The Experts below are selected from a list of 66807 Experts worldwide ranked by ideXlab platform
Martin Wetzels - One of the best experts on this subject based on the ideXlab platform.
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hierarchical Latent Variable models in pls sem guidelines for using reflective formative type models
Long Range Planning, 2012Co-Authors: Jan-michael Becker, Kristina Klein, Martin WetzelsAbstract:Partial least squares structural equation modeling (PLS-SEM), or partial least squares path modeling (PLS) has enjoyed increasing popularity in recent years. In this context, the use of hierarchical Latent Variable models has allowed researchers to extend the application of PLS-SEM to more advanced and complex models. However, the attention has been mainly focused on hierarchical Latent Variable models with reflective relationships. In this manuscript, we focus on second-order hierarchical Latent Variable models that include formative relationships. First, we discuss a typology of (second-order) hierarchical Latent Variable models. Subsequently, we provide an overview of different approaches that can be used to estimate the parameters in these models: (1) the repeated indicator approach, (2) the two-stage approach, and (3) the hybrid approach. Next, we compare the approaches using a simulation study and an empirical application in a strategic human resource management context. The findings from the simulation and the empirical application serve as a basis for recommendations and guidelines regarding the use and estimation of reflective-formative type hierarchical Latent Variable models in PLS-SEM.
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Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models
Long Range Planning, 2012Co-Authors: Jan-michael Becker, Kristina Klein, Martin WetzelsAbstract:Partial least squares structural equation modeling (PLS-SEM), or partial least squares path modeling (PLS) has enjoyed increasing popularity in recent years. In this context, the use of hierarchical Latent Variable models has allowed researchers to extend the application of PLS-SEM to more advanced and complex models. However, the attention has been mainly focused on hierarchical Latent Variable models with reflective relationships. In this manuscript, we focus on second-order hierarchical Latent Variable models that include formative relationships. First, we discuss a typology of (second-order) hierarchical Latent Variable models. Subsequently, we provide an overview of different approaches that can be used to estimate the parameters in these models: (1) the repeated indicator approach, (2) the two-stage approach, and (3) the hybrid approach. Next, we compare the approaches using a simulation study and an empirical application in a strategic human resource management context. The findings from the simulation and the empirical application serve as a basis for recommendations and guidelines regarding the use and estimation of reflective-formative type hierarchical Latent Variable models in PLS-SEM. © 2012 Elsevier Ltd.
Zhiqiang Ge - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application
IEEE Transactions on Control Systems Technology, 2019Co-Authors: Zhiqiang Ge, Xinru ChenAbstract:Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data modeling and analytics. In this paper, the dynamical and uncertain data characteristics are both taken into consideration for the regression modeling purpose. Based on the probabilistic Latent Variable modeling framework, the linear dynamic system is introduced for incorporation of the dynamical data feature. The expectation-maximization Algorithm is introduced for parameter learning of the dynamical probabilistic Latent Variable model, based on which a new soft sensing scheme is then formulated for online prediction of key/quality Variables in the process. An industrial case study illustrates the necessity and effectiveness of introducing the dynamical data information into the probabilistic Latent Variable model.
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process data analytics via probabilistic Latent Variable models a tutorial review
Industrial & Engineering Chemistry Research, 2018Co-Authors: Zhiqiang GeAbstract:Dimensionality reduction is important for the high-dimensional nature of data in the process industry, which has made Latent Variable modeling methods popular in recent years. By projecting high-dimensional data into a lower-dimensional space, Latent Variables models are able to extract key information from process data while simultaneously improving the efficiency of data analytics. Through a probabilistic viewpoint, this paper carries out a tutorial review of probabilistic Latent Variable models on process data analytics. Detailed illustrations of different kinds of basic probabilistic Latent Variable models (PLVM) are provided, as well as their research statuses. Additionally, more counterparts of those basic PLVMs are introduced and discussed for process data analytics. Several perspectives are highlighted for future research on this topic.
Jan-michael Becker - One of the best experts on this subject based on the ideXlab platform.
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hierarchical Latent Variable models in pls sem guidelines for using reflective formative type models
Long Range Planning, 2012Co-Authors: Jan-michael Becker, Kristina Klein, Martin WetzelsAbstract:Partial least squares structural equation modeling (PLS-SEM), or partial least squares path modeling (PLS) has enjoyed increasing popularity in recent years. In this context, the use of hierarchical Latent Variable models has allowed researchers to extend the application of PLS-SEM to more advanced and complex models. However, the attention has been mainly focused on hierarchical Latent Variable models with reflective relationships. In this manuscript, we focus on second-order hierarchical Latent Variable models that include formative relationships. First, we discuss a typology of (second-order) hierarchical Latent Variable models. Subsequently, we provide an overview of different approaches that can be used to estimate the parameters in these models: (1) the repeated indicator approach, (2) the two-stage approach, and (3) the hybrid approach. Next, we compare the approaches using a simulation study and an empirical application in a strategic human resource management context. The findings from the simulation and the empirical application serve as a basis for recommendations and guidelines regarding the use and estimation of reflective-formative type hierarchical Latent Variable models in PLS-SEM.
