A re-evaluation of random-effects meta-analysis

Summary.  Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.     

Automating and evaluating reversible jump MCMC proposal distributions

The reversible jump Markov chain Monte Carlo (MCMC) sampler (Green in Biometrika 82:711–732, 1995) has become an invaluable device for Bayesian practitioners. However, the primary difficulty with the sampler lies with the efficient construction of transitions between competing models of possibly differing dimensionality and interpretation. We propose the use of a marginal density estimator to construct between-model proposal distributions. This provides both a step towards black-box simulation for reversible jump samplers, and a tool to examine the utility of common between-model mapping strategies. We compare the performance of our approach to well established alternatives in both time series and mixture model examples.     

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model. Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the r$r$th moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin et al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214–223]. The usefulness of these models is illustrated in a simulation study and in applications to three real data sets.     

A new estimation method for Weibull-type tails based on the mean excess function

Studying the right tail of a distribution, one can classify the distributions into three classes based on the extreme value index γ$\gamma$. The class γ>0$\gamma >0$ corresponds to Pareto-type or heavy tailed distributions, while γ<0$\gamma <0$ indicates that the underlying distribution has a finite endpoint. The Weibull-type distributions form an important subgroup within the Gumbel class with γ=0$\gamma =0$. The tail behaviour can then be specified using the Weibull tail index. Classical estimators of this index show severe bias. In this paper we present a new estimation approach based on the mean excess function, which exhibits improved bias and mean squared error. The asserted properties are supported by simulation experiments and asymptotic results. Illustrations with real life data sets are provided.     

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model. The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil.     

Quality_Family Deployment: A New Perspective in Determining Priority Importance for Improving Work Performance in Organization

Determining priority importance is a matter of concerns among the organization to improve their performance. One of the important aspects that should be considered by the organization is management of human resources, comprising of members who have their own family life. In this paper, we deliver a new perspective for organization to provide priority importance for their members with respect to family matters in order to inculcate the sense of belonging in the organization. To be effective in considering family matters in the organizational policy making, closer look of family characteristics are required. The idea of translating several family characteristics as quality variables and applying the quality function deployment (QFD) method to these variables could present a new way of improving the decision making in the organization by considering the process of decision in the family. Quality function deployment for family (QFDF) produces a friendly interpretation of a highly complex and intangible matters around family life; thus, making the assessment of a family easier. QFD is applied on the data gathered from a questionnaire survey based on 1,213 families in West Malaysia, Malaysia. Twelve variables are identified as voice of family, where seven of them are found to have the highest priority of importance. These variables could also be translated into quality variables in the context of organization, the strategy of improvement for the family could be interpreted as strategy for improvement in the organization. Furthermore, the results of this study provide suggestion to improve actions for managing human resource. In the discussion, three most high ranked variables from both perspectives of family and organization are considered.     

Measuring Active Citizenship through the Development of a Composite Indicator

This article defines Active Citizenship within a European context as a broad range of value based participation. It develops a framework for measuring this phenomenon which combines the four dimensions of Protest and Social Change, Community Life, Representative Democracy and Democratic values. The European Social Survey 2002 is used to populate the framework as this survey provided the best data coverage available and covered 19 European Countries. In total 61 indicators were selected. A composite indicator (CI), The Active Citizenship Composite Indicator (ACCI), is built using the framework provided and using experts’ weights. In addition, the robustness of the results is tested using sensitivity analysis. The limitations to the ACCI are explained in terms of the limitation of the data availability in particular concerning the new forms of participation and less organised forms of participation. Nevertheless, ACCI proved to be statistically robust and reliable and proved to be a useful tool for monitoring levels of citizenship in Europe. The results of ACCI exhibit interesting and quite distinct regional patterns. The results showed that the Nordic countries, and in particular Sweden, have the highest rate of Active Citizenship, followed by Central Europe and Anglo-Saxon countries. Mediterranean countries are next followed by Eastern European countries that close the ranking.

文化生态与艺术传承——以热贡艺术为例

本文结合田野调查资料,根据热贡文化生态特点,从文化生态学、艺术人类学角度,探讨了热贡艺术的文化生态,及生态系统中的文化特质,并时热贡艺术产业化发展进行了思考.     

Racial Disparities in Early Criminal Justice Involvement

Criminologists have long reported the existence of racial disparity in the criminal justice system, but the important question is why. While some argue that observed differences are a consequence of more criminal behavior among minorities, the weight of the evidence indicates that this is but a partial explanation. In this paper, we study data from a sample of juveniles to examine how racial differences in early police contact, and important social environments—family, school, and neighborhoods—affect later contact and arrests, controlling for self-reported delinquency. We find that early (in middle school) contact with police is an important predictor of later (high school) arrests. Also we found that, in addition to being male and living in a low-income family, children who have parents who have a history of arrest, who have experienced school disciplinary actions, who have delinquent peers, and who are in networks with deviant adults are more likely to have problems with law enforcement. These factors help to explain racial differences in police contacts and arrests.