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.     

Drama therapy in diagnosis and treatment

The author examines the use of drama therapy as a therapeutic and diagnostic tool that can help provide insights into personality and character and aid in the exploration of conflicts of children in therapy.     

PARENTAL DEATH AS A PRECIPITANT OF MARITAL CONFLICT IN MIDDLE AGE*

When a long-standing marriage suddenly becomes conflictual, one precipitant can be the recent death of the parent of one of the partners. The bereaved spouse can identify with the deceased parent, attacking or withdrawing from his or her partner; or he/she can become irrationally angry because the partner cannot replace an idealized parent. The main goal of therapy is to facilitate the bereaved partner's mourning of the dead parent. Choosing an effective therapeutic method depends on the couple's capacity for mutual empathy and support and their need for insight. Without marital therapy, unrecognized mourning may contribute to the breakdown of long-standing marriages.     

Communicating uncertainty in national security intelligence: Expert and nonexpert interpretations of and preferences for verbal and numeric formats

Organizations in several domains including national security intelligence communicate judgments under uncertainty using verbal probabilities (e.g., likely) instead of numeric probabilities (e.g., 75% chance), despite research indicating that the former have variable meanings across individuals. In the intelligence domain, uncertainty is also communicated using terms such as low, moderate, or high to describe the analyst's confidence level. However, little research has examined how intelligence professionals interpret these terms and whether they prefer them to numeric uncertainty quantifiers. In two experiments (N = 481 and 624, respectively), uncertainty communication preferences of expert (n = 41 intelligence analysts in Experiment 1) and nonexpert intelligence consumers were elicited. We examined which format participants judged to be more informative and simpler to process. We further tested whether participants treated verbal probability and confidence terms as independent constructs and whether participants provided coherent numeric probability translations of verbal probabilities. Results showed that although most nonexperts favored the numeric format, experts were about equally split, and most participants in both samples regarded the numeric format as more informative. Experts and nonexperts consistently conflated probability and confidence. For instance, confidence intervals inferred from verbal confidence terms had a greater effect on the location of the estimate than the width of the estimate, contrary to normative expectation. Approximately one-fourth of experts and over one-half of nonexperts provided incoherent numeric probability translations for the terms likely and unlikely when the elicitation of best estimates and lower and upper bounds were briefly spaced by intervening tasks.     

Child and Family Outcomes in New York and Tel AVIV: Using Social Indicators in a City Level Comparative Analysis

This article presents a comparative study in which social indicators were employed as a means to examine differences in living conditions and family and children outcomes on a local level. The study obtained household-level data on the well-being of children and families in two cities: New York (NYC) and Tel Aviv (TLV). Data were collected using computer assisted telephone interview (CATI) technology and random digit dialing (RDD). Telephone interviews were conducted with the randomly selected adults in English, Spanish and Chinese in NYC and in Hebrew in TLV. The study reported here documented differences in family and child well-being between the two cities. It further documented that family size and caregiver level of education play a similar role in both cities and their importance in regard to child and family outcomes. The significant differences found in adults’ and especially children’s outcomes were analyzed by the caregiver’s level of education and further support the need for policies that alleviate the burden of less educated caregivers and aim to improve the well-being of them and their families. The study demonstrates the relevance of social indicators at the local level, not only for measuring outcomes among specific populations, but also in regard to their possible implications for social policies, a most timely task in an era of social services devolution.     

The potential of child care tax credits to reduce poverty and welfare recipiency

As more and more mothers of young children enter the work force, interest in government financing of child care grows. The chief government subsidy for child care is the child care credit in the federal Internal Revenue Code. This is a nonrefundable credit and therefore provides benefits only to those with incomes high enough to require them to pay income tax. Yet of the $ 5.5 billion spent by the federal government on child care in 1986, this program accounted for $ 3.5 billion.This paper simulates the effects of expanding the child care tax credit by (1) doubling the reimbursement rates of the current credit; (2) making the credit refundable; and (3) both making the credit refundable and increasing its value for all families with income below $ 32,000.Results suggest that these changes will have modest effects on the income and earnings of mothers, and on the poverty gap and welfare recipiency. Costs, however, differ substantially. Doubling the value of the credit is far more expensive than either making the credit refundable or making it both refundable and more generous at the bottom of the income distribution. Making the credit refundable may cost taxpayers very little by leading to increases in hours worked and concomitant reductions in welfare payments.     

A bayesian approach to a reliability problem: Theory,analysis and interesting numerics

We consider approximate Bayesian inference about the quantity R = P[Y₂> Y₁] when both the random variables Y₁, Y₂ have expectations that depend on certain explanatory variables. Our interest centers on certain characteristics of the posterior of R under Jeffreys's prior, such as its mean, variance and percentiles. Since the posterior of R is not available in closed form, several approximation procedures are introduced, and their relative performance is assessed using two real datasets.