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Monitoring profiles in multistage processes using the multivariate multiple regression model

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2022年第7期38卷 3437-3450页

作者： Park, Changsoon Lee, Jaeheon Chung Ang Univ Dept Appl Stat 84 Heukseok Ro Seoul 06974 South Korea State Univ New York Korea SUNYKOREA Dept Appl Math & Stat Incheon South Korea

Most recent production processes comprise multiple stages, where the final quality of products is determined by the upstream effects of output quality at each stage. Moreover, the quality of each stage is characterized by a functional relationship, referred to as a profile, between input variables and output quality variables. Therefore, the effective profile monitoring of the multiple stages is crucial in maintaining and improving the final output quality. A multistage process is separated into a series of single-stage processes, and each single-stage process is treated as a profile structure. In each stage, we implement the multivariate multiple linear regression (MMLR) model using the orthogonal design coding. The coefficient estimators of the MMLR are obtained as a matrix, whose column vectors indicate the coefficient vectors for given output quality variables and row vectors indicate the coefficients of output quality variables for given regression coefficients. Since the use of orthogonal design coding makes the regression coefficient estimator vectors for the intercept and the input variables mutually independent, it is possible to monitor the process using Hotelling's T-2 charts separately. We explain the process of estimating and monitoring the regression coefficients from Phase I samples, and evaluate the performance of the proposed procedure in Phase II.

关键词： Hotelling's T(2)chart multistage process multivariate multiple linear regression orthogonal design coding profile monitoring statistical process control

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Investigating the impacts of traffic signal timing on the urban traffic-related particulate matters (PMs): A case study in Xi'an, China

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ATMOSPHERIC POLLUTION RESEARCH 2021年第2期12卷 1-9页

作者： Song, Jianhua Ren, Gang Qiu, Zhaowen Lv, Huitao Southeast Univ Jiangsu Key Lab Urban ITS Jiangsu Prov Collaborat Innovat Ctr Modern Urban Sch Transportat Rd 2 Nanjing 211189 Jiangsu Peoples R China Changan Univ Sch Automobile Changan Rd Xian 710064 Shaanxi Peoples R China

Characterizing the relationship between signal timing and particulate matter (PM) is important for a sustainable traffic signal control and accurate exposure assessment at emission hotspot locations. The impact of traffic signal timing and meteorological variables on size-resolved PM in the 0.25-10 mu m range at the signalized intersection was investigated. The PMs, particle number concentrations (PNCs), and particle mass concentrations (PMCs) in the given size distribution were obtained at the signalized intersection in Xi'an. A comprehensive analysis method involving statistical analysis, regression analysis, simulation was used. The results demonstrated that the fine particles (PMC0.25-2.5 and PNC0.25-2.5) have a positive relationship with wind speed and vehicles per cycle, and a size ranges of PM, ranging from 2.5 to 10 mu m, was greatly affected by those factors. Moreover, the regression findings via multivariate multiple linear regression (MMLR) showed that the coefficients of independent variables (temperature, and delay) were statistically significant, which indicated that those factors enormously affect fine particles. In addition, the simulation results from the CAL3QHC indicated the benefit of pollutant reduction (PM2.5 reduced by an average of 5.3%) becomes more significant as signal timing optimization. This study serves as a demonstration of the abilities of reasonable signal timing to improve air quality and make better at the service level of the intersection.

关键词： Particle number concentrations (PNCs) Particle mass concentrations (PMCs) Autoregressive log-linear regression model multivariate multiple linear regression Signalized intersection

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A new Bayesian multivariate exponentially weighted moving average control chart for phase II monitoring of multivariate multiple linear profiles

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2019年第7期35卷 2152-2177页

作者： Yazdi, Ahmad Ahmadi Hamadani, Ali Zeinal Amiri, Amirhossein Grzegorczyk, Marco IUT Dept Ind & Syst Engn Esfahan *** Iran Shahed Univ Dept Ind Engn Fac Engn Tehran Iran Univ Groningen BI Dept Math Fac Sci & Engn Groningen Netherlands

Recently, statistical profile monitoring methods have become efficient tools for monitoring the quality of a product (or a production process) using control charts. The key idea is to describe the relationship between a response variable and a set of explanatory variables in the form of a statistical regression model, which called profile. Traditionally, those control charts are constructed with standard "frequentistic" regression models. Recently, it has been proposed to apply Bayesian regression models instead, and it has been empirically demonstrated that Bayesian regression models have the potential to perform significantly better. In this paper, we introduce a novel Bayesian multivariate exponentially weighted moving average control chart for monitoring multivariate multiple linear profiles in phase II. The key idea is to use the data from historical data sets to generate informative prior distributions for the regression models in phase II. The results of our empirical simulation studies show that the Bayesian multivariate multiple linear regression model is superior to its classical "frequentistic" counterpart in terms of the average run length. Our empirical findings are in agreement with findings reported in recently published articles. To shed more light onto the merit of the proposed Bayesian method, we carry out a sensitivity analysis, in which we investigate how the amount of phase I data influences the results. We also demonstrate the applicability and superiority of the proposed Bayesian method by a real-world application.

