Bayesian analysis of elapsed times in continuous‐time Markov chains

The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.     

Model Diagnostics for Remote Access Regression Servers

To protect public-use microdata, one approach is not to allow users access to the microdata. Instead, users submit analyses to a remote computer that reports back basic output from the fitted model, such as coefficients and standard errors. To be most useful, this remote server also should provide some way for users to check the fit of their models, without disclosing actual data values. This paper discusses regression diagnostics for remote servers. The proposal is to release synthetic diagnostics—i.e. simulated values of residuals and dependent and independent variables–constructed to mimic the relationships among the real-data residuals and independent variables. Using simulations, it is shown that the proposed synthetic diagnostics can reveal model inadequacies without substantial increase in the risk of disclosures. This approach also can be used to develop remote server diagnostics for generalized linear models.     

Age Cohort, Ancestry, and Immigrant Generation Influences in Family Relations and Psychological Well-Being among Black Caribbean Family Members

Immigration is contributing to the U.S. population becoming increasingly ethnically diverse. This article examines the role of family relations and well-being among different generations of Black Caribbean immigrants. Family disruptions, such as migration, can have complex effects on the support networks and emotional well-being of family members. Data from a recently completed national study of American Blacks in the United States, however, reveal significant similarities across ancestry and immigrant status in family contact, solidarity and well-being. It is concluded that intrafamilial relations may serve to overcome barriers of geographical distance in providing comparable levels of contact, solidarity, and well-being for both U.S.-born and immigrant Black Caribbean family members of different generations. Because of the increasing numbers of immigrant elders, these sources of family support will become increasingly more important in bridging the gaps between government resources and needed assistance in an aging society.     

Negotiating the “Global” within the Global Playscapes of Mount Everest*

Within the anthropology of tourism, “tourist” and “local” are often used to conceptualize social relations constitutive of international tourism, where “First World” mobile subjects visit stationary “Third World” “Others.” Globalization, as both discourse and condition, is changing the contours and conceptualizations of tourist spaces. In this paper, I show how “the global” is negotiated by different subjects, and how recreational mountain climbers from Nepal negotiate their identities as “locals” as well as global, mobile, travel subjects within “the global playscapes” of Himalayan mountaineering. I suggest that the question of who can be a tourist within a globalizing world should be closely examined.     

Sets of alternatives as Condorcet winners

We characterize sets of alternatives which are Condorcet winners according to preferences over sets of alternatives, in terms of properties defined on preferences over alternatives. We state our results under certain preference extension axioms which, at any preference profile over alternatives, give the list of admissible preference profiles over sets of alternatives. It turns out to be that requiring from a set to be a Condorcet winner at every admissible preference profile is too demanding, even when the set of admissible preference profiles is fairly narrow. However, weakening this requirement to being a Condorcet winner at some admissible preference profile opens the door to more permissive results and we characterize these sets by using various versions of an undomination condition. Although our main results are given for a world where any two sets – whether they are of the same cardinality or not – can be compared, the case for sets of equal cardinality is also considered. Received: 15 March 2001/Accepted: 31 May 2002 This paper was written while Barış Kaymak was a graduate student in Economics at Boğazi?i University. We thank ?ağatay Kayı and İpek ?zkal-Sanver who kindly agreed to be our initial listeners. The paper has been presented at the Economic Theory seminars of Bilkent, Ko? and Sabancı Universities as well as at the Fifth Conference of the Society for the Advancement of Economic Theory, July 2001, Ischia, Italy and at the 24^th Bosphorus Workshop on Economic Design, August 2001, Bodrum, Turkey. We thank Fuad Aleskerov, İzak Atiyas, ?zgür Kıbrıs, Semih Koray, Gilbert Laffond, Bezalel Peleg, Murat Sertel, Tayfun S?nmez, Utku ünver and all the participants. Remzi Sanver acknowledges partial financial support from İstanbul Bilgi University and the Turkish Academy of Sciences and thanks Haluk Sanver and Serem Ltd. for their continuous moral and financial support. Last but not the least, we thank Carmen Herrero and two anonymous referees. Of course we are the sole responsible for all possible errors.     

Nonresponse weighting adjustment using estimated response probability

To reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator.     

Case study in the Bayesian analysis of a cost‐effectiveness trial in the evaluation of health care technologies: Depression

We present a case study based on a depression study that will illustrate the use of Bayesian statistics in the economic evaluation of cost‐effectiveness data, demonstrate the benefits of the Bayesian approach (whilst honestly recognizing any deficiencies) with respect to frequentist methods, and provide details of using the methods, including computer code where appropriate. Copyright © 2003 John Wiley & Sons, Ltd.