Latent variable model

About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.

Estimation of generalized linear latent variable models

Abstract: Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M-estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.

Multi-conditional Latent Variable Model for Joint Facial Action Unit Detection

Abstract: We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units. In our approach we exploit the structure-discovery capabilities of generative models such as Gaussian processes, and the discriminative power of classifiers such as logistic function. This leads to superior performance compared to existing classifiers for the target task that exploit either the discriminative or generative property, but not both. The model learning is performed via an efficient, newly proposed Bayesian learning strategy based on Monte Carlo sampling. Consequently, the learned model is robust to data overfitting, regardless of the number of both input features and jointly estimated facial action units. Extensive qualitative and quantitative experimental evaluations are performed on three publicly available datasets (CK+, Shoulder-pain and DISFA). We show that the proposed model outperforms the state-of-the-art methods for the target task on (i) feature fusion, and (ii) multiple facial action unit detection.

Joint modelling of mixed outcome types using latent variables.

Abstract: After a brief review of the use of latent variables to accommodate the correlation among multiple outcomes of mixed types, through theoretical and numerical calculation, the consequences of such a construction are quantified. The effects of including latent variables on marginal inference in these models are contrasted with the situation for jointly normal outcomes. A simulation study illustrates the efficiency and reduction in bias gains possible in using joint models, and analysis of an example from the field of osteoarthritis illustrates potential practical differences.

[Principles & Practice of Structural Equation Modelling]

Abstract: Structural equation modeling (SEM) is one of the most rapidly growing analytic techniques in use today. Proponents of the approach have virtually declared the advent of a statistical revolution, while skeptics worry about the widespread misuse of complex and often poorly understood analytic methods. Despite the growing interest in and use of structural equation models, few individuals using these techniques have benefitted from any formal training. Indeed, most graduate programs provide no courses on SEM. Individuals interested in acquiring skills in this technique must eider attend expensive training seminars or plow through technical books and manuals on their own.The two new books under renew are therefore timely. Both are valuable, but differ in important ways. Kevin Kelloway's book is directed at the researcher with little knowledge of structural equation modeling and is intricately linked to one of the more popular structural equation modeling programs, LISREL. For researchers keen to begin analyzing data quickly, this book is an invaluable resource that will speed one's introduction to SEM.On the other hand, the volume written by Rex Kline represents one of the most comprehensive of available introductions to the application, execution, and interpretation of this technique. The book is written for both students and researchers who do not have extensive quantitative background. It is especially attentive to quantitative issues common to most structural equation applications.Kelloway's book is designed for the researcher unfamiliar with structural equation modeling and structural equation software. Chapter 1 provides a brief overview of the book and differentiates among historical concepts such as path analysis and latent variable model. Although the focus of the book is on using LISREL, the book offers two of the most clearly written and concise introductory chapters on SEM that I have ever read. They provide an ideal introduction to the relevant basic concepts of the technique.The theory behind the basic steps of structural equation modeling is reviewed in Chapter 2 and includes model specification, identification, estimation, testing fit, and respecification. The author emphasizes the importance of specifying the model. Indeed, this is the fundamental step in SEM that allows researchers to test hypotheses about the relation among a number of variables, and that makes structural equation modeling an inherently confirmatory technique. How a model is specified influences other issues such as identification and testing fit. Currently, there are over 20 indices of fit computed by most programs. Chapter 3 provides an overview of three general classes of fit indices, namely those assessing absolute fit, comparative fit, and parsimonious fit. Absolute fit indices assess the ability of the specified model to reproduce accurately the manner in which observed variables actually covary. Comparative fit indices assess the ability of the proposed model to account for the observed data relative to a less complex restricted model. Parsimonious fit indices recognize that better fit is usually achieved simply by increasing the number of parameters estimated. Parsimonious fit indices compensate by evaluating the benefit achieved, given the cost of estimating additional parameters.Chapter 4, the most technical chapter of the book, explains the various algebraic components and matrices required in fitting a structural equation model. Although no understanding of the algebraic components associated with fitting a structural equation model is needed to run the most recent versions of LISREL, EQS, and AMOS, this overview is useful. Indeed, the author has prudently avoided directing the book towards "point-and-click" users. This chapter provides sufficient information to novice users to appreciate the complexity of fitting a structural equation model without discouraging them.Chapters 5, 6, and 7 are devoted to the three most common applications of SEM, namely confirmatory factor analysis, observed variable path analysis, and latent variable path analysis. …