A Proposal for a Model-Based Composite Indicator: Experience on Perceived Discrimination in Europe

Social Indicators Research - The multidimensional poverty index (MPI) is generally credited for better capturing the various components of poverty. Where such indexes have a spatial component,...     

On the predictive performance of a non-optimal action in hypothesis testing

Statistical Methods & Applications - In Bayesian decision theory, the performance of an action is measured by its posterior expected loss. In some cases it may be convenient/necessary to use a...     

The use of predicted values for item parameters in item response theory models: an application in intelligence tests

In testing, item response theory models are widely used in order to estimate item parameters and individual abilities. However, even unidimensional models require a considerable sample size so that all parameters can be estimated precisely. The introduction of empirical prior information about candidates and items might reduce the number of candidates needed for parameter estimation. Using data for IQ measurement, this work shows how empirical information about items can be used effectively for item calibration and in adaptive testing. First, we propose multivariate regression trees to predict the item parameters based on a set of covariates related to the item-solving process. Afterwards, we compare the item parameter estimation when tree-fitted values are included in the estimation or when they are ignored. Model estimation is fully Bayesian, and is conducted via Markov chain Monte Carlo methods. The results are two-fold: (a) in item calibration, it is shown that the introduction of prior information is effective with short test lengths and small sample sizes and (b) in adaptive testing, it is demonstrated that the use of the tree-fitted values instead of the estimated parameters leads to a moderate increase in the test length, but provides a considerable saving of resources.     

Unemployment Duration of Spouses: Evidence From France

This paper analyses the conditional probability of leaving unemployment of French married individuals from 1991 to 2002. We find that the effect of spousal labor income on unemployment duration is asymmetric for men and women. In particular, the probability of men to find a job is increasing in wife's labor income, while it is decreasing in husband's earnings for women. To adjust for endogenous selection into marriage, we use the quarter of birth as an instrumental variable for the spousal wage. Finally, we show that introducing a breadwinner stigma in a joint job search model generates the positive correlation observed for men in the data.     

Two-stage adaptive cluster sampling

Adaptive cluster sampling is usually applied when estimating the abundance of elusive, clustered biological populations. It is commonly supposed that all individuals in the selected area units are detected by the observer, but in many acutal situations this assumption may be highly unrealistic and some individuals may be missed. This paper deals with the problem of handling imperfect detectability in adaptive cluster sampling by using a pure design-based approach. A two-stage adaptive procedure is proposed where the abundance in the selected units is estimated by replicated counts.     

Multivariate linear regression with non-normal errors: a solution based on mixture models

In some situations, the distribution of the error terms of a multivariate linear regression model may depart from normality. This problem has been addressed, for example, by specifying a different parametric distribution family for the error terms, such as multivariate skewed and/or heavy-tailed distributions. A new solution is proposed, which is obtained by modelling the error term distribution through a finite mixture of multi-dimensional Gaussian components. The multivariate linear regression model is studied under this assumption. Identifiability conditions are proved and maximum likelihood estimation of the model parameters is performed using the EM algorithm. The number of mixture components is chosen through model selection criteria; when this number is equal to one, the proposal results in the classical approach. The performances of the proposed approach are evaluated through Monte Carlo experiments and compared to the ones of other approaches. In conclusion, the results obtained from the analysis of a real dataset are presented.     

Female sterilizing operations in New South Wales: A demographic perspective

This paper examines trends in female sterilizing operations from a demographic perspective. These operations have declined in New South Wales since 1981, with a substantial drop in tubal ligation and hysterectomy, particularly among younger women. The decline in sterilization of women of childbearing age has been due to postponement of births. Younger women have avoided terminal methods of birth control and continued to use methods, such as oral contraceptives and back-up abortion, which allow for a pregnancy at a later age. Sterilizing operations still remain the most commonly reported means of birth control by women over age 35.     

Predictive control of posterior robustness for sample size choice in a Bernoulli model

In this article we consider the sample size determination problem in the context of robust Bayesian parameter estimation of the Bernoulli model. Following a robust approach, we consider classes of conjugate Beta prior distributions for the unknown parameter. We assume that inference is robust if posterior quantities of interest (such as point estimates and limits of credible intervals) do not change too much as the prior varies in the selected classes of priors. For the sample size problem, we consider criteria based on predictive distributions of lower bound, upper bound and range of the posterior quantity of interest. The sample size is selected so that, before observing the data, one is confident to observe a small value for the posterior range and, depending on design goals, a large (small) value of the lower (upper) bound of the quantity of interest. We also discuss relationships with and comparison to non robust and non informative Bayesian methods.