A robust modification of the ordered-heterogeneity test

An ordered heterogeneity (OH) test is a test for a trend that combines a non-directional heterogeneity test with the rank-order information specified under the alternative. We propose two modifications of the OH test procedure: (1) to use the mean ranks of the groups rather than the sample means to determine the observed ordering of the groups, and (2) to use the maximum correlation out of the 2^k???1 – 1 possibilities under the alternative rather than the single ordering (1, 2, … , k), where k is the number of independent groups. A simulation study indicates that these two changes increase the power of the ordered heterogeneity test when, as common in practice, the underlying distribution may deviate from a normal distribution and the trend pattern is a priori unknown. In contrast to the original OH test, the modified OH test can detect all possible patterns under the alternative with a relatively high power.     

How Can the Process of Parental Alienation and the Alienator Be Effectively Treated?

This article discusses primarily approaches or methods in dealing with a parent who alienates a child against another parent due to the hostility developed following divorce and separation. The basic vision or aims for combating parents in conflict are delineated. This is followed by the importance of being aware of the problems resulting in parental alienation (PA). To overcome PA an orderly set of stages from soft options to severe strategies are presented, with reasons for the use of the more severe method being provided. Finally, there is an appeal to the family courts and its judges to consider seriously the conclusions reached by one expert witness in how to combat PA by considering first and foremost the short- and long-term needs of the child and secondarily the alienated parent.     

Measuring Success in Intentional Communities: A Critical Evaluation of Commitment and Longevity Theories

Abstract

Intentional Communities (ICs) are groups of people that form for a specific agreed-upon purpose and live in close proximity to achieve their desired end. The prevailing scholarship in the study of these communities is that communal processes of commitment, as well as ethnic and linguistic homogeneity, play a strong role in determining a community's success when defined as how long-lived a community was. However, most of these conclusions are based in historical research on communities that no longer exist. In this article, we use survey data collected from present-day ICs to find that those assumptions do not necessarily hold true, and we propose a definition of success that incorporates how well communities report satisfaction fulfilling their intentions. By testing a sample of living ICs, we find that the community decision-making structure is more important than any other factor in determining whether communities reports progress toward this metric of success.     

Employee Engagement and Organizational Behavior Management

No abstract available for this article.     

Some asymptotic results on generalized penalized spline smoothing

Summary.  The paper discusses asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be a priori normally distributed links the smoothing framework to generalized linear mixed models. We consider the asymptotic rates such that the Laplace approximation is justified and the resulting fits in the mixed model correspond to penalized spline estimates. Secondly, we make use of a fully Bayesian viewpoint by imposing an a priori distribution on all parameters and coefficients. We argue that with the postulated rates at which the spline basis dimension increases with the sample size the posterior distribution of the spline coefficients is approximately normal. The validity of this result is investigated in finite samples by comparing Markov chain Monte Carlo results with their asymptotic approximation in a simulation study.     

Aggregated functional data model for near-infrared spectroscopy calibration and prediction

Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer–Lambert formula. Considering that, for each chemical sample, the resulting spectrum is a continuous curve obtained as the summation of overlapped absorption spectra from each analyte plus a Gaussian error, we assume that each individual spectrum can be expanded as a linear combination of B-splines basis. Calibration is then performed using two procedures for estimating the individual analytes’ curves: basis smoothing and smoothing splines. Prediction is done by minimizing the square error of prediction. To assess the variance of the predicted values, we use a leave-one-out jackknife technique. Departures from the standard error models are discussed through a simulation study, in particular, how correlated errors impact on the calibration step and consequently on the analytes’ concentration prediction. Finally, the performance of our methodology is demonstrated through the analysis of two publicly available datasets.     

Maximum Test versus Adaptive Tests for the Two-Sample Location Problem

For the non-parametric two-sample location problem, adaptive tests based on a selector statistic are compared with a maximum and a sum test, respectively. When the class of all continuous distributions is not restricted, the sum test is not a robust test, i.e. it does not have a relatively high power across the different possible distributions. However, according to our simulation results, the adaptive tests as well as the maximum test are robust. For a small sample size, the maximum test is preferable, whereas for a large sample size the comparison between the adaptive tests and the maximum test does not show a clear winner. Consequently, one may argue in favour of the maximum test since it is a useful test for all sample sizes. Furthermore, it does not need a selector and the specification of which test is to be performed for which values of the selector. When the family of possible distributions is restricted, the maximin efficiency robust test may be a further robust alternative. However, for the family of t distributions this test is not as powerful as the corresponding maximum test.     

Dynamic Discrete-time Duration Models: Estimation via Markov Chain Monte Carlo

Discrete-time or grouped duration data, with one or multiple types of terminating events, are often observed in social sciences or economics. In this paper we suggest and discuss dynamic models for flexible Bayesian nonparametric analysis of such data. These models allow simultaneous incorporation and estimation of baseline hazards and time-varying covariate effects, without imposing particular parametric forms. Methods for exploring the possibility of time-varying effects, as for example the impact of nationality or unemployment insurance benefits on the probability of reemployment, have recently gained increasing interest. Our modeling and estimation approach is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques. A detailed analysis of unemployment duration data, with full-time job, part-time job and other causes as terminating events, illustrates our methods and shows how they can be used to obtain refined results and interpretations.     

Misdirection on the road to Shangri-La

Will life expectancy in the United States rise or fall in this century? The implications of either scenario are far reaching. We contend that the rise of childhood obesity in the United States in the past three decades has been so dramatic that it will soon lead to higher than expected death rates at middle ages and a possible decline in life expectancy by midcentury. The most detrimental health and longevity effects will not be seen for decades--a phenomenon that cannot be detected by current methods used to forecast life expectancy or estimate the number of deaths currently attributable to obesity. This scenario contrasts sharply with the views of mathematical demographers who generate forecasts by relying on the assumption that the U.S. pattern of longevity will follow that of other longer lived nations and on the extrapolation of historical trends in life expectancy into the future.