The effect of immigration on religious belief and practice: A theologizing or alienating experience?

Using data from the New Immigrant Survey, we examine the religious beliefs and practices of new legal immigrants to the United States. We find that Christian immigrants are more Catholic, more Orthodox, and less Protestant than American Christians, and that those immigrants who are Protestant are more likely to be evangelical. In addition to being more Catholic and more Orthodox than American Christians, the new immigrants are also paradoxically less Christian, with a fifth reporting some other faith. Detailed analysis of reported church attendance at places of origin and in the United States suggest that immigration is a disruptive event that alienates immigrants from religious practice rather than “theologizing” them. In addition, our models clearly show that people who join congregations in the United States are highly selected and unrepresentative of the broader population of immigrants in any faith. In general, congregational members were more observant both before and after emigration, were more educated, had more cumulative experience in the United States, and were more likely to have children present in the household and be homeowners and therefore yield biased representations of all adherents to any faith. The degree of selectivity and hence bias also varies markedly both by religion and nationality.     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.     

Generalized model for IGC growth in aluminum alloys

A generalized brick wall model is developed to describe intergranular corrosion in Equi-axed AA7178-T6 and Wingskin AA7178-T6 aluminum alloys. The intergranular corrosion rate is highly related to grain size and shape. High strength aluminum alloys are often elongated and anisotropic, with the fastest nominal IGC growth rate in the longitudinal direction (L) or long transverse direction (T) and the slowest in the short transverse direction (S). We propose a three-way intersection model and use it to simulate the corrosion kinetics for each direction. With a proper combination of model parameters, the generalized IGC model provides a good fit to experimental data developed by the foil penetration technique.     

Describing the Dynamics of Attention to TV Commercials: A Hierarchical Bayes Analysis of the Time to Zap an Ad

This paper provides insights into the dynamics of attention to TV commercials via an analysis of the length of time that commercials are viewed before being 'zapped'. The model, which incorporates a flexible baseline hazard rate and captures unobserved heterogeneity across both consumers and commercials using a hierarchical Bayes approach, is estimated on two datasets in which commercial viewing is captured by a passive online device that continually monitors a household's TV viewing. Consistent with previous findings in psychology about the nature of attentional engagement in TV viewing, baseline hazard rates are found to be non-monotonic. In addition, the data show considerable ad-to-ad and household-to-household heterogeneity in zapping behavior. While one of the datasets contains some information on characteristics of the ads, these data do not reveal any firm links between the ad heterogeneity and the ad characteristics. A number of methodological and computational issues arise in the hierarchical Bayes analysis.     

Can physicians lead other physicians into the future?

This article reflects upon some of the dynamics that prevent physicians from successfully engaging change. Physicians are enculturated to the competitive and hierarchical, and to value personal autonomy. These traits promote distrust and inhibit the formation of collaborative relationships. At this time of growing complexity, when most other industries are developing styles of work based on teamwork, worker empowerment, cross training, and information sharing, physicians cling to the metaphor of the ship's captain, a lone decision-marker and authoritarian possessor of grand knowledge. And yet, in order to lead, physicians need to learn to work differently and nurture a more collaborative approach. The author's blueprint for change includes: Stop trying to manage consensus; commit to measured accountability; think systemically; don't make the mistake of thinking that people will follow because you are right; and, most importantly, create relationships based on shared purpose and principles.     

Making the health care delivery system accountable.

Accountability has become the fact of life for the health care provider and the delivery system. Until recently, accountability has been viewed primarily through the judicial process as issues of fraud and liability, or by managed care entities through evaluation of the financial bottom line. It is this second consideration and its ramifications that will be explored in this article. Appropriate measurement tools are needed to evaluate services, delivery, performance, customer satisfaction, and outcomes assessment. Measurement tools will be considered in light of the industry's unique considerations and realities. All participants, including insurers, employers, management, and health care providers and recipients, bear responsibilities which necessitate assessment and analysis. However, until the basic question, "Who is the customer?" is resolved, accountability issues remain complex and obscured. Accountability costs and impacts must be evaluated over time. They go way beyond bottom line cost containment and reduction. Accountability will be accomplished when the health care industry implements quality and measurement concepts that yield the highest levels of validity and appropriateness for health care delivery.     

A critical look at Lo's modified R/S statistic

We report on an empirical investigation of the modified rescaled adjusted range or R/S statistic that was proposed by Lo, 1991. Econometrica 59, 1279–1313, as a test for long-range dependence with good robustness properties under ‘extra’ short-range dependence. In contrast to the classical R/S statistic that uses the standard deviation S to normalize the rescaled range R, Lo's modified R/S-statistic V_q is normalized by a modified standard deviation S_q which takes into account the covariances of the first q lags, so as to discount the influence of the short-range dependence structure that might be present in the data. Depending on the value of the resulting test-statistic V_q, the null hypothesis of no long-range dependence is either rejected or accepted. By performing Monte-Carlo simulations with ‘truly’ long-range- and short-range dependent time series, we study the behavior of V_q, as a function of q, and uncover a number of serious drawbacks to using Lo's method in practice. For example, we show that as the truncation lag q increases, the test statistic V_q has a strong bias toward accepting the null hypothesis (i.e., no long-range dependence), even in ideal situations of ‘purely’ long-range dependent data.     

Comparing two measures of creatinine clearance: an application of errors-in-variables and bootstrap techniques

Two indices of creatinine clearance (an index of kidney function) are compared on a group of cancer patients who underwent chemotherapy with a potentially nephrotoxic drug. The standard index, measured creatinine clearance MCC, is cumbersome to use, whereas the more convenient alternative, estimated creatinine clearance ECC, has not yet been conclusively evaluated on cancer patients. We conclude that under certain clinical conditions ECC and MCC are identically calibrated for males, but not for females, and we obtain estimated true and false positive rates for assessing the use of ECC instead of MCC as a diagnostic tool. We use a model that is formally equivalent to an errors-in-variables model with (unbalanced) repeated observations and correlated measurement errors. The bootstrap is used to obtain standard errors and confidence limits.     

Modelling Variability in the Branching Structure of Strawberry Inflorescences

The branching structure of inflorescences of the cultivated strawberry ( Fragaria × ananassa Duch.) is very variable. This paper demonstrates that some aspects of this variability are well described by a simple stochastic model of branching that has two adjustable parameters. The model is shown to provide a good fit to data from a set of almost 700 inflorescences of the cultivar Elsanta, collected over two successive years. For one parameter the maximum likelihood estimator is a moment estimator which is fully efficient even if the detailed branching structure of the inflorescences is not recorded. This parameter provides a convenient summary of branching vigour. The maximum likelihood estimator of the second parameter must be determined iteratively and can be quite inefficient unless the full branching structure is recorded. The model demonstrates that branching structure is affected by the order in which inflorescences emerge on the plant.     

Prior distribution assessment for a multivariate normal distribution: an experimental study

A variety of methods of eliciting a prior distribution for a multivariate normal (MVN) distribution have recently been proposed. This paper reports an experiment in which 16 meteorologists used the methods to quantify their opinions about climatology variables. Our results compare prior models and show, in particular, that it can be better to assume the mean and variance of an MVN distribution are independent a priori, rather than to model opinion by the conjugate prior distribution. Using a proper scoring rule, different forms of assessment task are examined and alternative ways of estimating parameters are compared. To quantify opinion about means, it proved preferable to ask directly about the means rather than individual observations while, to quantify opinion about the variance matrix, it was best to ask about deviations from the mean. Further results include recommendations for the way parameters of the prior distribution are estimated.