The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
V. Didelez - One of the best experts on this subject based on the ideXlab platform.
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ACI@UAI - Causal reasoning for events in continuous time: a decision—theoretic approach
2015Co-Authors: V. DidelezAbstract:The dynamics of events occurring in continuous time can be modelled using marked point processes, or multi-state processes. Here, we review and extend the work of R0ysland et al. (2015) on causal reasoning with Local Independence graphs for marked point processes in the context of survival analysis. We relate the results to the decision-theoretic approach of Dawid & Didelez (2010) using influence diagrams, and present additional identifying conditions.
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asymmetric separation for Local Independence graphs
arXiv: Artificial Intelligence, 2012Co-Authors: V. DidelezAbstract:Directed possibly cyclic graphs have been proposed by Didelez (2000) and Nodelmann et al. (2002) in order to represent the dynamic dependencies among stochastic processes. These dependencies are based on a generalization of Granger-causality to continuous time, first developed by Schweder (1970) for Markov processes, who called them Local dependencies. They deserve special attention as they are asymmetric unlike stochastic (in)dependence. In this paper we focus on their graphical representation and develop a suitable, i.e. asymmetric notion of separation, called delta-separation. The properties of this graph separation as well as of Local Independence are investigated in detail within a framework of asymmetric (semi)graphoids allowing a deeper insight into what information can be read off these graphs.
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graphical models for marked point processes based on Local Independence
arXiv: Statistics Theory, 2007Co-Authors: V. DidelezAbstract:A new class of graphical models capturing the dependence structure of events that occur in time is proposed. The graphs represent so-called Local Independences, meaning that the intensities of certain types of events are independent of some (but not necessarily all) events in the past. This dynamic concept of Independence is asymmetric, similar to Granger non-causality, so that the corresponding Local Independence graphs differ considerably from classical graphical models. Hence a new notion of graph separation, called delta-separation, is introduced and implications for the underlying model as well as for likelihood inference are explored. Benefits regarding facilitation of reasoning about and understanding of dynamic dependencies as well as computational simplifications are discussed.
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asymmetric separation for Local Independence graphs
Uncertainty in Artificial Intelligence, 2006Co-Authors: V. DidelezAbstract:Directed possibly cyclic graphs have been proposed by Didelez (2000) and Nodelmann et al. (2002) in order to represent the dynamic dependencies among stochastic processes. These dependencies are based on a generalization of Granger-causality to continuous time, first developed by Schweder (1970) for Markov processes, who called them Local dependencies. They deserve special attention as they are asymmetric. In this paper we focus on their graphical representation and develop an asymmetric notion of separation. The properties of this graph separation as well as Local Independence are investigated in detail within a framework of asymmetric (semi)graphoids allowing insight into what information can be read off these graphs.
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UAI - Asymmetric separation for Local Independence graphs
2006Co-Authors: V. DidelezAbstract:Directed possibly cyclic graphs have been proposed by Didelez (2000) and Nodelmann et al. (2002) in order to represent the dynamic dependencies among stochastic processes. These dependencies are based on a generalization of Granger-causality to continuous time, first developed by Schweder (1970) for Markov processes, who called them Local dependencies. They deserve special attention as they are asymmetric. In this paper we focus on their graphical representation and develop an asymmetric notion of separation. The properties of this graph separation as well as Local Independence are investigated in detail within a framework of asymmetric (semi)graphoids allowing insight into what information can be read off these graphs.
David Andrich - One of the best experts on this subject based on the ideXlab platform.
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Accounting for Local Dependence with the Rasch Model: The Paradox of Information Increase.
Journal of applied measurement, 2020Co-Authors: David AndrichAbstract:Test theories imply statistical, Local Independence. Where Local Independence is violated, models of modern test theory that account for it have been proposed. One violation of Local Independence occurs when the response to one item governs the response to a subsequent item. Expanding on a formulation of this kind of violation between two items in the dichotomous Rasch model, this paper derives three related implications. First, it formalises how the polytomous Rasch model for an item constituted by summing the scores of the dependent items absorbs the dependence in its threshold structure. Second, it shows that as a consequence the unit when the dependence is accounted for is not the same as if the items had no response dependence. Third, it explains the paradox, known, but not explained in the literature, that the greater the dependence of the constituent items the greater the apparent information in the constituted polytomous item when it should provide less information.
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Violations of the Assumption of Independence I—Multidimensionality and Response Dependence
Springer Texts in Education, 2019Co-Authors: David Andrich, Ida MaraisAbstract:The Rasch model implies statistical Independence of responses, generally referred to as Local Independence. Local Independence can be violated in two generic ways: multidimensionality, when person parameters other than \( \beta \) are involved in the response, and response dependence, when the response to one item might depend on the response to a previous item. In general, over-discriminating items often indicate response dependence and under-discriminating items often indicate multidimensionality. High correlations between standardized item residuals or a PCA of the residuals with a meaningful pattern indicate a violation of Independence. These two violations have opposite effects on the scale and on reliability.
