Enhancing Model Precision: Advanced Solutions for Common Challenges in Latent Variable Modeling

The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders. As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches that can provide a better framework for conceptualizing mental disorders. Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward. (PsycINFO Database Record

Recently, there has been a focus on drawing progress on representation learning to obtain more identifiable and interpretable latent representations for spike trains, which helps analyze neural population activity and understand neural mechanisms. Most existing deep generative models adopt carefully designed constraints to capture meaningful latent representations. For neural data involving navigation in cognitive space, based on insights from studies on cognitive maps, we argue that the good representations should reflect such directional nature. Due to manifold mismatch, models utilizing the Euclidean space learn a distorted geometric structure that is difficult to interpret. In the present work, we explore capturing the directional nature in a simpler yet more efficient way by introducing hyperspherical neural latent variable models (SNLVM). SNLVM is an improved deep latent variable model modeling neural activity and behavioral variables simultaneously with hyperspherical latent space. It bridges cognitive maps and latent variable models. We conduct experiments on modeling a static unidirectional task. The results show that while SNLVM has competitive performance, a hyperspherical prior naturally provides more informative and significantly better latent structures that can be interpreted as spatial cognitive maps.

Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd.

Latent variable models (LVMs) are commonly used to capture the underlying dependencies, patterns, and hidden structures in observed data. Source duplication is a by-product of the data Hankelisation pre-processing step common to single-channel LVM applications, which hinders practical LVM utilisation. In this article, a Python package titled spectrally-regularised-LVMs is presented. The proposed package addresses the source duplication issue by adding a novel spectral regularisation term. This package provides a framework for spectral regularisation in single-channel LVM applications, thereby making it easier to investigate and utilise LVMs with spectral regularisation. This is achieved via symbolic or explicit representations of potential LVM objective functions, which are incorporated into a framework that uses spectral regularisation during the LVM parameter estimation process. This package aims to provide a consistent linear LVM optimisation framework incorporating spectral regularisation and caters to single-channel time-series applications.

Extending the traditional discrete choice model by incorporating latent psychological factors can help to better understand the individual’s decision-making process and therefore to yield more reliable part-worth estimates and market share predictions. Several integrated choice and latent variable (ICLV) models which merge the conditional logit model with a structural equation model exist in the literature. They assume homogeneity in the part-worths and use latent variables to model the heterogeneity among the respondents. This paper starts from the mixed logit model that describes the heterogeneity in the part-worths and uses the latent variables to decrease the unexplained part of the heterogeneity. The empirical study presented here shows these ICLV models perform very well with respect to model fit and prediction.

In multilevel modeling, one often distinguishes between macro-micro and micro-macro situations. In a macro-micro multilevel situation, a dependent variable measured at the lower level is predicted or explained by variables measured at that lower or a higher level. In a micro-macro multilevel situation, a dependent variable defined at the higher group level is predicted or explained on the basis of independent variables measured at the lower individual level. Up until now, multilevel methodology has mainly focused on macro-micro multilevel situations. In this article, a latent variable model is proposed for analyzing data from micro-macro situations. It is shown that regression analyses carried out at the aggregated level result in biased parameter estimates. A method that uses the best linear unbiased predictors of the group means is shown to yield unbiased estimates of the parameters.

Although topic models designed for textual collections annotated with geographical meta-data have been previously shown to be effective at capturing vocabulary preferences of people living in different geographical regions, little is known about their utility for information retrieval in general or microblog retrieval in particular. In this work, we propose simple and scalable geographical latent variable generative models and a method to improve the accuracy of retrieval from collections of geo-tagged documents through document expansion that is based on the topics identified by the proposed models. In particular, we experimentally compare the retrieval effectiveness of four geographical latent variable models: two geographical variants of post-hoc LDA, latent variable model without hidden topics and a topic model that can separate background from geographically-specific topics. The experiments conducted on TREC microblog datasets demonstrate significant improvement in search accuracy of the proposed method over both the traditional probabilistic retrieval model and retrieval models utilizing geographical post-hoc variants of LDA.

