Economics and Statistics

In this paper we propose an alternative approach to conjoint analysis (CA) based on the principal component analysis onto a reference sub-space (PCAR). Following this technique, the results of a CA are considerably enriched by some graphical interpretation tools and optimal synthesis of the individual estimated part-worth coe$cients are also obtained with respect to hierarchized judges sub-populations.

Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relations.
Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results.
The aim of this paper is to point out some issues that arise in these different phases, highlighting:
i) the use of local archive versus international bibliographic databases; ii) the use of different approaches for setting boundary in a whole-network; iii) the definition of coauthorship
data matrix (binary and weighted ties) and iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases, within an illustrative example on real data which refer to scientific collaboration among researchers affiliated with an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted coauthorship networks in different scientific fields.

Keywords Bibliographic data, Scientific Collaboration, Social Network Analysis, Weighted Networks

In the last decades the use of regression-like preference models has found widespread application in marketing research. The Conjoint Analysis models have even more been used to analyze consumer preferences and simulate product positioning. The typical data structure of this kind of models can be enriched by the presence of supplementary information observed on respondents. We suppose that relational data observed on pairs of consumers are available. In such a case, the existence of a consumer network is introduced in the Conjoint model as a set of contiguous constraints among the respondents. The proposed approach will allow to bring together the theoretical framework of Social Network Analysis with the explicative power of Conjoint Analysis models. The combined use of relational and choice data could be usefully exploited in the framework of relational and tribal marketing strategies.

Conjoint Analysis is one of the most widely used techniques in the assessment of the consumer's behaviors. This method allows to estimate the partial utility coefficients according to a statistical model linking the overall note of preference with the attribute levels describing the stimuli. Conjoint analysis results are useful in new-product positioning and market segmentation. In this paper a cluster-based segmentation strategy based on a new metric has been proposed. The introduced distance is based on a convex linear combination of two Euclidean distances em bedding information both on the estimated parameters and on the model fitting. Market segments can be then defined according to the proximity of the part-worth coecients and to the explicative power of the estimated models.

In this paper we propose to bring together some facets of the Response Surface Methodology used in Design of Experiments and the graphical displays arising from Multidimensional Data Analysis in order to enhance the interpretation of the traditional plots of the Factorial Techniques. By exploiting the peculiar interpretation of a response surface we describe a data-fitting based approach in order to analyse the relationships between an outer quantitative response variable and a set of principal axes. Moreover, we to use this interpretative-aid tool to represent by a surface response the information about the quality of the representation derived by a multidimensional data analysis.

Objective: The association between celiac disease (CD) and liver disease in pediatrics is widely recognized, but its prevalence is unknown. This study aims to conduct a systematic review and meta-analysis to evaluate the prevalence of CD in children with cryptogenic persistent hypertransaminasemia (HTS) or autoimmune hepatitis (AIH), and vice versa. Methods: We searched MEDLINE/PubMed, the Cochrane Library, Web of Science, and MD Consult from 1977 to May 2012 for studies reporting either CD and HTS or AIH. Pooled prevalences with 95% confidence intervals (CI) and relative risk (RR) were calculated. Results: Nine studies (2046 patients) were identified. Pooled prevalences of CD in children with mild, nonspecific cryptogenic persistent HTS and vice versa were 12.0% (95% CI 4.17-29.96) and 36.0% (95% CI 32.15-40.11), respectively. A gluten-free diet normalized transaminase levels in 77% to 100% of patients with CD within 4 to 8 months. Pooled prevalences of CD in children with AIH and vice versa were 6.3% (95% CI 3.87-11.73) and 1.4% (95% CI 0.84-2.15), respectively. The RR of HTS in children with CD versus the general population, and of CD in children with HTS was 6.55 (95% CI 5.65-7.60) and 11.59 (95% CI 3.80-35.33), respectively. The corresponding RR of AIH in children with CD was 188.54 (95% CI 92.23-385.43). The RR of CD in children with AIH was 6.63 (95% CI 3.86-11.40). Conclusions: CD is associated with elevated transaminase levels in about one-third of newly diagnosed children. Cryptogenic persistent HTS may signal gluten-dependent nonspecific mild hepatitis (12.0% of cases) or more rarely (6.3%) severe CD-related autoimmune hepatopathy. RRs confirm these trends in the considered associations.

Multiplex networks arise when more than one source of relationships exists for a common set of nodes. Many approaches to deal with this kind of complex network data structure are reported in the literature. In this paper, we propose the use of factorial methods to visually explore the complex structure of multiplex networks. Specifically, the adjacency matrices derived from multiplex networks are analyzed using the DISTATIS technique , an extension of multidimensional scaling to three-way data. This technique allows the representation of the different types of relationships in both separate spaces for each layer and a compromise space. The analytical procedure is illustrated using a real world example and simulated data.

Aim of this paper is to present a way to explore time series by combining some fundamental results of stochastic processes theory with graphical and reduction features of factorial methods. A multiple time series visualization and identification strategy is provided by defining a common structural subspace where different regions related to particular ARMA processes are represented. This subspace becomes the reference map for the exploration of multiple time series. In order to provide a unique identification of the ARMA model, a complementary tool represented by a classification tree is proposed.

In this paper we deal with the study of Social Networks by look ing at a new characterisation of the net structure. A network can be repr esented according to its graph structure as the entity G (N, E), whereN is the set of nodes and E is the set of edges. Different statistical measures have been defined in order to describe social networks (i.e. centrality, density, cohesion, etc.) (Wasserman and Faust , 1994). Recently, the statistical modeling of random networks has widely used to describe soci al networks (Koehly and Pattison, 2005). The statistical exponential family model s (Strauss and Ikeda, 1990) are a generalization of the Markov random network models int roduced by Frank and Strauss (1986). These models recognize the complex depende ncies within relational data structures and allow to describe a network as a function of a v ector of parameters and a known vector of graph statistics (see eq.1).

Over the past few decades Social Network Analysis has found increasing application in many social research areas to describe relational ties among social entities. In this paper, we propose to use Multidimensional Data Analysis in the framework of SNA in order to explore the structural properties of a network. In particular, we refer to Contiguity Analysis in order to deal with relational data defined by ties among the actors and described by network centrality and clustering coefficients. The expected results consist of the definition of a network meta-analysis able to synthesise and visualise the pattern of social relationships in a metric space where the related data structure is described. The proposed method is applied to an illustrative example in the context of a virtual learning community.

Conjoint Analysis is one of the most widely used techniques in the assessment of the consumer’s behaviors. This method allows to estimate the partial utility coefficients according to a statistical model linking the overall note of preference with the attribute levels describing the stimuli. Conjoint analysis results are useful in new-product positioning and market segmentation. In this paper a cluster-based segmentation strategy based on a new metric has been proposed. The introduced distance is based on a convex linear combination of two Euclidean distances em bedding information both on the estimated parameters and on the model fitting. Market segments can be then defined according to the proximity of the part-worth coecients and to the explicative power of the estimated models.

In this paper we propose to bring together some facets of the Response Surface Methodology used in Design of Experiments and the graphical displays arising from Multidimensional Data Analysis in order to enhance the interpretation of the traditional plots of the Factorial Techniques. By exploiting the peculiar interpretation of a response surface we describe a data-fitting based approach in order to analyse the relationships between an outer quantitative response variable and a set of principal axes. Moreover, we to use this interpretative-aid tool to represent by a surface response the information about the quality of the representation derived by a multidimensional data analysis.