Robust Mixture Bayesian Latent Variable Regression with Structural Sparsity and Application to Inferential Sensing of Quality Variables

2020 ◽  
Vol 59 (50) ◽  
pp. 21822-21840
Author(s):  
Lin Luo ◽  
Lei Xie ◽  
Hongye Su
Keyword(s):  
Latent Variable ◽  
Latent Variable Regression

scholarly journals Integrated Multiscale Latent Variable Regression and Application to Distillation Columns

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Muddu Madakyaru ◽  
Mohamed N. Nounou ◽  
Hazem N. Nounou
Keyword(s):  
Feature Extraction ◽  
Latent Variable ◽  
Prediction Accuracy ◽  
Distillation Column ◽  
Selection Criterion ◽  
Process Data ◽  
Distillation Columns ◽  
Latent Variable Regression ◽  
Multiscale Filtering ◽  
Modeling Algorithm

Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions), which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.

Latent Variable Regression Four-Level Hierarchical Model Using Multisite Multiple-Cohort Longitudinal Data

2019 ◽  
Vol 44 (5) ◽  
pp. 597-624 ◽  
Author(s):  
Kilchan Choi ◽  
Jinok Kim
Keyword(s):  
Longitudinal Data ◽  
Hierarchical Model ◽  
Latent Variable ◽  
Student Growth ◽  
Individual Student ◽  
Assessment Scores ◽  
Student Academic Performance ◽  
Latent Variable Regression ◽  
Fully Bayesian ◽  
Types Of Change

This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual assessment scores over grades for students who are nested within cohorts within schools, the LVR-HM4 attempts to simultaneously model two types of change, arising from individual student over grades, and successive cohorts in the same grade over years. In addition, as an extension of Choi and Seltzer, the LVR coefficients, that is, gap-in-time parameter, capturing the relationships between initial status and rates of changes within each cohort and school, help bring to light the distribution of student growth and differences in the distribution over different cohorts within schools. Advantages associated with the LVR-HM4 can be highlighted in studies on monitoring school performance or evaluations of policies and practices that may target different aspects of student academic performance such as initial status, growth, or gap over time in schools.

Interpretation of regression coefficients under a latent variable regression model

2001 ◽  
Vol 15 (4) ◽  
pp. 265-284 ◽  
Author(s):  
Alison J. Burnham ◽  
John F. MacGregor ◽  
Roman Viveros
Keyword(s):  
Regression Model ◽  
Latent Variable ◽  
Regression Coefficients ◽  
Latent Variable Regression

scholarly journals Multiscale Latent Variable Regression

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamed N. Nounou ◽  
Hazem N. Nounou
Keyword(s):  
Latent Variable ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Multiple Scales ◽  
Principal Component Regression ◽  
Principal Component ◽  
Measurement Noise ◽  
Process Data ◽  
Latent Variable Regression ◽  
Modeling Algorithm

Multiscale wavelet-based representation of data has been shown to be a powerful tool in feature extraction from practical process data. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of some of the latent variable regression models, such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression (MSLVR) modeling algorithm. The idea is to decompose the input-output data at multiple scales using wavelet and scaling functions, construct multiple latent variable regression models at multiple scales using the scaled signal approximations of the data and then using cross-validation, and select among all MSLVR models the model which best describes the process. The main advantage of the MSLVR modeling algorithm is that it inherently accounts for the presence of measurement noise in the data by the application of the low-pass filters used in multiscale decomposition, which in turn improves the model robustness to measurement noise and enhances its prediction accuracy. The advantages of the developed MSLVR modeling algorithm are demonstrated using a simulated inferential model which predicts the distillate composition from measurements of some of the trays' temperatures.

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