1,720,965 research outputs found
Context-Specific Independencies in Stratified Chain Regression Graphical Models
Get PDFGraphical models are a useful tool with increasing diffusion. In the categorical variable framework, they provide important visual support to understand the relationships among the considered variables. Besides, particular chain graphical models are suitable to represent multivariate regression models. However, the associated parameterization, such as marginal log-linear models, is often difficult to interpret when the number of variables increases because of a large number of parameters involved. On the contrary, conditional and marginal independencies reduce the number of parameters needed to represent the joint probability distribution of the variables. In compliance with the parsimonious principle, it is worthwhile to consider also the so-called context-specific independencies, which are conditional independencies holding for particular values of the variables in the conditioning set. In this work, we propose a particular chain graphical model able to represent these context-specific independencies through labeled arcs. We provide also the Markov properties able to describe marginal, conditional, and context-specific independencies from this new chain graph. Finally, we show the results in an application to a real data set
Context-specific independencies in Hierarchical Multinomial Marginal models
Get PDFThis paper focuses on studying the relationships among a set of categorical (ordinal) variables collected in a contingency table. Besides the marginal and conditional (in)dependencies, thoroughly analyzed in the literature, we consider the context-specific independencies holding only in a subspace of the outcome space of the conditioning variables. To this purpose we consider the Hierarchical Multinomial Marginal models and we provide several original results about the representation of context-specific independencies through these models. The theoretical results are supported by an application concerning the innovation degree of Italian enterprises
Study of context-specific independencies through Chain Stratified Graph Models for categorical variables
No full textThis work focuses on the study of the relationships among a set of categorical (ordinal) variables under the perspective of marginal, conditional and contextspecific independencies. If the first two are well known, the last one concerns independencies holding only in a subspace of the outcome space. At this aim we take advantage from the well know relationship between the chain graphical models and the marginal log-linear models and we adapted this by considering the context-specific independencies. The resultant graphical model is a so-called ”stratified” graphical model with labeled arcs that can be described by new constraints on marginal loglinear models. An application about the innovation degree of the Italian enterprises is provided
Context-specific independence in innovation study
No full textThe study of (in)dependence relationships among a set of categorical variables collected in a contingency table is an amply topic. In this work we want to focus on the so called context-specific independence where the conditional independence holds only in a subspace of the outcome space. The main aspects that we introduce concern the definition in the same model of marginal, conditional and context-specific independencies, through the marginal models. Furthermore, we investigate how it is possible to test these context-specific independencies when there are ordinal variables. Finally, we propose a graphical representation of all the considered independencies taking advantages from the chain graph model. We show the results on an application on ”The Italian Innovation Survey” of Istat (2012)
Context-Specific independencies embedded in Chain Graph Models of type I
Get PDFFor a set of variables collected in a contingency table, we focus on a particular kind of relationships such as the context-specific independencies. These are conditional independencies that hold for particular values of the conditioning set. Given the advantages of the graphical models, we use them to represent different relationships among the variables, including the context-specific independencies. In particular, we enrich chain graph models with labelled arcs. Furthermore, we consider the well-known relationships between chain graph models and hierarchical multinomial marginal models and we introduce new constraints on parameters in order to describe the context-specific relationship. Finally, we provide an application to the study of innovation in Italy by comparing two different periods
Investigation on Life Satisfaction Through (Stratified) Chain Regression Graph Models
No full textThe study of marginal and/or conditional relationships among a set of categorical variables is widely investigated in the literature. In this work we focus on Chain Graph Models combined with the Hierarchical Multinomial Marginal Models and we improve the framework in order to take into account the context-specic independencies that are particular conditional independencies holding only for certain values of the conditioning variables. Letting the role of the variables to be purely explicative, purely response or mixed, in particular, we consider the (Stratied) Chain Regression Graph Model. A social application on life satisfaction is provided in order to investigate how the satisfaction of the interviewees' life can be affected by individual characteristics and personal achievement and, at the same time, how the personal aspects can affect the educational level and the working position
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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