A general approximation to quantiles

For many continuous distributions, a closed-form expression for their quantiles does not exist. Numerical approximations for their quantiles are developed on a distribution-by-distribution basis. This work develops a general approximation for quantiles using the Taylor expansion. Our method only requires that the distribution has a continuous probability density function and its derivatives can be derived to a certain order (usually 3 or 4). We demonstrate our unified approach by approximating the quantiles of the normal, exponential, and chi-square distributions. The approximation works well for these distributions.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.     

Restructuring of the Financial Industry: The Disappearance of Locally Owned Traditional Financial Services in Rural America

Restructuring in the financial services industry has altered the relationship between small business owners and capital. In the past small businesses have relied on relational, or soft data, lending from locally owned banks for capital. The proliferation of absentee‐owned local branch networks brought standardized practices, thus eliminating the autonomy of local loan officers to utilize soft data in loan decisions. In this article we examine the changes in the percentage of traditional financial services that are locally owned in three county types: metropolitan, micropolitan, and noncore. We utilize the Longitudinal Business Database at the U.S. Census Bureau Center for Economic Studies. We examine changes in local ownership of traditional financial services between 1976 and 2007. We find that the rate of decline of local ownership has been greatest in the noncore (most rural) counties. We also explore to what extent these patterns are related to the emergence of alternative financial services during the same period. We find that such alternative services are growing in all three county types, but at rates not significantly different than the population growth for these county types. We supplement our analysis with data from qualitative interviews with small business owners throughout rural Texas. We conclude with a discussion of implications and plans for future research.     

How Latinos Are Transforming the United States: Research, Theory, and Policy

The United States is entering a new era, a period marked by some important demographic changes in the composition of the population, most especially significant increases in the Latino and Latino immigrant segments of society. These population shifts require corresponding interpersonal, organizational, and structural changes. The present issue bridges research and theory across disciplines and includes studies incorporating a variety of methodologies to examine these important areas. These articles begin to fill some of the voids where a systematic and robust corpus of knowledge is lacking. The contributions address topics ranging from issues of identity and interpersonal relations to pressing matters of educational significance to general approaches to navigating the cultural transitions that mark fluid transnational adaptations. Finally, each contribution delineates the policy implications resulting from the processes and literatures that are examined.     

Activation and social inclusion: challenges and possibilities

The last two decades have seen an increasing focus on the activation of individuals receiving welfare benefits. This has entailed an attempt to transform employment assistance and social security systems to make them more employment‐friendly through the development of explicit linkages between social protection policies, labour market participation and labour market programs. At the same time there has been a growing interest in the concept of social inclusion, and social inclusion has been a commonly stated goal of activation programs. However, despite some synergies between these two approaches, in other respects it is not clear that they are always compatible. This paper explores the extent to which activation programs are central to the social inclusion approach and the similarities and differences between these approaches. It then briefly examines the evidence regarding the ability of activation programs to enhance social inclusion.     

Dynamic reasoning and time pressure: Transition from analytical operations to experiential responses

Based upon the Decision Field Theory (Busemeyer and Townsend 1993), we tested a model of dynamic reasoning to predict the effect of time pressure on analytical and experiential processing during decision-making. Forty-six participants were required to make investment decisions under four levels of time pressure. In each decision, participants were presented with experiential cues which were either congruent or incongruent with the analytical information. The congruent/incongruent conditions allowed us to examine how many decisions were based upon the experiential versus the analytical information, and to see if this was affected by the varying degrees of time pressure. As expected, the overall accuracy was reduced with greater time pressure and accuracy was higher when the experiential and analytical cues were congruent than when they were incongruent. Of great interest was the data showing that under high time pressure participants used more experiential cues than at other time pressures. We suggest that the dynamic reasoning paradigm has some future potential for predicting the effects of experiential biases in general, and specifically under time pressure.     

The Role of Sexual Communication in Couples’ Sexual Outcomes: A Dyadic Path Analysis

In a study of 142 couples, we gathered survey data to show how sexual communication influences sexual and relationship satisfaction as well as sexual and orgasm frequency. In two dyadic data path analyses, we observed the significant paths of influence that sexual communication has on sexual and relationship satisfaction, as well as sexual and orgasm frequency. Our findings revealed greater amounts of sexual communication were associated with increased orgasm frequency in women and greater relationship and sexual satisfaction in both sexes. We also observed important differences in the associations of sexual communication and general communication on satisfaction levels. With these analyses, we expand the current literature to broaden our understanding of the role that sexual communication plays in committed relationships.     

The Distribution of Returns to Education for People with Disabilities

This article takes a first look at the distribution of returns to education for people with disabilities, a particularly disadvantaged group whose labor market performances have not been well studied or documented. Using a nonparametric approach, we uncover significant heterogeneity in the returns to education for these workers, which is drastically masked by conventional parametric methods. Based on these estimates, we construct the Sharpe ratio of human capital investment (taking into account its substantial risk), and our results corroborate the claimed importance of human capital in improving these workers’ wages. Our stochastic dominance tests show that the returns to education for workers with disabilities, as a group, may have been affected more adversely in the most recent recession, relative to their non-disabled counterparts.