Identities and inequalities: an examination of the role of racial identity in the formation of social capital inside a voluntary youth organization

This ethnographic study provides empirical illustrations of patterns of racial homophily and network ties within a voluntary youth association. The paper seeks to examine the role of racial identity and its relationship to the formation of social capital among a diverse group of youth participants. Drawing on narrative data, this article explores the kinds of organizational experiences that promote the development of interracial ties as well as how the construction of racial identity influences network formation and enhances social capital. The major findings are that racial homophily (staying within one's own group) are strongest among white participants while blacks are equally likely to form interracial ties with socially dissimilar peers as with socially similar peers. Some gendered and class differences also emerged. Institutional agents were also found to be important in helping youth participants bridge racial barriers.     

Advocacy for Black and Minority Ethnic Communities: Understandings and Expectations

Recent policy has promoted ‘advocacy’ as a meansof promoting social justice for many disadvantaged groups. Yet‘advocacy’ is a contested concept, and the understandingsthat members of disadvantaged groups themselves have of advocacyhave rarely been explored. Previous research indicates thatunderstandings may vary considerably. Using empirical evidencefrom research conducted in Glasgow, Scotland, this paper examinesthe understandings and expectations of ‘advocacy’held by black and minority ethnic (BME) service providers andpotential service users. The BME service providers believedthat they were offering advocacy, and did so in the contextof a marginalized position for their services. The BME communitymembers supported the development of advocacy services, buttheir own marginalization was in many ways reinforced by servicesthey were already using. They had clear ideas about appropriateadvocacy services for their situation. These ideas were groundedin their current situation, and did not necessarily conformto dominant ideas about advocacy. In conclusion, the prospectsfor successful advocacy are assessed.     

Is choice the correct primitive? On using certainty equivalents and reference levels to predict choices among gambles

Choice is viewed as a derived, not a primitive, concept. Individual gambles are assigned subjective certainty equivalents (CE₁); the choice setX has an associated reference level [RL(X)] based on the CE₁S of its members; the outcomes of each gamble are recoded as deviations from the RL(X); and new CE₂S are constructed. The gamble having the largest CE₂ is chosen. The CEs are described by the rank-and sign-dependent theory of Luce (1992b). The concept of RL is studied axiomatically. The model predicts many behavioral anomalies and is tested with data sets of Mellers, Chang, Birnbaum, and Ordóñez (1992).     

Rethinking Risk Attitude: Aspiration as Pure Risk

There exists no completely satisfactory theory of risk attitude in current normative decision theories. Existing notions confound attitudes to pure risk with unrelated psychological factors such as strength of preference for certain outcomes, and probability weighting. In addition traditional measures of risk attitude frequently cannot be applied to non-numerical consequences, and are not psychologically intuitive. I develop Pure Risk theory which resolves these problems – it is consistent with existing normative theories, and both internalises and generalises the intuitive notion of risk being related to the probability of not achieving one’s aspirations. Existing models which ignore pure risk attitudes may be misspecified, and effects hitherto modelled as loss aversion or utility curvature may be due instead to Pure Risk attitudes.     

Two-sample Pitman closeness comparison under progressive Type-II censoring

In this paper, we consider two problems concerning two independent progressively Type-II censored samples. We first consider the Pitman closeness (PC) of order statistics from two independent progressively censored samples to a specific population quantile. We then consider the point prediction of a future progressively censored order statistic and discuss the determination of the closest progressively censored order statistic from the current sample according to the simultaneous closeness probabilities. For both these problems, explicit expressions are derived for the pertinent PC probabilities, and then special cases are given as examples. For various censoring schemes, we also present numerical results for the standard uniform, standard exponential, and standard normal distributions. Finally, a distribution-free result for the median is obtained.     

Parameter estimation in a highly non-linear model using simultaneous perturbation stochastic approximation

Often it is necessary to estimate the parameters of a model or unknown system. Various techniques exist to accomplish this task, including Kalman and Wiener filtering, least-mean-square (LMS) algorithms, and the Levenberg-Marquardt(L-M) algorithm. These techniques require an analytic form of the gradient of the function of the parameters to be estimated. A key feature of the simultaneous perturbation stochastic approximation (SPSA) method is that it is a gradient-free optimization technique (Spall; 1992,1998a,b, 1999). In the current problem, the function of parameters to be identified is highly non-linear and of sufficient difficulty that obtaining an analytic form of the gradient is impractical. Therefore, in this paper the

performance of the SPSA algorithm will be examined in terms of parameter selection, data requirements, and convergence performance on this non-linear problem. Results will be reported on both a first-order "standard" implementation of SPSA and on a second-order version of SPSA that tends to enhance convergence.     

Sample moments of the autocorrelations of moving average processes and a modification to bartlett'sasymptotic variance formula

In this paper we express the sample autocorrelations for a moving average process of order q as a function of its own theoretical autocorrelations and the sample autocorrelations for the generating white noise series. Approximate analytic expressions are then obtained forthe moments of the sample autocorrelations of the moving average process.

Using these expressions, together with numerical evidence, we show that Bartlett's asymptotic formula for the variance of the sample autocorrelations of moving average processes, which is used widely in identifying these processes, is a large overestimate when considering finitesample sizes.

Our approach is for motivational purposes and so is purely formal, the amount of mathematics presented being kept to a minimum.     

Nonparametric Regression as an Example of Model Choice

Nonparametric regression can be considered as a problem of model choice. In this article, we present the results of a simulation study in which several nonparametric regression techniques including wavelets and kernel methods are compared with respect to their behavior on different test beds. We also include the taut-string method whose aim is not to minimize the distance of an estimator to some “true” generating function f but to provide a simple adequate approximation to the data. Test beds are situations where a “true” generating f exists and in this situation it is possible to compare the estimates of f with f itself. The measures of performance we use are the L²- and the L^∞-norms and the ability to identify peaks.     

Connecting child care quality to child outcomes: drawing policy lessons from nonexperimental data

Effective early childhood intervention and child care policies should be based on an understanding of the effects of child care quality and type on child well-being. This article describes methods for securing unbiased estimates of these effects from nonexperimental data. It focuses on longitudinal studies like the one developed by the National Institute of Child Health and Human Development's Early Child Care Research Network. This article first describes bias problems that arise in analyses of nonexperimental data and then explains strategies for controlling for biases arising from parental selection of child care. Next, it comments on attrition in longitudinal studies and outlines some strategies for addressing possible attrition bias. Finally, it discusses the need to translate "effect sizes" derived from these studies into the kinds of cost and benefit information needed by policy makers.