The Experts below are selected from a list of 27390 Experts worldwide ranked by ideXlab platform
Han Liu - One of the best experts on this subject based on the ideXlab platform.
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on semiparametric Exponential Family graphical models
Journal of Machine Learning Research, 2018Co-Authors: Zhuoran Yang, Yang Ning, Han LiuAbstract:We propose a new class of semiparametric Exponential Family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice. Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions. In particular, we propose a symmetric pairwise score test for the presence of a single edge in the graph. Compared to the existing methods for hypothesis tests, our approach takes into account of the symmetry of the parameters, such that the inferential results are invariant with respect to the dierent parametrizations of the same edge. Thorough numerical simulations and a real data example are provided to back up our theoretical results.
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on semiparametric Exponential Family graphical models
arXiv: Machine Learning, 2014Co-Authors: Zhuoran Yang, Yang Ning, Han LiuAbstract:We propose a new class of semiparametric Exponential Family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice. Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions. In particular, we propose a symmetric pairwise score test for the presence of a single edge in the graph. Compared to the existing methods for hypothesis tests, our approach takes into account of the symmetry of the parameters, such that the inferential results are invariant with respect to the different parametrizations of the same edge. Thorough numerical simulations and a real data example are provided to back up our results.
Pavel N. Krivitsky - One of the best experts on this subject based on the ideXlab platform.
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Exponential-Family Random Graph Models for Multi-Layer Networks
Psychometrika, 2020Co-Authors: Pavel N. Krivitsky, Laura M. Koehly, Christopher Steven MarcumAbstract:Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the Exponential-Family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway–Maxwell–Binomial distribution to model the marginal dependence among multiple layers; a “layer logic” language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and nondegenerate triadic and degree effects. The developments are demonstrated on two previously published datasets.
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Exponential Family random graph models for rank order relational data
Sociological Methodology, 2017Co-Authors: Pavel N. Krivitsky, Carter T ButtsAbstract:Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of Exponential-Family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of interpersonal interaction among members of a technology education program.
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Exponential Family random graph models for valued networks
Electronic Journal of Statistics, 2012Co-Authors: Pavel N. KrivitskyAbstract:Exponential-Family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM for networks whose ties are counts and discuss issues that arise when moving beyond the binary case. We introduce model terms that generalize and model common social network features for such data and apply these methods to a network dataset whose values are counts of interactions.
Zhuoran Yang - One of the best experts on this subject based on the ideXlab platform.
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on semiparametric Exponential Family graphical models
Journal of Machine Learning Research, 2018Co-Authors: Zhuoran Yang, Yang Ning, Han LiuAbstract:We propose a new class of semiparametric Exponential Family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice. Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions. In particular, we propose a symmetric pairwise score test for the presence of a single edge in the graph. Compared to the existing methods for hypothesis tests, our approach takes into account of the symmetry of the parameters, such that the inferential results are invariant with respect to the dierent parametrizations of the same edge. Thorough numerical simulations and a real data example are provided to back up our theoretical results.
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on semiparametric Exponential Family graphical models
arXiv: Machine Learning, 2014Co-Authors: Zhuoran Yang, Yang Ning, Han LiuAbstract:We propose a new class of semiparametric Exponential Family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice. Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions. In particular, we propose a symmetric pairwise score test for the presence of a single edge in the graph. Compared to the existing methods for hypothesis tests, our approach takes into account of the symmetry of the parameters, such that the inferential results are invariant with respect to the different parametrizations of the same edge. Thorough numerical simulations and a real data example are provided to back up our results.
Carter T Butts - One of the best experts on this subject based on the ideXlab platform.
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a perfect sampling method for Exponential Family random graph models
arXiv: Computation, 2017Co-Authors: Carter T ButtsAbstract:Generation of deviates from random graph models with non-trivial edge dependence is an increasingly important problem. Here, we introduce a method which allows perfect sampling from random graph models in Exponential Family form ("Exponential Family random graph" models), using a variant of Coupling From The Past. We illustrate the use of the method via an application to the Markov graphs, a Family that has been the subject of considerable research. We also show how the method can be applied to a variant of the biased net models, which are not Exponentially parameterized.
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Exponential Family random graph models for rank order relational data
Sociological Methodology, 2017Co-Authors: Pavel N. Krivitsky, Carter T ButtsAbstract:Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of Exponential-Family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of interpersonal interaction among members of a technology education program.
Yang Ning - One of the best experts on this subject based on the ideXlab platform.
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on semiparametric Exponential Family graphical models
Journal of Machine Learning Research, 2018Co-Authors: Zhuoran Yang, Yang Ning, Han LiuAbstract:We propose a new class of semiparametric Exponential Family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice. Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions. In particular, we propose a symmetric pairwise score test for the presence of a single edge in the graph. Compared to the existing methods for hypothesis tests, our approach takes into account of the symmetry of the parameters, such that the inferential results are invariant with respect to the dierent parametrizations of the same edge. Thorough numerical simulations and a real data example are provided to back up our theoretical results.
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on semiparametric Exponential Family graphical models
arXiv: Machine Learning, 2014Co-Authors: Zhuoran Yang, Yang Ning, Han LiuAbstract:We propose a new class of semiparametric Exponential Family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice. Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions. In particular, we propose a symmetric pairwise score test for the presence of a single edge in the graph. Compared to the existing methods for hypothesis tests, our approach takes into account of the symmetry of the parameters, such that the inferential results are invariant with respect to the different parametrizations of the same edge. Thorough numerical simulations and a real data example are provided to back up our results.
