Partial cell suppression: A new methodology for statistical disclosure control

In this paper we address the problem of protecting confidentiality in statistical tables containing sensitive information that cannot be disseminated. This is an issue of primary importance in practice. Cell Suppression is a widely-used technique for avoiding disclosure of sensitive information, which consists in suppressing all sensitive table entries along with a certain number of other entries, called complementary suppressions. Determining a pattern of complementary suppressions that minimizes the overall loss of information results into a difficult (i.e., -hard) optimization problem known as the Cell Suppression Problem. We propose here a different protection methodology consisting of replacing some table entries by appropriate intervals containing the actual value of the unpublished cells. We call this methodology Partial Cell Suppression, as opposed to the classical complete cell suppression. Partial cell suppression has the important advantage of reducing the overall information loss needed to protect the sensitive information. Also, the new method provides automatically auditing ranges for each unpublished cell, thus saving an often time-consuming task to the statistical office while increasing the information explicitly provided with the table. Moreover, we propose an efficient (i.e., polynomial-time) algorithm to find an optimal partial suppression solution. A preliminary computational comparison between partial and complete suppression methologies is reported, showing the advantages of the new approach. Finally, we address possible extensions leading to a unified complete/partial cell suppression framework.     

Evaluation of what parents know about their children's drug use and how they perceive the most common family risk factors

Research on family risk factors for addictive behaviors in young people has not paid a great deal of attention to parents' knowledge of their children's addictive behaviors and of the family risk factors that affect such behaviors. The aim of this work is to compare knowledge about these two aspects in two groups of parents that differ regarding their children's declared drug use. The research was carried out with a sample of 309 schoolchildren and their families, divided into two groups: one of 154 families with children defined as non-Drug Users and another of 155 families with children defined as Drug Users. The results show that parents with children who consume drugs tend to strongly underestimate their children's use. Moreover, they overestimate the control they exercise over them and appear to be more familiarized with the use of drugs, if we compare them with parents of non-Drug Users. It is suggested that these results may be relevant for the planning of strategies that increase motivation in families with drug-risk children to participate in prevention programs.     

The business environment in the 1980s

Dr. Harold Linstone, the editor of ‘Technological Forecasting and Social Change’ holds that too often the future is too much discounted. This seems particularly true in the case of business corporations, which tend to pay lip service to long-range planning—especially when prospective studies point to threats rather than to opportunities.The author believes that truly long-range planning will rarely be operative until business corporations muster the political will to jointly write a scenario of their (interlinked) future, and to act accordingly. Illustratively and speculatively, such a scenario might read as follows:     

Sampling hierarchies of discrete random structures

Statistics and Computing - Hierarchical normalized discrete random measures identify a general class of priors that is suited to flexibly learn how the distribution of a response variable changes...     

Optimal classification of Gaussian processes in homo- and heteroscedastic settings

A procedure to derive optimal discrimination rules is formulated for binary functional classification problems in which the instances available for induction are characterized by random trajectories sampled from different Gaussian processes, depending on the class label. Specifically, these optimal rules are derived as the asymptotic form of the quadratic discriminant for the discretely monitored trajectories in the limit that the set of monitoring points becomes dense in the interval on which the processes are defined. The main goal of this work is to provide a detailed analysis of such optimal rules in the dense monitoring limit, with a particular focus on elucidating the mechanisms by which near-perfect classification arises. In the general case, the quadratic discriminant includes terms that are singular in this limit. If such singularities do not cancel out, one obtains near-perfect classification, which means that the error approaches zero asymptotically, for infinite sample sizes. This singular limit is a consequence of the orthogonality of the probability measures associated with the stochastic processes from which the trajectories are sampled. As a further novel result of this analysis, we formulate rules to determine whether two Gaussian processes are equivalent or mutually singular (orthogonal).

     

Methodological challenges of evaluating the impact of the Global Environment Facility's biodiversity program

In this paper, we explore some of the methodological challenges that evaluators face in assessing the impacts of complex intervention strategies. We illustrate these challenges, using the specific example of an impact evaluation of one of the six focal areas of the Global Environment Facility; its biodiversity program. The discussion is structured around the concepts of attribution and aggregation, offering the reader a framework for reflection. Subsequently, the paper discusses how theory-based evaluation can provide a basis for addressing the attribution and aggregation challenges presented.     

A stochastic recurrence equations approach for score driven correlation models

We describe stationarity and ergodicity (SE) regions for a recently proposed class of score driven dynamic correlation models. These models have important applications in empirical work. The regions are derived from sufficiency conditions in Bougerol (1993 Bougerol, P. (1993). Kalman filtering with random coefficients and contractions. SIAM Journal on Control and Optimization 31(4):942–959.[Crossref], [Web of Science ®] , [Google Scholar]) and take a nonstandard form. We show that the nonstandard shape of the sufficiency regions cannot be avoided by reparameterizing the model or by rescaling the score steps in the transition equation for the correlation parameter. This makes the result markedly different from the volatility case. Observationally equivalent decompositions of the stochastic recurrence equation yield regions with different shapes and sizes. We use these results to establish the consistency and asymptotic normality of the maximum likelihood estimator. We illustrate our results with an analysis of time-varying correlations between U.K. and Greek equity indices. We find that also in empirical applications different decompositions can give rise to different conclusions regarding the stability of the estimated model.     

Objective Bayesian analysis for the multivariate skew-t model

We propose a novel Bayesian analysis of the p-variate skew-t model, providing a new parameterization, a set of non-informative priors and a sampler specifically designed to explore the posterior density of the model parameters. Extensions, such as the multivariate regression model with skewed errors and the stochastic frontiers model, are easily accommodated. A novelty introduced in the paper is given by the extension of the bivariate skew-normal model given in Liseo and Parisi (2013) to a more realistic p-variate skew-t model. We also introduce the R package mvst, which produces a posterior sample for the parameters of a multivariate skew-t model.     

Macroeconomic stabilization of primary commodities price cycles in developing economies

This paper proposes a strategy for stabilizing macroeconomic policy to address jointly the effects of changes in the prices of food, minerals and energy (oil). Our approach differs from the general literature, which analyzes the effects of a commodity boom or bust and therefore the solutions in terms of economic policy separately, that is, by type of commodity. The stabilization strategy that we propose considers a key fact affecting many open economies, namely, that they not only are affected by increases or decreases in commodity prices, but also benefit from them. Consequently, we use a structural model for an open economy with restricted households to show that welfare could be improved with a fiscal rule incorporating transfers to stabilize household consumption. This strategy noticeably dominates an aggressive monetary policy focused only on stabilizing inflation and a fiscal policy that has an excessive bias toward saving income from exports.