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Quantifying Local, Response Dependence between Two Polytomous Items Using the Rasch Model.
Applied Psychological Measurement, 2012Co-Authors: David Andrich, Stephen Humphry, Ida MaraisAbstract:Models of modern test theory imply statistical Independence among responses, generally referred to as Local Independence. One violation of Local Independence occurs when the response to one item governs the response to a subsequent item. Expanding on a formulation of this kind of violation as a process in the dichotomous Rasch model, this article generalizes the dependence process to the case of the unidimensional, polytomous Rasch model. It then shows how the magnitude of this violation can be estimated as a change in the location of thresholds separating adjacent categories in the second item caused by the response dependence on the first. As in the dichotomous model, it is suggested that this index is relatively more tangible in interpretation than other indices of dependence that are either a weight in the interaction term in a model or a correlation coefficient. One function of this method of assessing dependence is likely to be in the development of tests and assessment formats where evidence of the...
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Quantifying Response Dependence between Two Dichotomous Items Using the Rasch Model.
Applied Psychological Measurement, 2010Co-Authors: David Andrich, Svend KreinerAbstract:Models of modern test theory imply statistical Independence among responses, generally referred to as Local Independence. One violation of Local Independence occurs when the response to one item governs the response to a subsequent item. Expanding on a formulation of this kind of violation as a process in the dichotomous Rasch model, this article shows how the magnitude of this violation can be estimated as a change in the location of the second item caused by its dependence on the first. It is suggested that this index is relatively more tangible in interpretation than other indices of dependence that are either a weight in the interaction term in a model or a correlation coefficient. The prime function of this method of assessing dependence is likely to be in the development of tests where evidence of the degree of dependence of one item on another in a pilot study can be used as part of the evidence in deciding which items will be retained in a final version of a test.
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formalizing dimension and response violations of Local Independence in the unidimensional rasch model
Journal of applied measurement, 2008Co-Authors: Ida Marais, David AndrichAbstract:: Local Independence in the Rasch model can be violated in two generic ways that are generally not distinguished clearly in the literature. In this paper we distinguish between a violation of unidimensionality, which we call trait dependence, and a specific violation of statistical Independence, which we call response dependence, both of which violate Local Independence. Distinct algebraic formulations for trait and response dependence are developed as violations of the dichotomous Rasch model, data are simulated with varying degrees of dependence according to these formulations, and then analysed according to the Rasch model assuming no violations. Relative to the case of no violation it is shown that trait and response dependence result in opposite effects on the unit of scale as manifested in the range and standard deviation of the scale and the standard deviation of person locations. In the case of trait dependence the scale is reduced; in the case of response dependence it is increased. Again, relative to the case of no violation, the two violations also have opposite effects on the person separation index (analogous to Cronbach's alpha reliability index of traditional test theory in value and construction): it decreases for data with trait dependence; it increases for data with response dependence. A standard way of accounting for dependence is to combine the dependent items into a higher-order polytomous item. This typically results in a decreased person separation index index and Cronbach's alpha, compared with analysing items as discrete, independent items. This occurs irrespective of the kind of dependence in the data, and so further contributes to the two violations not being distinguished clearly. In an attempt to begin to distinguish between them statistically this paper articulates the opposite effects of these two violations in the dichotomous Rasch model.
Carrie R Houts - One of the best experts on this subject based on the ideXlab platform.
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a diagnostic procedure to detect departures from Local Independence in item response theory models
Psychological Methods, 2017Co-Authors: Michael C Edwards, Carrie R HoutsAbstract:: Item response theory (IRT) is a widely used measurement model. When considering its use in education, health outcomes, and psychology, it is likely to be one of the most impactful psychometric models in existence. IRT has many advantages over classical test theory-based measurement models. For these advantages to hold in practice, strong assumptions must be satisfied. One of these assumptions, Local Independence, is the focus of the work described here. Local Independence is the assumption that, conditional on the latent variable(s), item responses are unrelated to one another (i.e., independent). Stated another way, Local Independence implies that the only thing causing items to covary is the modeled latent variable(s). Violations of this assumption, quite aptly titled Local dependence, can have serious consequences for the estimated parameters. A new diagnostic is proposed, based on parameter stability in an item-level jackknife resampling procedure. We review the ideas underlying the new diagnostic and how it is computed before covering some simulated and real examples demonstrating its effectiveness. (PsycINFO Database Record
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Comparing Surface and Underlying Local Dependence Levels via Polychoric Correlations.
