Non-Equivalence of Measurement in Latent Variable Modelling of Multigroup Data: A Sensitivity Analysis

2013 ◽  
Author(s):  
Jouni Kuha ◽  
Irini Moustaki
Keyword(s):  
Sensitivity Analysis ◽  
Latent Variable ◽  
Latent Variable Modelling ◽  
Multigroup Data

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

Experiences in batch trajectory alignment for pharmaceutical process improvement through multivariate latent variable modelling

2011 ◽  
Vol 21 (10) ◽  
pp. 1370-1377 ◽  
Author(s):  
Salvador García-Muñoz ◽  
Mark Polizzi ◽  
Andrew Prpich ◽  
Cathal Strain ◽  
Adam Lalonde ◽  
...  
Keyword(s):  
Process Improvement ◽  
Latent Variable ◽  
Latent Variable Modelling

Evaluating Measurement Invariance in Categorical Data Latent Variable Models with the EPC-Interest

2015 ◽  
Vol 23 (4) ◽  
pp. 550-563 ◽  
Author(s):  
Daniel L. Oberski ◽  
Jeroen K. Vermunt ◽  
Guy B. D. Moors
Keyword(s):  
Social Sciences ◽  
Sensitivity Analysis ◽  
Measurement Invariance ◽  
Categorical Data ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Continuous Data ◽  
External Variable ◽  
The Social ◽  
The World

Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of “measurement invariance” may, for example, confound true differences across countries in postmaterialism with measurement differences. To deal with this problem, researchers commonly aim at “partial measurement invariance” that is, to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the “EPC-interest” was recently introduced for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-interest to latent variable models for categorical data and demonstrates its use in example analyses of U.S. Senate votes as well as respondent rankings of postmaterialism values in the World Values Study.

scholarly journals Effects of cumulative trauma load on long-term trajectories of life satisfaction and health in a population-based study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Livia Sacchi ◽  
Mariia Merzhvynska ◽  
Mareike Augsburger
Keyword(s):  
Life Satisfaction ◽  
Latent Variable ◽  
Traumatic Events ◽  
Well Being ◽  
Functional Adaptation ◽  
Future Research ◽  
Cumulative Trauma ◽  
Latent Variable Modelling ◽  
Latent Trajectories

Abstract Background Lifetime traumatic events are known to have a detrimental long-term impact on both mental and physical health. Yet, heterogeneity in the stress response regarding well-being in adults is not well understood. This study investigates effects of cumulative trauma on latent trajectories of two indices of well-being, subjective health and life satisfaction in a large representative sample by means of latent variable modelling techniques. Methods Data from the pairfam study wave 2–9, a longitudinal representative survey was used (N = 10,825). Individuals reported on lifetime trauma type exposure on wave 7 and indicated levels of life satisfaction and health at each wave. Different types of latent Variable Mixture Models were applied in an iterative fashion. Conditional models investigated effects of cumulative trauma load. Results The best fitting model indicated three latent trajectories for life, and four for health, respectively. Trauma load significantly predicted class membership: Higher exposure was associated with non-stable trajectories for both indices but followed complex patterns of both improving and decreasing life satisfaction and health. Trauma load also explained variability within classes. Conclusions The current study expands on evidence to the long-term development of health and life satisfaction in response to traumatic events from a latent variable modelling perspective. Besides detrimental effect, it also points to functional adaptation after initial decline and increased well-being associated with trauma exposure. Thus, response to traumatic stress is marked by great heterogeneity. Future research should focus on variables beyond exposure to trauma that can further identify individuals prone to trajectories of declining well-being.

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