗教的“象征符号”可被视为某种类似于DNA解码序列的东西。