Screening for prostate cancer by using random-effects models

Summary. Random-effects models are used to screen male participants in a long-term longitudinal study for prostate cancer. By using posterior probabilities, each male can be classified into one of four diagnostic states for prostate disease: normal, benign prostatic hyperplasia, local cancer and metastatic cancer. Repeated measurements of prostate-specific antigen, collected when there was no clinical evidence of prostate disease, are used in the classification process. An individual's screening data are considered one repeated measurement at a time as his data are collected longitudinally over time. Posterior probabilities are calculated on the basis of data from other individuals with confirmed diagnoses of each of the four diagnostic states.     

Permutation Tests for Comparing Inequality Measures

ABSTRACT

Asymptotic and bootstrap tests for inequality measures are known to perform poorly in finite samples when the underlying distribution is heavy-tailed. We propose Monte Carlo permutation and bootstrap methods for the problem of testing the equality of inequality measures between two samples. Results cover the Generalized Entropy class, which includes Theil’s index, the Atkinson class of indices, and the Gini index. We analyze finite-sample and asymptotic conditions for the validity of the proposed methods, and we introduce a convenient rescaling to improve finite-sample performance. Simulation results show that size correct inference can be obtained with our proposed methods despite heavy tails if the underlying distributions are sufficiently close in the upper tails. Substantial reduction in size distortion is achieved more generally. Studentized rescaled Monte Carlo permutation tests outperform the competing methods we consider in terms of power.     

A Bayesian approach for misclassified ordinal response data

ABSTRACT

Motivated by a longitudinal oral health study, the Signal-Tandmobiel^® study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulation-based example and the analysis of the motivating study.     

The New Face of Political Islam in Central Asia: The Rise of Islamo-democrats

Political Islam in Central Asia is currently undergoing a transitional phase. Radical groups such as the Islamic Movement of Uzbekistan and Hizb ut-Tahrir no longer monopolize the Islamist scene. There is now a new generation of Islamist leaders in Kyrgyzstan, Kazakhstan, and Tajikistan that advocate a public role for Islam without seeking regime change. They can be called Islamo-democrats because they participate in elections and recognize the constitutional process. The article will examine and compare the biography, political career, and beliefs of three representatives of this political trend: Tursunbai Bakir Uulu, Bekbolat Tleukhan, and Mohiddin Kabiri. It will claim that the emergence of Islamo-democrats is partly the result of developments in Turkey, especially the rise to power of the Adalet ve Kalkinma Partisi. Two factors account for the Turkish influence in Kyrgyzstan, Kazakhstan, and Tajikistan: the successful diffusion of ideological norms and the importance that governments attach to maintaining good relations with Turkey.     

The construction,composition and rationale of immigrants’ network: The support strategies of Ghanaian immigrants in Toronto,Canada

In this article, we investigate the construction, composition and rationale behind the personal networks of recent immigrants to Canada. Drawing on egocentric-network analysis and interviews with 172 Ghanaian immigrants in Toronto, we reveal their networking strategies during their integration. First, we identify social locations that help create ties with different groups: workplaces and schools offer access to ties with non-immigrants and other immigrants alike, while religious and ethnic organizations facilitate ties to co-nationals (i.e., Ghanaians). Second, most individuals within immigrants’ network are co-nationals whose relationship began in Canada, followed by sustained transnational ties in the origin and diaspora and few ties with the non-immigrant. The nature of this network is explained by examining the migration project of immigrants together with the context of reception in Canada, which suggests a desire by immigrants to stay in Canada and make Canada their second home. The implications of these findings are discussed.     

Tabulations for value at risk and expected shortfall

Value at risk and expected shortfall are the two most popular measures of financial risk. Here, we tabulate expressions for both these measures for over 100 parametric distributions, including all commonly known distributions, and illustrate a data application. We expect that this collection of expressions could serve as a source of reference and encourage further research with respect to measures of financial risk.     

Is there a causal relationship between oil prices and tourist arrivals?

This application note investigates the causal relationship between oil price and tourist arrivals to further explain the impact of oil price volatility on tourism-related economic activities. The analysis itself considers the time domain, frequency domain and information theory domain perspectives. Data relating to US and nine European countries are exploited in this paper with causality tests which include time domain, frequency domain, and Convergent Cross Mapping (CCM). The CCM approach is nonparametric and therefore not restricted by assumptions. We contribute to existing research through the successful and introductory application of an advanced method, and via the uncovering of significant causal links from oil prices to tourist arrivals.     

Estimating Housing Quality for Poverty and Development Policy Analysis: CWIQ in Ghana

Inadequate data on housing characteristics and lack of consensusregarding appropriate measures of housing quality characteristicsretard developments to meet housing needs of people in lessdeveloped countries. A constructed housing quality index is developed and Cronbach's alpha and factor analysis are employedeo evaluate its measurement properties. With data from the CoreWelfare Indicators Questionnaire (CWIQ) survey of 1997, theconstructed housing quality index identifies key factors relatedto housing quality in Ghana. Access to quality of life amenities contributes significant information and policy value to traditionalphysical attributes in modeling a housing quality index.     

SELECTION OF WEIGHTS FOR WEIGHTED MODEL AVERAGING

We address the task of choosing prior weights for models that are to be used for weighted model averaging. Models that are very similar should usually be given smaller weights than models that are quite distinct. Otherwise, the importance of a model in the weighted average could be increased by augmenting the set of models with duplicates of the model or virtual duplicates of it. Similarly, the importance of a particular model feature (a certain covariate, say) could be exaggerated by including many models with that feature. Ways of forming a correlation matrix that reflects the similarity between models are suggested. Then, weighting schemes are proposed that assign prior weights to models on the basis of this matrix. The weighting schemes give smaller weights to models that are more highly correlated. Other desirable properties of a weighting scheme are identified, and we examine the extent to which these properties are held by the proposed methods. The weighting schemes are applied to real data, and prior weights, posterior weights and Bayesian model averages are determined. For these data, empirical Bayes methods were used to form the correlation matrices that yield the prior weights. Predictive variances are examined, as empirical Bayes methods can result in unrealistically small variances.     

On the effect of the number of quadrature points in a logistic random effects model: an example

Although generalized linear mixed models are recognized to be of major practical importance, it is also known that they can be computationally demanding. The problem is the evaluation of the integral in calculating the marginalized likelihood. The straightforward method is based on the Gauss–Hermite technique, based on Gaussian quadrature points. Another approach is provided by the class of penalized quasi-likelihood methods. It is commonly believed that the Gauss–Hermite method works relatively well in simple situations but fails in more complicated structures. However, we present here a strikingly simple example of a logistic random-intercepts model in the context of a longitudinal clinical trial where the method gives valid results only for a high number of quadrature points ( Q ). As a consequence, this result warns the practitioner to examine routinely the dependence of the results on Q . The adaptive Gaussian quadrature, as implemented in the new SAS procedure NLMIXED, offered the solution to our problem. However, even the adaptive version of Gaussian quadrature needs careful handling to ensure convergence.