On the Local Sensitivity Analysis of the Inoperability Input‐Output Model

Natural and man‐made disasters are currently a source of major concern for contemporary societies. In order to understand their economic impacts, the inoperability input‐output model has recently gained recognition among scholars. In a recent paper, Percoco (2006) has proposed an extension of the model to map the technologically most important sectors through so‐called fields of influence. In the present note we aim to show that this importance measure also has a clear connection with local sensitivity analysis theory.     

An information theoretic approach to post randomization methods under differential privacy

Statistics and Computing - Post randomization methods are among the most popular disclosure limitation techniques for both categorical and continuous data. In the categorical case, given a...     

Signaling strength? An analysis of decision making in <Emphasis Type="Italic">The Weakest Link</Emphasis>

We analyze contestants’ behavior in the game show “The Weakest Link”. We focus on banking decisions, where a contestant chooses to secure an amount of money for the eventual winner, or to risk it on a general knowledge question. We find that contestants do not use the banking strategy that maximizes total expected prize money. Average earnings could be at least 17% higher. Our results suggest that contestants are not overconfident, but do try to convince other contestants that their ability is higher than it really is, in order to increase chances of winning the prize. We argue that this mechanism may also be applicable to other situations that are of economic interest.     

全球法与掠夺:法治的阴暗面

"法治"(rule of law),习惯上已被当作一个具有积极本质、政治中立的"工具",且效力普适,放之四海而皆准.作为Ugo Mattei与Laura Nadar教授合著的Plunder:When the Ruleof Law is Illegal (Blackwell Publishing,Oxford 2008)一书观点的综述,本文认为,作为一个歧义丛生的概念,"法治"具有光明与阴暗的两面性,而"法治"的阴暗面长期以来一直被排除在公众讨论的范围之外."法治"作为一个华丽辞藻,已被西方强势者所挟持,用来论证其(主要)对"发展中"国家的干涉及其最终演变成的掠夺(plunder)实践的合法性,并使西方经济势力对"非西方世界"(the rest)的扩张成为可能.基于此,我们得出了这样的结论:"法治"已被"非法地"(illegally)利用.目前国际金融机构--当今的全球立法者--在用"法治"观念包裹着其提供给"发展中世界"的各种改革方案的背后,隐藏着智识短视、种族中心主义和帝国主义立场."技术性"概念对"法"概念的偷换,美国法所独享的全球性支配地位以及当今西方跨国公司的帝国主义态度等作为其中的一些指标,向我们展示了殖民主义与当今新自由主义政策之间的沆瀣一气.     

Estimation of contingency tables in complex survey sampling using probabilistic expert systems

In this paper we explore the possibility to use a particular class of models, known as probabilistic expert systems, to define two classes of estimators of a contingency table in case of stratified sampling designs. The two classes are characterized by the different role of the sampling design: in the first, the sampling design is treated as an additional variable; in the second, it is used only for estimation purposes by means of the survey weights. The bias/variance trade off of these estimators is analyzed and the consequences of model misspecification are illustrated. Furthermore, it is shown that the Horvitz–Thompson estimator belongs to both classes of estimators. It comes out that the Horvitz–Thompson estimator is almost always inefficient but robust. Monte Carlo simulations illustrate the efficiency of the proposed estimators.     

Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms

Variable selection is one of the main problems faced by data mining and machine learning techniques. These techniques are often, more or less explicitly, based on some measure of variable importance. This paper considers Total Decrease in Node Impurity (TDNI) measures, a popular class of variable importance measures defined in the field of decision trees and tree-based ensemble methods, like Random Forests and Gradient Boosting Machines. In spite of their wide use, some measures of this class are known to be biased and some correction strategies have been proposed. The aim of this paper is twofold. Firstly, to investigate the source and the characteristics of bias in TDNI measures using the notions of informative and uninformative splits. Secondly, a bias-correction algorithm, recently proposed for the Gini measure in the context of classification, is extended to the entire class of TDNI measures and its performance is investigated in the regression framework using simulated and real data.     

A Clustering Method for Categorical Ordinal Data

Often, categorical ordinal data are clustered using a well-defined similarity measure for this kind of data and then using a clustering algorithm not specifically developed for them. The aim of this article is to introduce a new clustering method suitably planned for ordinal data. Objects are grouped using a multinomial model, a cluster tree and a pruning strategy. Two types of pruning are analyzed through simulations. The proposed method allows to overcome two typical problems of cluster analysis: the choice of the number of groups and the scale invariance.     

Multiple change points detection and clustering in dynamic networks

The increasing amount of data stored in the form of dynamic interactions between actors necessitates the use of methodologies to automatically extract relevant information. The interactions can be represented by dynamic networks in which most existing methods look for clusters of vertices to summarize the data. In this paper, a new framework is proposed in order to cluster the vertices while detecting change points in the intensities of the interactions. These change points are key in the understanding of the temporal interactions. The model used involves non-homogeneous Poisson point processes with cluster-dependent piecewise constant intensity functions and common discontinuity points. A variational expectation maximization algorithm is derived for inference. We show that the pruned exact linear time method, originally developed for change points detection in univariate time series, can be considered for the maximization step. This allows the detection of both the number of change points and their location. Experiments on artificial and real datasets are carried out, and the proposed approach is compared with related methods.     

Optimal predictive sample size for case–control studies

Summary.  The identification of factors that increase the chances of a certain disease is one of the classical and central issues in epidemiology. In this context, a typical measure of the association between a disease and risk factor is the odds ratio. We deal with design problems that arise for Bayesian inference on the odds ratio in the analysis of case–control studies. We consider sample size determination and allocation criteria for both interval estimation and hypothesis testing. These criteria are then employed to determine the sample size and proportions of units to be assigned to cases and controls for planning a study on the association between the incidence of a non-Hodgkin's lymphoma and exposition to pesticides by eliciting prior information from a previous study.     

Applications in business,medical and industrial statistics of bi-aspect nonparametric tests for location problems

Starting from the theory of the Nonparametric Combination of Dependent Permutation Tests (Pesarin, 1992, 2001), Marozzi (2002a, b) proposed two bi-aspect nonparametric tests for the two-sample and the multi-sample location problems. These tests are shown by simulation to be remarkably more powerful than the traditional parametric and permutation competitors (which can be seen as uni-aspect tests) under heavy-tailed and skewed distributions. After a brief presentation of the bi-aspect idea to location testing problems, three actual applications are discussed. The first one is a problem of business statistics and deals with the analysis of time for service calls. The second one is in medical statistics and deals with the analysis of the effect of cigarette smoking on maternal airway function during pregnancy. The third one is in industrial statistics and deals with the analysis of the setting of machines that produce steel ball bearings. The bi-aspect testing allows us to draw deeper and more informative inference than that allowed by traditional competitors.Marco Marozzi: Part of the research was done when the author was in Dipartimento di Scienze Statistiche, Universitá di Bologna, Italy.