1,117 research outputs found
Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
We describe some functions in the R package ggm to derive from a given Markov model, represented by a directed acyclic graph, different types of graphs induced after marginalizing over and conditioning on some of the variables. The package has a few basic functions that find the essential graph, the induced concentration and covariance graphs, and several types of chain graphs implied by the directed acyclic graph (DAG) after grouping and reordering the variables. These functions can be useful to explore the impact of latent variables or of selection effects on a chosen data generating model.
Matrix representations and independencies in directed acyclic graphs
For a directed acyclic graph, there are two known criteria to decide whether
any specific conditional independence statement is implied for all
distributions factorized according to the given graph. Both criteria are based
on special types of path in graphs. They are called separation criteria because
independence holds whenever the conditioning set is a separating set in a graph
theoretical sense. We introduce and discuss an alternative approach using
binary matrix representations of graphs in which zeros indicate independence
statements. A matrix condition is shown to give a new path criterion for
separation and to be equivalent to each of the previous two path criteria.Comment: Published in at http://dx.doi.org/10.1214/08-AOS594 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Chain graph models of multivariate regression type for categorical data
We discuss a class of chain graph models for categorical variables defined by
what we call a multivariate regression chain graph Markov property. First, the
set of local independencies of these models is shown to be Markov equivalent to
those of a chain graph model recently defined in the literature. Next we
provide a parametrization based on a sequence of generalized linear models with
a multivariate logistic link function that captures all independence
constraints in any chain graph model of this kind.Comment: Published in at http://dx.doi.org/10.3150/10-BEJ300 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Star graphs induce tetrad correlations: for Gaussian as well as for binary variables
Tetrad correlations were obtained historically for Gaussian distributions
when tasks are designed to measure an ability or attitude so that a single
unobserved variable may generate the observed, linearly increasing dependences
among the tasks. We connect such generating processes to a particular type of
directed graph, the star graph, and to the notion of traceable regressions.
Tetrad correlation conditions for the existence of a single latent variable are
derived. These are needed for positive dependences not only in joint Gaussian
but also in joint binary distributions. Three applications with binary items
are given.Comment: 21 pages, 2 figures, 5 table
Central banks and information provided to the private sector
This paper examines the information provided to the private sector by central anks. By using the principal component analysis, we investigated the variance of the procedural rules followed by nine major central banks about information reatments. We investigate problems related to the information coming from the entral banks by focusing on the quantity and quality perspectives and highlight the methodological complexity of the investigation. We find that a synthetic uantitative index of transparency is not enough to represent the phenomenon ince it can result misleading in understanding the behavior of institutionally different central banks associated with the same index values.Central bank transparency, principal components, monetary policy.
Central Banks and Information Provided to the Private Sector
This paper examines the information provided to the private sector by central banks. By using the principal component analysis, we investigated the variance of the procedural rules followed by nine major central banks about information treatments. We investigate problems related to the information coming from the central banks by focusing on the quantity and quality perspectives and highlight the methodological complexity of the investigation. We find that a synthetic quantitative index of transparency is not enough to represent the phenomenon since it can result misleading in understanding the behavior of institutionally different central banks associated with the same index values.Central bank transparency, principal components, monetary policy
Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
We describe some functions in the R package ggm to derive from a given Markov model, represented by a directed acyclic graph, different types of graphs induced after marginalizing over and conditioning on some of the variables. The package has a few basic functions that find the essential graph, the induced concentration and covariance graphs, and several types of chain graphs implied by the directed acyclic graph (DAG) after grouping and reordering the variables. These functions can be useful to explore the impact of latent variables or of selection effects on a chosen data generating model
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