Risk of Error and the Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

The accuracy of a binary diagnostic test is usually measured in terms of its sensitivity and its specificity, or through positive and negative predictive values. Another way to describe the validity of a binary diagnostic test is the risk of error and the kappa coefficient of the risk of error. The risk of error is the average loss that is caused when incorrectly classifying a non-diseased or a diseased patient, and the kappa coefficient of the risk of error is a measure of the agreement between the diagnostic test and the gold standard. In the presence of partial verification of the disease, the disease status of some patients is unknown, and therefore the evaluation of a diagnostic test cannot be carried out through the traditional method. In this paper, we have deduced the maximum likelihood estimators and variances of the risk of error and of the kappa coefficient of the risk of error in the presence of partial verification of the disease. Simulation experiments have been carried out to study the effect of the verification probabilities on the coverage of the confidence interval of the kappa coefficient.     

Post-Divorce Transfers and the Welfare of Mothers and Children in the United States

ABSTRACT In this paper we analyze the relationship between post-divorce private transfers (child support and alimony), mothers' economic independence and children's welfare using US data. Our empirical results show that mothers who receive child support and alimony from absent fathers are more likely to work and to spend a higher proportion of the income on child-related goods. We argue that the pattern of empirical results is consistent with there being significant externalities between parents even after divorce.     

The role of grandparenting in early childcare and child outcomes

This paper examines the short and medium term impact of early childcare provision by grandparents and formal care settings on child cognitive outcomes, using data from the Millennium Cohort Study (UK). Compared with children placed in formal childcare, children cared for by their grandparents are better at naming objects, but perform worse on tests of basic concept development and non-verbal reasoning. These results mask strong heterogeneities. On the one hand, the positive association between grandparental care and child outcomes is stronger for children from more advantaged households; on the other, the negative association is only significant for those from more disadvantaged households. The results of OLS estimations used for our analysis are confirmed using panel methods and an instrumental variable approach.     

Emotions into Disorder: Anxiety Disorders and the Social Meaning of Fear

This article explores what we refer to as norm-stimuli-state discrepancies, which are disparities between people's physical-emotional responses to emotional cues and the normative meanings of those cues. Drawing on forty qualitative interviews and participant observation research at support groups, we show that people with anxiety disorders describe two forms of norm-stimuli-state discrepancies. The first form involves discrepancies of type, in which people label fearful emotional states as deviant for being caused by the “wrong” stimuli. The second involves discrepancies of intensity, in which people label fearful states as deviant for involving feelings or displays of “too much” anxiety in response to an “appropriate” stimuli. The article further addresses the role of stimuli in prompting treatment seeking. Unexpected and intense emotional distress in combination with the falling away of external cues—which we refer to as “stimuli-less fear”—serve as a critical juncture on the path to an anxiety disorder diagnosis.     

Sequential Monte Carlo for rare event estimation

This paper discusses a novel strategy for simulating rare events and an associated Monte Carlo estimation of tail probabilities. Our method uses a system of interacting particles and exploits a Feynman-Kac representation of that system to analyze their fluctuations. Our precise analysis of the variance of a standard multilevel splitting algorithm reveals an opportunity for improvement. This leads to a novel method that relies on adaptive levels and produces, in the limit of an idealized version of the algorithm, estimates with optimal variance. The motivation for this theoretical work comes from problems occurring in watermarking and fingerprinting of digital contents, which represents a new field of applications of rare event simulation techniques. Some numerical results show performance close to the idealized version of our technique for these practical applications.     

An adaptive sequential Monte Carlo method for approximate Bayesian computation

Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCMC) implementations of ABC, the use of sequential Monte Carlo (SMC) methods has recently been suggested. Most effective SMC algorithms that are currently available for ABC have a computational complexity that is quadratic in the number of Monte Carlo samples (Beaumont et al., Biometrika 86:983?C990, 2009; Peters et al., Technical report, 2008; Toni et al., J.?Roy. Soc. Interface 6:187?C202, 2009) and require the careful choice of simulation parameters. In this article an adaptive SMC algorithm is proposed which admits a computational complexity that is linear in the number of samples and adaptively determines the simulation parameters. We demonstrate our algorithm on a toy example and on a birth-death-mutation model arising in epidemiology.