Applied Psychological Measurement, 2014Co-Authors: Carrie R Houts, Michael C EdwardsAbstract:Item response theory (IRT) is a set of psychometric models used in the social and behavioral sciences. As part of applying these models in practice, a number of assumptions are made. A large literature exists assessing the extent to which these assumptions are satisfied in a given data set. One of these assumptions, Local Independence, is the focus of the research described here. When the Local Independence assumption is violated, there is said to be Local dependence (LD). Several different models of LD have been proposed, and a number of studies have been conducted examining the performance of different methods at detecting LD. Underlying LD (ULD) and surface LD (SLD) were proposed as two possible mechanisms underlying observed LD in an early exploration of detection procedures. In a number of previous studies, it appears as though ULD is more difficult to detect than SLD. In this article, the authors demonstrate a procedure to examine comparability of induced LD and present results, which suggest a re-i...
Guido Consonni - One of the best experts on this subject based on the ideXlab platform.
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AISTATS - Relaxing the Local Independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition models.
1999Co-Authors: Daniela Golinelli, David Madigan, Guido ConsonniAbstract:The simplest method proposed by Spiegelhalter and Lauritzen (1990) to perform quantitative learning in ADG presents a potential weakness: the Local Independence assumption. We propose to alleviate this problem through the use of Hierarchical Partition Models. Our approach is compared with the previous one from an interpretative and predictive point of view.
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relaxing the Local Independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition models
International Conference on Artificial Intelligence and Statistics, 1999Co-Authors: Daniela Golinelli, David Madigan, Guido ConsonniAbstract:The simplest method proposed by Spiegelhalter and Lauritzen (1990) to perform quantitative learning in ADG presents a potential weakness: the Local Independence assumption. We propose to alleviate this problem through the use of Hierarchical Partition Models. Our approach is compared with the previous one from an interpretative and predictive point of view.
Ida Marais - One of the best experts on this subject based on the ideXlab platform.
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Violations of the Assumption of Independence I—Multidimensionality and Response Dependence
Springer Texts in Education, 2019Co-Authors: David Andrich, Ida MaraisAbstract:The Rasch model implies statistical Independence of responses, generally referred to as Local Independence. Local Independence can be violated in two generic ways: multidimensionality, when person parameters other than \( \beta \) are involved in the response, and response dependence, when the response to one item might depend on the response to a previous item. In general, over-discriminating items often indicate response dependence and under-discriminating items often indicate multidimensionality. High correlations between standardized item residuals or a PCA of the residuals with a meaningful pattern indicate a violation of Independence. These two violations have opposite effects on the scale and on reliability.
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Quantifying Local, Response Dependence between Two Polytomous Items Using the Rasch Model.
Applied Psychological Measurement, 2012Co-Authors: David Andrich, Stephen Humphry, Ida MaraisAbstract:Models of modern test theory imply statistical Independence among responses, generally referred to as Local Independence. One violation of Local Independence occurs when the response to one item governs the response to a subsequent item. Expanding on a formulation of this kind of violation as a process in the dichotomous Rasch model, this article generalizes the dependence process to the case of the unidimensional, polytomous Rasch model. It then shows how the magnitude of this violation can be estimated as a change in the location of thresholds separating adjacent categories in the second item caused by the response dependence on the first. As in the dichotomous model, it is suggested that this index is relatively more tangible in interpretation than other indices of dependence that are either a weight in the interaction term in a model or a correlation coefficient. One function of this method of assessing dependence is likely to be in the development of tests and assessment formats where evidence of the...
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formalizing dimension and response violations of Local Independence in the unidimensional rasch model
Journal of applied measurement, 2008Co-Authors: Ida Marais, David AndrichAbstract:: Local Independence in the Rasch model can be violated in two generic ways that are generally not distinguished clearly in the literature. In this paper we distinguish between a violation of unidimensionality, which we call trait dependence, and a specific violation of statistical Independence, which we call response dependence, both of which violate Local Independence. Distinct algebraic formulations for trait and response dependence are developed as violations of the dichotomous Rasch model, data are simulated with varying degrees of dependence according to these formulations, and then analysed according to the Rasch model assuming no violations. Relative to the case of no violation it is shown that trait and response dependence result in opposite effects on the unit of scale as manifested in the range and standard deviation of the scale and the standard deviation of person locations. In the case of trait dependence the scale is reduced; in the case of response dependence it is increased. Again, relative to the case of no violation, the two violations also have opposite effects on the person separation index (analogous to Cronbach's alpha reliability index of traditional test theory in value and construction): it decreases for data with trait dependence; it increases for data with response dependence. A standard way of accounting for dependence is to combine the dependent items into a higher-order polytomous item. This typically results in a decreased person separation index index and Cronbach's alpha, compared with analysing items as discrete, independent items. This occurs irrespective of the kind of dependence in the data, and so further contributes to the two violations not being distinguished clearly. In an attempt to begin to distinguish between them statistically this paper articulates the opposite effects of these two violations in the dichotomous Rasch model.