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Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models
Long Range Planning, 2012Co-Authors: Jan-michael Becker, Kristina Klein, Martin WetzelsAbstract:Partial least squares structural equation modeling (PLS-SEM), or partial least squares path modeling (PLS) has enjoyed increasing popularity in recent years. In this context, the use of hierarchical Latent Variable models has allowed researchers to extend the application of PLS-SEM to more advanced and complex models. However, the attention has been mainly focused on hierarchical Latent Variable models with reflective relationships. In this manuscript, we focus on second-order hierarchical Latent Variable models that include formative relationships. First, we discuss a typology of (second-order) hierarchical Latent Variable models. Subsequently, we provide an overview of different approaches that can be used to estimate the parameters in these models: (1) the repeated indicator approach, (2) the two-stage approach, and (3) the hybrid approach. Next, we compare the approaches using a simulation study and an empirical application in a strategic human resource management context. The findings from the simulation and the empirical application serve as a basis for recommendations and guidelines regarding the use and estimation of reflective-formative type hierarchical Latent Variable models in PLS-SEM. © 2012 Elsevier Ltd.
Neil D Lawrence - One of the best experts on this subject based on the ideXlab platform.
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bayesian gaussian process Latent Variable model
International Conference on Artificial Intelligence and Statistics, 2010Co-Authors: Michalis K Titsias, Neil D LawrenceAbstract:We introduce a variational inference framework for training the Gaussian process Latent Variable model and thus performing Bayesian nonlinear dimensionality reduction. This method allows us to variationally integrate out the input Variables of the Gaussian process and compute a lower bound on the exact marginal likelihood of the nonlinear Latent Variable model. The maximization of the variational lower bound provides a Bayesian training procedure that is robust to overfitting and can automatically select the dimensionality of the nonlinear Latent space. We demonstrate our method on real world datasets. The focus in this paper is on dimensionality reduction problems, but the methodology is more general. For example, our algorithm is immediately applicable for training Gaussian process models in the presence of missing or uncertain inputs.
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topologically constrained Latent Variable models
International Conference on Machine Learning, 2008Co-Authors: Raquel Urtasun, Trevor Darrell, David J Fleet, Andreas Geiger, Jovan Popovic, Neil D LawrenceAbstract:In dimensionality reduction approaches, the data are typically embedded in a Euclidean Latent space. However for some data sets this is inappropriate. For example, in human motion data we expect Latent spaces that are cylindrical or a toroidal, that are poorly captured with a Euclidean space. In this paper, we present a range of approaches for embedding data in a non-Euclidean Latent space. Our focus is the Gaussian Process Latent Variable model. In the context of human motion modeling this allows us to (a) learn models with interpretable Latent directions enabling, for example, style/content separation, and (b) generalise beyond the data set enabling us to learn transitions between motion styles even though such transitions are not present in the data.
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hierarchical gaussian process Latent Variable models
International Conference on Machine Learning, 2007Co-Authors: Neil D Lawrence, Andrew J MooreAbstract:The Gaussian process Latent Variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.
Kristina Klein - One of the best experts on this subject based on the ideXlab platform.
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hierarchical Latent Variable models in pls sem guidelines for using reflective formative type models
Long Range Planning, 2012Co-Authors: Jan-michael Becker, Kristina Klein, Martin WetzelsAbstract:Partial least squares structural equation modeling (PLS-SEM), or partial least squares path modeling (PLS) has enjoyed increasing popularity in recent years. In this context, the use of hierarchical Latent Variable models has allowed researchers to extend the application of PLS-SEM to more advanced and complex models. However, the attention has been mainly focused on hierarchical Latent Variable models with reflective relationships. In this manuscript, we focus on second-order hierarchical Latent Variable models that include formative relationships. First, we discuss a typology of (second-order) hierarchical Latent Variable models. Subsequently, we provide an overview of different approaches that can be used to estimate the parameters in these models: (1) the repeated indicator approach, (2) the two-stage approach, and (3) the hybrid approach. Next, we compare the approaches using a simulation study and an empirical application in a strategic human resource management context. The findings from the simulation and the empirical application serve as a basis for recommendations and guidelines regarding the use and estimation of reflective-formative type hierarchical Latent Variable models in PLS-SEM.
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Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models
Long Range Planning, 2012Co-Authors: Jan-michael Becker, Kristina Klein, Martin WetzelsAbstract:Partial least squares structural equation modeling (PLS-SEM), or partial least squares path modeling (PLS) has enjoyed increasing popularity in recent years. In this context, the use of hierarchical Latent Variable models has allowed researchers to extend the application of PLS-SEM to more advanced and complex models. However, the attention has been mainly focused on hierarchical Latent Variable models with reflective relationships. In this manuscript, we focus on second-order hierarchical Latent Variable models that include formative relationships. First, we discuss a typology of (second-order) hierarchical Latent Variable models. Subsequently, we provide an overview of different approaches that can be used to estimate the parameters in these models: (1) the repeated indicator approach, (2) the two-stage approach, and (3) the hybrid approach. Next, we compare the approaches using a simulation study and an empirical application in a strategic human resource management context. The findings from the simulation and the empirical application serve as a basis for recommendations and guidelines regarding the use and estimation of reflective-formative type hierarchical Latent Variable models in PLS-SEM. © 2012 Elsevier Ltd.
