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11
Binary models for marginal independence
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B
, 2005
"... A number of authors have considered multivariate Gaussian models for marginal independence. In this paper we develop models for binary data with the same independence structure. The models can be parameterized based on Möbius inversion and maximum likelihood estimation can be performed using a versi ..."
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Cited by 21 (2 self)
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A number of authors have considered multivariate Gaussian models for marginal independence. In this paper we develop models for binary data with the same independence structure. The models can be parameterized based on Möbius inversion and maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. The approach is illustrated on a simple example. Relations to multivariate logistic and dependence ratio models are discussed.
Logmean linear models for binary data
 Biometrika
, 2013
"... This paper is devoted to the theory and application of a novel class of models for binary data, which we call logmean linear (LML) models. The characterizing feature of these models is that they are specified by linear constraints on the LML parameter, defined as a loglinear expansion of the mean ..."
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Cited by 3 (2 self)
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This paper is devoted to the theory and application of a novel class of models for binary data, which we call logmean linear (LML) models. The characterizing feature of these models is that they are specified by linear constraints on the LML parameter, defined as a loglinear expansion of the mean parameter of the multivariate Bernoulli distribution. We show that marginal independence relationships between variables can be specified by setting certain LML interactions to zero and, more specifically, that graphical models of marginal independence are LML models. LML models are code dependent, in the sense that they are not invariant with respect to relabelling of variable values. As a consequence, they allow us to specify submodels defined by codespecific independencies, which are independencies in subpopulations of interest. This special feature of LML models has useful applications. Firstly, it provides a flexible way to specify parsimonious submodels of marginal independence models. The main advantage of this approach concerns the interpretation of the submodel, which is fully characterized by independence relationships, either marginal or codespecific. Secondly, the codespecific nature of these models can be exploited to focus on a fixed, arbitrary, cell of the probability table and on the corresponding subpopulation. This leads to an innovative family of models, which we call pivotal codespecific LML models, that is especially useful when the interest of researchers is focused on a small subpopulation obtained by stratifying individuals according to some features. The application of LML models is illustrated on two datasets, one of which concerns the use of pivotal codespecific LML models in the field of personalized medicine.
Some Group Inspection Based MultiAttribute Control Charts to Identify Process Deterioration
"... Abstract: In a production process, when quality of the product depends on more than one characteristic, ‘Multivariate Quality Control ’ (MQC) techniques are used. Many MQC techniques have been developed to control multivariate variable processes, but not much work has been reported on multivariate a ..."
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Abstract: In a production process, when quality of the product depends on more than one characteristic, ‘Multivariate Quality Control ’ (MQC) techniques are used. Many MQC techniques have been developed to control multivariate variable processes, but not much work has been reported on multivariate attribute processes. In this article, we propose some group inspection based multiattribute control charts to identify process deterioration. The charts proposed are the ‘MultiAttribute np ’ (MA−np) chart, the ‘MultiAttribute Synthetic’ (MA − Syn) chart and the ‘MultiAttribute Group Runs ’ (MA−GR) chart. The charts are developed by using MPtest based on the exact distribution. It is numerically illustrated that, MA−GR chart performs better than the other two charts. Also in steady state MA−GR chart performs better than MA−np and MA−Syn control charts. A procedure of identifying the attributes causing signal is also proposed.
Dichotomization invariant logmean linear parameterization for discrete graphical models
, 2013
"... of marginal independence ..."
Logmean linear models for binary data Alberto
, 2012
"... This paper introduces a novel class of models for binary data, which we call logmean linear models. The characterizing feature of these models is that they are specified by linear constraints on the logmean linear parameter, defined as a loglinear expansion of the mean parameter of the multivaria ..."
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This paper introduces a novel class of models for binary data, which we call logmean linear models. The characterizing feature of these models is that they are specified by linear constraints on the logmean linear parameter, defined as a loglinear expansion of the mean parameter of the multivariate Bernoulli distribution. We show that marginal independence relationships between variables can be specified by setting certain logmean linear interactions to zero and, more specifically, that graphical models of marginal independence are logmean linear models. Our approach overcomes some drawbacks of the existing parameterizations of graphical models of marginal independence.
Modelling Association Among Bivariate Exposures In Matched CaseControl Studies
"... The paper considers the problem of modelling association between two exposure variables in a matched casecontrol study, where both the exposures may be partially missing. The exposure variables could all be categorical or continuous or could be a mixed set of some categorical and some continuous va ..."
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The paper considers the problem of modelling association between two exposure variables in a matched casecontrol study, where both the exposures may be partially missing. The exposure variables could all be categorical or continuous or could be a mixed set of some categorical and some continuous variables. Association models for the missing exposure variables using the completely observed covariates and disease status are proposed for each of the three scenarios. The models account for varying stratum heterogeneity in different matched sets. Three real data examples accompany the proposed models. The examples as well as a small scale simulation study indicate that in presence of missingness and association, modelling the association between the exposures rather than ignoring it, often leads to better estimates of the relative risk parameters with smaller standard errors. Estimation of the model parameters is carried out in a Bayesian framework and the estimates are compared with classical conditional logistic regression estimates. AMS (2000) subject classification. Primary 62F10, 62F15, 62H12.
JOINT REGRESSION AND ASSOCIATION MODELS FOR REPEATED CATEGORICAL RESPONSES
, 2006
"... ISBN 9517406770 ..."
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ORTH: R and SAS Software For Correlated Binary Data Based On Orthogonalized
, 2008
"... In this article, we describe a new software for modelling correlated binary data whose raison d’etre is based on the work of Zink (Zink, 2003). The approach taken is based on what Zink calls“orthogonalized residuals ” that includes, as a special case, alternating logistic regressions(Carey et al., ..."
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In this article, we describe a new software for modelling correlated binary data whose raison d’etre is based on the work of Zink (Zink, 2003). The approach taken is based on what Zink calls“orthogonalized residuals ” that includes, as a special case, alternating logistic regressions(Carey et al., 1993). The use of these residuals leads to a feasible computational platform for correlated binary data that addresses some of the shortcomings of an earlier formulation based on conditional residuals. Furthermore, this new approach recasts alternating logistic regressions in a framework consistent with standard estimating equation theory facilitating study of its properties. The software is flexible with respect to fitting in that the user can choose estimating equations for the association model based on alternating logistic regressions or orthogonalized residuals, the latter choice providing a nondiagonal working covariance matrix for second moment parameters for obtaining potentially greater efficiency. Diagnostics based on this method are also implemented in the software. The mathematical details of the procedure are briefly reviewed and the software is applied to medical data sets. 1 1
Multimarket directionofchange modeling using dependence ratios
"... We consider a multivariate dynamic model for the joint distribution of binary outcomes associated with directionsofchange for several markets or assets. The marginal distribution of each binary outcome follows a dynamic binary choice model, while the association structure is parameterized via poss ..."
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We consider a multivariate dynamic model for the joint distribution of binary outcomes associated with directionsofchange for several markets or assets. The marginal distribution of each binary outcome follows a dynamic binary choice model, while the association structure is parameterized via possibly time varying dependence ratios. We illustrate the technique using daily stock index returns from three European markets, from three Baltic markets, and from two Chinese exchanges.