关键词： Bayesian modelling multivariate multiple linear regression phase II profile monitoring statistical process monitoring

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Changes in land use typology of global airports: An empirical investigation with implications for the aerotropolis concept

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JOURNAL OF TRANSPORT GEOGRAPHY 2021年 97卷 103217-103217页

作者： Kamruzzaman, Md. Aston, Laura Baker, Douglas Braun, Boris Shatu, Farjana Monash Univ Urban Planning & Design Monash Art Design & Architecture 900 Dandenong Rd Caulfield Vic 3145 Australia Queensland Univ Technol Sch Built Environm 2 George St Brisbane Qld 4000 Australia Univ Cologne Inst Geog Cologne Germany Freelance Res 16 Kenton Walk Narre Warren South Vic 3805 Australia

Aerotropolis is an urban development concept characterised by the concentration of land uses fostering economic activities around airports. However, limited empirical evidence exists about the formation of aerotropolises and their effects on the productivity of airports. This study addresses the gaps by examining changes in land use patterns around 75 major airports globally, using data extracted from Google Earth for two time periods: 2004 and 2014. A Two-Step cluster analysis technique was applied to identify natural groupings of the airports in terms of their land use patterns for both periods. Changes in cluster membership were observed between the periods to examine the process of aerotropolis formation. A multivariate multiple linear regression (MMLR) model is estimated to identify factors contributing to aerotropolis formation process. Finally, the effects of the aerotropolis formation process on airport productivity is estimated in a two-stage least square (2SLS) regression model. Cluster analysis results show that airport land side development can be classified into three groups: Island airports (surrounded by largely vacant land), Waterfront airports (adjacent to a large body of water), and Built form airports (well-developed residential, commercial and industrial land uses). Overall, built-up areas increased by 2.1% between 2004 and 2014, and only three airports transitioned from Island to Built form - suggesting a weak evidence of the aerotropolis formation process. Developable land surrounding the airports and city population growth rate contributed significantly to this process, which consequently contributed to the productivity of airports. In conclusion, aerotropolises are a promising concept, yet unreal. It needs to be integrated with city plans to direct development, and thereby, to make it a reality.

关键词： Airport typology Land use and land cover change Aerotropolis Cluster analysis multivariate multiple linear regression Instrument variable Two-stage least square regression Airport productivity

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Semiparametric Bayesian regression via Potts Model

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2017年第2期26卷 265-274页

作者： Murua, Alejandro Quintana, Fernando A. Univ Montreal Dept Math & Stat Montreal PQ Canada Pontificia Univ Catolica Chile Fac Matemat Dept Estadist Santiago Chile

We consider Bayesian nonparametric regression through random partition models. Our approach involves the construction of a covariate-dependent prior distribution on partitions of individuals. Our goal is to use covariate information to improve predictive inference. To do so, we propose a prior on partitions based on the Potts clustering model associated with the observed covariates. This drives by covariate proximity both the formation of clusters, and the prior predictive distribution. The resulting prior model is flexible enough to support many different types of likelihood models. We focus the discussion on nonparametric regression. Implementation details are discussed for the specific case of multivariate multiple linear regression. The proposed model performs well in terms of model fitting and prediction when compared to other alternative nonparametric regression approaches. We illustrate the methodology with an application to the health status of nations at the turn of the 21st century. Supplementary materials are available online.

关键词： Clustering multivariate multiple linear regression Random partition models

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A HIERARCHICAL multivariate BAYESIAN APPROACH TO ENSEMBLE MODEL OUTPUT STATISTICS IN ATMOSPHERIC PREDICTION

A HIERARCHICAL MULTIVARIATE BAYESIAN APPROACH TO ENSEMBLE MO...

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作者： Robert D. T. Wendt NAVAL POSTGRADUATE SCHOOL

学位级别：博士

Previous research in statistical post-processing has found systematic deficiencies in deterministic forecast guidance. As a result, ensemble forecasts of sensible weather variables often manifest biased central tendencies and anomalous dispersion. In this way, the numerical weather prediction community has largely focused on upgrades to upstream model components to improve forecast performance-that is, innovations in data assimilation, governing dynamics, numerical techniques, and various parameterizations of subgrid-scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to ensemble model output statistics. This technique directly parameterizes meteorological phenomena with probability distributions that describe the intrinsic structure of observable data. Bayesian posterior beliefs in model parameter were conditioned on previous observations and dynamical predictors available outside of the parent ensemble. An adaptive variant of the random-walk Metropolis algorithm was used to complete the inference scheme with block-wise multiparameter updates. This produced calibrated multivariate posterior predictive distributions (PPD) for 24-hour forecasts of diurnal extrema in surface temperature and wind speed. These Bayesian PPDs reliably characterized forecast uncertainty and outperformed the parent ensemble and a classical least-squares approach to multivariate multiple linear regression using both measures-oriented and distributions-oriented scoring rules.