It is common to model responses to surveys within latent variable frameworks (e.g., item response theory [IRT], confirmatory factor analysis [CFA]) and use model fit indices to evaluate model-data congruence. Unfortunately, research shows that people occasionally engage in careless responding (CR) when completing online surveys. While CR has the potential to negatively impact model fit, this issue has not been systematically explored. To better understand the CR-fit linkage, two studies were conducted. In study 1, participants' response behaviors were experimentally shaped and used to embed aspects of a comprehensive simulation (study 2) with empirically informed data. For this simulation, 144 unique conditions (which varied the sample size, number of items, CR prevalence, CR severity, and CR type), two latent variable models (IRT, CFA), and six model fit indices (χ2, RMSEA, SRMSR [CFA] and M2, RMSEA, SRMSR [IRT]), were examined. The results indicated that CR deteriorates model fit under most circumstances, though these effects are nuanced, variable, and contingent on many factors. These findings can be leveraged by researchers and practitioners to improve survey methods, obtain more accurate survey results, develop more precise theories, and enable more justifiable data-driven decisions.

Latent Variable Models in the Investigation of Salary Discrimination: Theory and Practice

Latent trait models have long been used in the social science literature for studying variables that can only be measured indirectly through multiple items. However, such models are also very useful in accounting for correlation in multivariate and longitudinal data, particularly when outcomes have mixed measurement scales. Bayesian methods implemented with Markov chain Monte Carlo provide a flexible framework for routine fitting of a broad class of latent variable (LV) models, including very general structural equation models. However, in considering LV models, a number of challenging issues arise, including identifiability, confounding between the mean and variance, uncertainty in different aspects of the model, and difficulty in computation. Motivated by the problem of modelling multidimensional longitudinal data, this article reviews the recent literature, provides some recommendations and highlights areas in need of additional research, focusing on methods for model uncertainty.

Measurement of non-random attrition effects on mobility rates using trip diaries data

In the past years, latent variable models have played an important role in various industrial AI systems, among which quality prediction is one of the most representative applications. Inspired by the idea of deep learning, those basic latent variable models have been extended to deep forms, based on which the quality prediction performance has been significantly improved. However, different latent variable models have their own strengths and weaknesses, a model works well under one scenario might not provide satisfactory performance under another. The motivation of this article is based on the viewpoint of information fusion and ensemble learning for heterogeneous latent variable models. Particularly, a collaborative deep learning and model fusion framework is formulated for the purpose of industrial quality prediction. In the first stage of the framework, collaborative layer-by-layer feature extractions are implemented among different latent variable models, through which different patterns of latent variables are identified in different layers of the deep model. Then, in the second stage, an ensemble regression modeling strategy is proposed to fuse the quality prediction results from different latent variable models, which is based on a well-designed data description method. Two real industrial examples are used for performance evaluation of the proposed method, based on which we can observe that information fusions in terms of both collaborative layer-by-layer feature extraction and heterogeneous model ensemble have positive effects in improving prediction accuracy and stability.

Recent Developments in Approximated-Likelihood Inference Methods for Latent Variable Models

Bartholomew (1980, 1984a) has laid down a foundation for factor analysis based on latent variable models. Shea (1984, 1985) has provided computer programs for estimating one‐factor latent variable models when responses are binary or polytomous variables. However, the programs have limitations on number of variables and the sample size, which limits their applicability, especially when variables are polytomous. However, the more important limitation is that it is not possible to use two‐factor models when one‐factor models are not adequate.Here, we have gone further and successfully obtained maximum likelihood estimates for both the one‐factor and two‐factor latent variable models for binary and polytomous variables. The new algorithm is much faster because of careful and efficient programming. More importantly, generalization to high latent domensions is straightforward. Formal hypothesis testing is very difficult when variables are very large in number. However, we have shown in our examples that some interesting graphical interpretations can be made.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti etal. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.