1,720,966 research outputs found
Nonparametric Estimation of the Random Effect Variance-Covariance Matrix with Partial Information from the Clusters
Full text linkThe proposed approach is useful whenever the variability of the response in a linear model can be viewed as the sum of two independent sources of variability, one that is common to all clusters and it is unknown, and another which is assumed to be available and it is cluster-specific. Here by clusters we mean, for instance, secondlevel units in multi-level models (schools, hospitals etc.), or subjects in repeated measure experiments. The responses here have to be thought as functions of the first-level observations, whose variability is known to depend only on the cluster’s specificities. These settings include linear mixed models (LMM) when the stimators of the parameters of interest are obtained conditionally on each cluster. The model may account for additional informations on the clusters, such as covariates, or contrast vectors. An estimator of the common source of variability is obtained from the residual deviance of the (multivariate) model, opportunely re-scaled, through the moment method. An iterative procedure is then suggested (whose initial step depends on the available information), that turns out to be a special case of the EM-algorithm
Nonparametric Estimation of Random Effect Variance with Partial Information from the Clusters
Full text linkThis work is a new proposal for estimating the variance of the random effects in case the knowledge of the internal variability of the clusters is (or might be) assumed to be known. Here by clusters we mean, for instance, second-level units in multi-level models (schools, hospitals etc.), or subjects in repeated measure experiments. The proposed approach is useful whenever the variability of the response in a linear model can be viewed as the sum of two independent sources of variability, one that is common to all clusters and it is unknown, and another which is assumed to be available and it is clusterspecific. The responses here have to be thought as functions of the first-level
observations, whose variability is known to depend only on the cluster's specificities. These settings include linear mixed models (LMM) when the estimators of the effects of interest are obtained conditionally on each cluster. The model may account for additional informations on the clusters, such as covariates, or contrast vectors. An estimator of the common source of variability is obtained from the residual deviance of the model, opportunely re-scaled, through the moment method. An iterative procedure is then suggested (whose initial step depends on the available information), that turns out to be a special case of the EM-algorithm
Permutation tests for between-unit fixed effects in multivariate generalized linear mixed models
Full text linkExact Multivariate Permutation Tests for Fixed Effects in Mixed-Models
Full text linkA test for the fixed effect in mixed-models is proposed. It is based on permutation strategy and is exact. The testing approach presented is very general and the class of model covered is very broad.
Multivariate responses with different type of variables (e.g. continuous, categorical and ranks) are usually tested with separated models and the overall test are usually reached trough Bonferroni-like combinations, i.e. without taking in account the joint distribution of the tests statistics. On the contrary in this approach the joint distribution is immediately obtained and the dependence among tests is taken in account in the overall test
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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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