关键词： ensemble model output statistics statistical post-processing multivariate multiple linear regression Bayesian data analysis Bayesian hierarchical modeling Markov chain Monte Carlo methods Metropolis algorithm machine learning atmospheric prediction

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Exploration of correlation between environmental factors and mobility at signalized intersections

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2014年第0期32卷 24-34页

作者： Guo, Rui Zhang, Yu Univ S Florida Dept Civil & Environm Engn Tampa FL 33620 USA

Characterizing the relationship between environmental factors and mobility is critical for developing a sustainable traffic signal control system. In this study, the authors investigate the correlation of the environmental impacts of transport and mobility measurements at signalized intersections. A metamodeling-based method involving experimental design, simulations, and regression analysis was developed. The simulations, involving microscopic traffic modeling and emission estimation with an emerging emission estimator, provide the flexibility of generating cases with various intersection types, vehicle types, and other parameters such as driver behavior, fuel types, and meteorological factors. A multivariate multiple linear regression (MMLR) analysis was applied to determine the relationship between environmental and mobility measurements. Given the limitations of using the built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in this paper makes a methodological contribution. The findings of this study set up the base for extensive application of simulation optimization to sustainable traffic operations and management. Moreover, the comparison of outputs from an advanced estimator with those from the current tool recommend improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Sustainable transportation Environmental factors and traffic metrics MOVES multivariate multiple linear regression

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A multi-resolution census algorithm for calculating vortex statistics in turbulent flows

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2008年第3期57卷 293-312页

作者： Whitcher, Brandon Lee, Thomas C. M. Weiss, Jeffrey B. Hoar, Timothy J. Nychka, Douglas W. Chinese Univ Hong Kong Dept Stat Shatin Hong Kong Peoples R China Colorado State Univ Ft Collins CO 80523 USA Univ London Imperial Coll Sci Technol & Med London SW7 2AZ England Univ Colorado Boulder CO 80309 USA Natl Ctr Atmospher Res Boulder CO 80307 USA

The fundamental equations that model turbulent flow do not provide much insight into the size and shape of observed turbulent structures. We investigate the efficient and accurate representation of structures in two-dimensional turbulence by applying statistical models directly to the simulated vorticity field. Rather than extract the coherent portion of the image from the background variation, as in the classical signal-plus-noise model, we present a model for individual vortices using the non-decimated discrete wavelet transform. A template image, which is supplied by the user, provides the features to be extracted from the vorticity field. By transforming the vortex template into the wavelet domain, specific characteristics that are present in the template, such as size and symmetry, are broken down into components that are associated with spatial frequencies. multivariate multiple linear regression is used to fit the vortex template to the vorticity field in the wavelet domain. Since all levels of the template decomposition may be used to model each level in the field decomposition, the resulting model need not be identical to the template. Application to a vortex census algorithm that records quantities of interest (such as size, peak amplitude and circulation) as the vorticity field evolves is given. The multiresolution census algorithm extracts coherent structures of all shapes and sizes in simulated vorticity fields and can reproduce known physical scaling laws when processing a set of vorticity fields that evolve over time.

关键词： multivariate multiple linear regression non-decimated discrete wavelet transform penalized likelihood turbulence vorticity

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An Application in multivariate Statistical Process Control for Power Supply Calibration

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Quality Engineering 1996年第1期9卷 99-99页

作者： Mader, D. P. Glycenfer, J. J. Prins, J. Greeley Hardcopy Division Hewlett-Packard Company Greeley CO 80634 United States Test Engineering Department Advanced Energy Industries Inc. Fort Collins CO 80525 1625 Sharp Point Drive United States Worldwide Enterprise Modeling Group Motorola Manufacturing Systems Schaumburg IL 60196 United States Hewlett-Packard's Greeley H. Greeley CO United States Advanced Energy Industries South Dakota State University Colorado School of Mines Colorado State University University of Colorado Boulder CO United States University of Santa Clara CA United States Spectra-Physics Mountain View CA United States Motorola Worldwide Modeling Group Schaumburg IL United States College of Business Administration University of Northern Colorado New Jersey Institute of Technology Rutgers University New York University Motorola's Six Sigma Res. Institute IBM

This article describes an application in real-time multivariate statistical process control for the automated power calibration of a family of high-energy power supplies. Subsequent to a variance-stabilizing transformation, data for the actual power produced by each unit under test is regressed on the specified input power over the operating range. The slope and intercept from the simple linear regression are then compared to the bivariate normal distribution for slope-intercept pairs from previous using using a prediction approach based on multivariate multiple linear regression. The approach has been effective in identifying quality problems on-line, and the efficiency of the multivariate SPC procedure has decreased the total test cycle time at the automated test. Process capability results for the power calibration test are presented for the first 632 production units for the Lightning power supply family, and the results indicate strong evidence that a six-sigma level of quality has been achieved for the power calibration process.

关键词： Hotelling's T2 multivariate multiple linear regression multivariate normal distribution Power calibration Prediction ellipsoid Statistical process control

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