Semiparametric latent variable regression models for spatiotemporal modelling of mobile source particles in the greater Boston area

2007 ◽  
Vol 56 (2) ◽  
pp. 183-209 ◽  
Author(s):  
Alexandros Gryparis ◽  
Brent A. Coull ◽  
Joel Schwartz ◽  
Helen H. Suh
Keyword(s):  
Latent Variable ◽  
Regression Models ◽  
Mobile Source ◽  
Spatiotemporal Modelling ◽  
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.

Process-Quality Monitoring Using Semi-supervised Probability Latent Variable Regression Models

2014 ◽  
Vol 47 (3) ◽  
pp. 8272-8277 ◽  
Author(s):  
Le Zhou ◽  
Zhihuan Song ◽  
Junghui Chen ◽  
Zhiqiang Ge ◽  
Zhao Li
Keyword(s):  
Latent Variable ◽  
Regression Models ◽  
Process Quality ◽  
Quality Monitoring ◽  
Latent Variable Regression

Variable importance in latent variable regression models

2014 ◽  
Vol 28 (8) ◽  
pp. 615-622 ◽  
Author(s):  
Olav M. Kvalheim ◽  
Reidar Arneberg ◽  
Olav Bleie ◽  
Tarja Rajalahti ◽  
Age K. Smilde ◽  
...  
Keyword(s):  
Latent Variable ◽  
Regression Models ◽  
Variable Importance ◽  
Latent Variable Regression

Modelling of a Stochastic Spatiotemporal Variable in Transport Domain

2021 ◽  
Vol 03 (01) ◽  
pp. 25-31
Author(s):  
Peter Krammer ◽  
Marcel Kvassay ◽  
Ladislav Hluchý
Keyword(s):  
Regression Models ◽  
Cross Validation ◽  
Distribution Characteristics ◽  
Spatiotemporal Modelling ◽  
Bike Sharing ◽  
Day Of The Week ◽  
Irregular Variation ◽  
Fold Cross Validation ◽  
Transport Domain ◽  
Cumulative Sums

In this article, building on our previous work, we engage in spatiotemporal modelling of transport demand in the Montreal metropolitan area over the period of six years. We employ classical machine learning and regression models, which predict bike-sharing demand in the form of daily cumulative sums of bike trips for each considered docking station. Hourly estimates of demand are then determined by considering the statistical distribution of demand across individual hours of an average day. In order to capture seasonal and other regular variation of demand, longer-term distribution characteristics of bike trips, such as their average number falling on each day of the week, month of the year, etc., were also used as input attributes. We initially conjectured that weather would be an important source of irregular variation in bike-sharing demand, and subsequently included several available meteorological variables in our models. We validated our models by Hold-Out and 10-Fold Cross-Validation, with encouraging results.

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