Chance-Corrected Measures of Reliability and Validity in 2 × 2 Tables

It is customary to use two groups of indices to evaluate a diagnostic method with a binary outcome: validity indices with a standard rater (sensitivity, specificity, and positive or negative predictive values) and reliability indices (positive, negative and overall agreements) without a standard rater. However neither of these classic indices is chance-corrected, and this may distort the analysis of the problem (especially in comparative studies). One way of chance-correcting these indices is by using the Delta model (an alternative to the Kappa model), but this means having to use a computer program to work out the calculations. This paper gives an asymptotic version of the Delta model, thus allowing simple expressions to be obtained for the estimator of each of the above-mentioned chance-corrected indices (as well as for its standard error).     

A Two-Latent-Class Model for Smoking Cessation Data with Informative Dropouts

Non ignorable missing data is a common problem in longitudinal studies. Latent class models are attractive for simplifying the modeling of missing data when the data are subject to either a monotone or intermittent missing data pattern. In our study, we propose a new two-latent-class model for categorical data with informative dropouts, dividing the observed data into two latent classes; one class in which the outcomes are deterministic and a second one in which the outcomes can be modeled using logistic regression. In the model, the latent classes connect the longitudinal responses and the missingness process under the assumption of conditional independence. Parameters are estimated by the method of maximum likelihood estimation based on the above assumptions and the tetrachoric correlation between responses within the same subject. We compare the proposed method with the shared parameter model and the weighted GEE model using the areas under the ROC curves in the simulations and the application to the smoking cessation data set. The simulation results indicate that the proposed two-latent-class model performs well under different missing procedures. The application results show that our proposed method is better than the shared parameter model and the weighted GEE model.     

A Dynamic Latent Model for Poverty Measurement

The main purpose of this paper is the longitudinal analysis of the poverty phenomenon. By interpreting poverty as a latent variable, we are able to resort to the statistical methodology developed for latent structure analysis. In particular, we propose to use the mixture latent Markov model which allows us to achieve two goals: (i) a time-invariant classification of households into homogenous groups, representing different levels of poverty; (ii) the dynamic analysis of the poverty phenomenon which highlights the distinction between transitory and permanent poverty situations. Furthermore, we exploit the flexibility provided by the model in order to achieve the measurement of poverty in a multidisciplinary framework, using several socio-economic indicators as covariates and identifying the main relevant factors which influence permanent and transitory poverty. The analysis of the longitudinal data of the Survey on Households Income and Wealth of the Bank of Italy provides the identification of two groups of households which are characterized by different dynamic features. Moreover, the inclusion of socio-economic covariates such as level of education, employment status, geographical area and residence size of the household head shows a direct association with permanent poverty.     

Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling

Fuzzy rule–based models, a key element in soft computing (SC), have arisen as an alternative for time series analysis and modeling. One difference with preexisting models is their interpretability in terms of human language. Their interactions with other components have also contributed to a huge development in their identification and estimation procedures. In this article, we present fuzzy rule–based models, their links with some regime-switching autoregressive models, and how the use of soft computing concepts can help the practitioner to solve and gain a deeper insight into a given problem. An example on a realized volatility series is presented to show the forecasting abilities of a fuzzy rule–based model.     

Depression and Missed Work among Informal Caregivers of Older Individuals with Dementia

Although providing informal care can negatively affect caregiver emotional health and lead to depression, the association between caregiver depression and missed work is unknown. We use data from the National Longitudinal Caregiver Survey to examine the relationship between caregiver depression and missed work among informal caregivers for older veterans with Alzheimer’s disease or vascular dementia. Two-part models are used to estimate the expected hours of work missed among working caregivers. Caregiver depression is statistically significantly associated with the likelihood and amount of time missed at work, resulting in an average of an extra half-day of work missed per month. While the effect of caregiver depression on work missed is modest, it represents another possible benefit from interventions to support caregivers.     

Manipulability, unanimity, anonymity and merging functions

A merging (or assessment aggregation) function (see [7]) is a rule that synthesizes several individual assessments, or numerical judgements, by creating a unique “collective” assessment. Individual i can manipulate assessment aggregation if the change in the merged assessment due to a change in i's assessment from x to y depends solely on x and y. Some assumptions on the functional form capturing this dependence are put forward and their effects on aggregation investigated under unanimity and anonymity conditions using a functional equation approach. Attention is restricted to three types of results: the merging function does not exist; it is the arithmetic mean; it is dictatorial. Received: 21 November 1997/Accepted: 31 May 1999     

Testing multivariate uniformity: The distance‐to‐boundary method

Given a random sample taken on a compact domain S ? ^d, the authors propose a new method for testing the hypothesis of uniformity of the underlying distribution. The test statistic is based on the distance of every observation to the boundary of S. The proposed test has a number of interesting properties. In particular, it is feasible and particularly suitable for high dimensional data; it is distribution free for a wide range of choices of 5; it can be adapted to the case that the support of S is unknown; and it also allows for one‐sided versions. Moreover, the results suggest that, in some cases, this procedure does not suffer from the well‐known curse of dimensionality. The authors study the properties of this test from both a theoretical and practical point of view. In particular, an extensive Monte Carlo simulation study allows them to compare their methods with some alternative procedures. They conclude that the proposed test provides quite a satisfactory balance between power, computational simplicity, and adaptability to different dimensions and supports.     

The new strategy for the concise presentation of sampling errors in the Italian Structural Business Statistics Survey

Reporting sampling errors of survey estimates is a problem that is commonly addressed when compiling a survey report. Because of the vast number of study variables or population characteristics and of interest domains in a survey, it is almost impossible to calculate and to publish the standard errors for each statistic. A way of overcoming such problem would be to estimate indirectly the sampling errors by using generalized variance functions, which define a statistical relationship between the sampling errors and the corresponding estimates. One of the problems with this approach is that the model specification has to be consistent with a roughly constant design effect. If the design effects vary greatly across estimates, as in the case of the Business Surveys, the prediction model is not correctly specified and the least-square estimation is biased. In this paper, we show an extension of the generalized variance functions, which address the above problems, which could be used in contexts similar to those encountered in Business Surveys. The proposed method has been applied to the Italian Structural Business Statistics Survey case.