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Hierarchical relationships among manifest variables can be detected by analyzing their correlation matrix. To pinpoint the hierarchy underlying a multidimensional phenomenon, the Ultrametric Correlation Model (UCM) has been proposed with the aim of reconstructing a nonnegative correlation matrix via an ultrametric one. In this paper, we illustrate the mathematical advantages that a simple structure induced by the ultrametric property entails for the estimation of the UCM parameters in a maximum likelihood framework.

Given a sample of unlabeled observations, the goal of a novelty detection method is to identify which units substantially deviate from the observed labeled patterns. Therefore, in a model-based framework, it is firstly of paramount importance to learn the components that correspond to the manifest groups in the training set. Secondly, one needs to take into account the lack of knowledge regarding the statistical novelties. Thirdly, contaminated elements in the known classes could greatly jeopardize the identification of new groups. Motivated by these challenges, we propose a two-stage Bayesian non-parametric novelty detector. At stage one, robust estimates are extracted from the training set and, subsequently, such information is employed to elicit informative priors within a flexible semiparametric mixture. This general paradigm can be easily adapted to complex modeling frameworks: we provide here an application to functional data from a food authenticity study.

In this paper, a new approach to the fuzzy analysis of multidimensional material deprivation data is provided, based on partial order theory. The main feature of the methodology is that the information needed for the deprivation assessment is extracted directly from the relational structure of the dataset, avoiding any kind of scaling and aggregation procedure, so as to respect the ordinal nature of the data. An example based on real data is worked out, pertaining to material deprivation in Italy for the year 2004

During the last 40 years conjoint analysis has been used to solve a wide variety of concerns in market research. Recently, a number of studies have begun to use conjoint analysis for the economic valuation of non-market goods. This paper discusses how to extend the conjoint analysis area of application by introducing a coefficient to measure economic re-evaluation on the basis of utility scores and the relative importances of attributes provided by conjoint analysis. We utilise the suggested coefficient for the economic valuation of a typical non-market good, such as a worldwide cultural event, to reveal the trade offs between its attributes in terms of revenue variation. Our findings indicate the most valuable change to be made to the existing status quo to generate economic surplus

In the context of statistical controlled calibration we introduce the "multilevel calibration estimator" in order to account for clustered measurements. To tackle this issue more closely, results from a simulation study will be extensively discussed. Finally, an application from a building industry will be presented.

We introduce a model for categorical panel data which is tailored to the dynamic evaluation of the impact of job training programs. The model may be seen as an extension of the dynamic logit model in which unobserved heterogeneity between subjects is taken into account by the introduction of a discrete latent variable. For the estimation of the model parameters we use an EM algorithm and we compute standard errors on the basis of the numerical derivative of the score vector of the complete data log-likelihood. The approach is illustrated through the analysis of a dataset containing the work histories of the employees of the private firms of the province of Milan between 2003 and 2005, some of whom attended job training programs supported by the European Social Fund

In the nineties, numerous authors proposed the use of Multilevel Models in effectiveness studies. However, this approach has been strongly criticized. Cluster-Weighted Modeling (CWM) is a flexible statistical framework, which is based on weighted combinations of local models. While Multilevel Models provide rankings of the institutions, in the CWM approach many models of effectiveness are estimated, each of them being valid for a certain subpopulation of users

We propose two different measures of the dental care industry: (a) BALG matrix, a new instrument to measure patient loyalty and its extent; (b) SERVQUAL based approach to measure patient satisfaction. Further investigation concerns the link between patient satisfaction and loyalty. The results prove that patient loyalty in the dental care industry is similar to consumer behaviour in all the other B2B and B2C services and furthermore, the results highlight low dependency of patient satisfaction on loyalty, We propose two differentmeasures of the dental care industry: (a) BALG matrix, a new instrument to measure patient loyalty and its extent; (b) SERVQUAL based approach to measure patient satisfaction. Further investigation concerns the link between patient satisfaction and loyalty. The results prove that patient loyalty in the dental care industry is similar to consumer behaviour in all the other B2B and B2C services and furthermore, the results highlight low dependency of patient satisfaction on loyalty. © Springer-Verlag Berlin Heidelberg 2011.

We introduce the control sample in survey sampling as a tool for measuring the association between a possible cause X and a particular effect Y. The elements of the target population are divided in two groups according to whether X is present or not, the absence of X identifies the control group. A random sample is selected from each group with the aim of measuring the association between X and Y. We propose an unbiased estimator of the associational risk difference between groups and then generalize our approach to the problem of estimating the causal risk difference

In many real datasets, the same variables are measured on objects from different groups, and the covariance structure may vary from group to group. Oftentimes, the underlying population covariance matrices are not identical, yet they still have a common basic structure, e.g. there exists some rotation that diagonalizes simultaneously all covariance matrices in the groups, or all covariances can be made congruent by some translation and/or dilation. The purpose of this paper is to show how a test of homoscedasticity can be made more informative by performing a separate check for equality between shapes and equality between orientations of the concentration ellipsoids. This approach, combined with parsimonious parametrization in mixture data modeling, provides a formal hypothesis testing procedure for model assessment

Latent segmentation procedures are usually aimed at detecting the heterogeneity of statisitical units with reference to a system of relationships described by a structural equation model with latent variables (SEM-LV). After a short review of hte main proposals given in the literature, we present a descriptive two-step procedure, wich first considers either an a-priori or a post-hoc segmentation approach: the former is based upon the existence of proper classification variables; the latter defines as classificaiton variables the latent scores estimated by a global SEM-LV model. In the second step a cluster analysis is performed by using the previously defined grouping vairables, in order to improve the classification by means of a cluster-wise Partial Least Squares algorithm; the classification refinement is executed with reference both to the SEM inner and outer models.