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.     

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     

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.     

Defining delayed consequences as reinforcers: some do, some don't, and nothing changes

Results of a survey sent to members of the editorial boards of five behavior-analytic journals in 1990 indicated that there was no consensus among respondents with respect to whether delayed events can function as reinforcers (Schlinger, Blakely, Fillhard, & Poling, 1991). Since that time, several studies with nonhuman animals have demonstrated that events delayed by at least several seconds can increase the probability of occurrence of the responses that produce them. To determine the relative number of contemporary behavior analysts who nonetheless believe that reinforcers must immediately follow the responses that produce them, the present study replicated the procedures used by Schlinger et al., with similar results. That is, respondents did not agree with respect to whether delayed events could be reinforcers.     

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.