Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Dependent masking and system life data analysis: Bayesian inference for two-component systems

Data from field operations of a system is often used to estimate the reliability of components. Under ideal circumstances, this system field data contains the time to failure along with information on the exact component responsible for the system failure. However, in many cases, the exact component causing the failure of the system cannot be identified, and is considered to be masked. Previously developed models for estimation of component reliability from masked system life data have been based upon the assumption that masking occurs independently of the true cause of system failure. In this paper we develop a Bayesian methodology for estimating component reliabilities from masked system life data when the probability of masking is dependent upon the true cause of system failure. The Bayesian approach is illustrated for the case of a two-component system of exponentially distributed components.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Some Cautions on the Measurement of User Information Satisfaction*

User information satisfaction (UIS) is important because of its potential effects on MIS department goals, quality of user work life, and extent of voluntary usage of systems. Reliable measurement of UIS is important for providing evaluative information for both researchers and practitioners. This study used 92 managers and executives as subjects to compare the test/retest reliability of a widely used, 13-scale UIS instrument together with four summary questions under experimental and control conditions. The summary questions behaved more reliably than the detailed questions for all groups, perhaps because of problems with scale units and origins and with item heterogeneity. This suggests that researchers need more reliable measures of UIS and that practitioners need to exercise caution when collecting and interpreting UIS scores.     

The relationship between Black and gay community involvement and HIV-related risk behaviors among Black men who have sex with men

Black gay men must navigate identities and stigmas related to being gay and Black, and report higher HIV incidence relative to their White male counterparts although they report lower rates of drug use and risky sexual behaviors. This study examined whether closeness to the gay or Black community correlated with HIV-related risk and protective behaviors. Data were drawn from uConnect, a population-based cohort study of young Black men who have sex with men (YBMSM) on Chicago's South Side. The sample consists of 618 Black MSM ranging in age from 16 to 29. Cross-sectional measures for this study include Black and gay community closeness, drug use, sexual risk behaviors, HIV testing, and health promotion behaviors. Closeness with the gay community was associated with greater pre-exposure prophylaxis (PrEP) knowledge as well as participating in health promotion programs along with higher rates of transactional sex and having sex under the influence of substances, and increased odds of self-reported HIV seropositivity. Involvement in the Black community was associated with lower odds of reporting being HIV positive. Findings suggest that programs and initiatives are needed to help promote the positive aspects of the Black and gay communities, while minimizing the negative correlates of such involvements.     

“It’s a generosity loop”: Religious and spiritual motivations of volunteers who glean produce to reduce food insecurity

Food insecurity is a common social issue and households look to assistance from food pantries, which often have difficulty supplying fresh or nutritious food. A substantial amount of produce is left in the fields, which can be collected through gleaning. In a focus group with volunteer gleaners at a faith-based nonprofit organization that attempts to fill the gaps of food insecurity, the motivations, challenges, and benefits of volunteering were examined. Volunteering in gleaning generated a sense of collective purpose and community; it addressed deeply held religious and spiritual impulses; and it puts volunteers directly in contact with nature.     

The Network Survival Method for Estimating Adult Mortality: Evidence From a Survey Experiment in Rwanda

Adult death rates are a critical indicator of population health and well-being. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastructure and must rely on estimates that are often problematic. In this article, we introduce the network survival method, a new approach for estimating adult death rates. We derive the precise conditions under which it produces consistent and unbiased estimates. Further, we develop an analytical framework for sensitivity analysis. To assess the performance of the network survival method in a realistic setting, we conducted a nationally representative survey experiment in Rwanda (n = 4,669). Network survival estimates were similar to estimates from other methods, even though the network survival estimates were made with substantially smaller samples and are based entirely on data from Rwanda, with no need for model life tables or pooling of data from other countries. Our analytic results demonstrate that the network survival method has attractive properties, and our empirical results show that this method can be used in countries where reliable estimates of adult death rates are sorely needed.     

A Psychometric Comparison of Three Scales and a Single-Item Measure to Assess Sexual Satisfaction

This study was designed to systematically compare and contrast the psychometric properties of three scales developed to measure sexual satisfaction and a single-item measure of sexual satisfaction. The Index of Sexual Satisfaction (ISS), Global Measure of Sexual Satisfaction (GMSEX), and the New Sexual Satisfaction Scale–Short (NSSS-S) were compared to one another and to a single-item measure of sexual satisfaction. Conceptualization of the constructs, distribution of scores, internal consistency, convergent validity, test-retest reliability, and factor structure were compared between the measures. A total of 211 men and 214 women completed the scales and a measure of relationship satisfaction, with 33% (n = 139) of the sample reassessed two months later. All scales demonstrated appropriate distribution of scores and adequate internal consistency. The GMSEX, NSSS-S, and the single-item measure demonstrated convergent validity. Test-retest reliability was demonstrated by the ISS, GMSEX, and NSSS-S, but not the single-item measure. Taken together, the GMSEX received the strongest psychometric support in this sample for a unidimensional measure of sexual satisfaction and the NSSS-S received the strongest psychometric support in this sample for a bidimensional measure of sexual satisfaction.