Optimal detection of homogeneous segment of observations in stochastic sequence

A Markov process is registered. At random moment $\theta$ the distribution of observed sequence changes. Using probability maximizing approach the optimal stopping rule for detecting the change is identified. Some explicit solution is obtained.Comment: 13 page

The interaction of lean and building information modeling in construction

Lean construction and Building Information Modeling are quite different initiatives, but both are having profound impacts on the construction industry. A rigorous analysis of the myriad specific interactions between them indicates that a synergy exists which, if properly understood in theoretical terms, can be exploited to improve construction processes beyond the degree to which it might be improved by application of either of these paradigms independently. Using a matrix that juxtaposes BIM functionalities with prescriptive lean construction principles, fifty-six interactions have been identified, all but four of which represent constructive interaction. Although evidence for the majority of these has been found, the matrix is not considered complete, but rather a framework for research to explore the degree of validity of the interactions. Construction executives, managers, designers and developers of IT systems for construction can also benefit from the framework as an aid to recognizing the potential synergies when planning their lean and BIM adoption strategies

Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays

Many statistical surveillance systems for the timely detection of outbreaks of infectious disease operate on laboratory data. Such data typically incur reporting delays between the time at which a specimen is collected for diagnostic purposes, and the time at which the results of the laboratory analysis become available. Statistical surveillance systems currently in use usually make some ad hoc adjustment for such delays, or use counts by time of report. We propose a new statistical approach that takes account of the delays explicitly, by monitoring the number of specimens identified in the current and past m time units, where m is a tuning parameter. Values expected in the absence of an outbreak are estimated from counts observed in recent years (typically 5 years). We study the method in the context of an outbreak detection system used in the United Kingdom and several other European countries. We propose a suitable test statistic for the null hypothesis that no outbreak is currently occurring. We derive its null variance, incorporating uncertainty about the estimated delay distribution. Simulations and applications to some test datasets suggest the method works well, and can improve performance over ad hoc methods in current use. Supplementary materials for this article are available online

Automated Detection of Pipe Bursts and other Events in Water Distribution Systems

Copyright 2012 by the American Society of Civil EngineersThis paper presents a new methodology for the automated near real-time detection of pipe bursts and other events which induce similar abnormal pressure/flow variations (e.g., unauthorised consumptions) at the District Metered Area (DMA) level. The new methodology makes synergistic use of several self-learning Artificial Intelligence (AI) techniques and statistical data analysis tools including wavelets for de-noising of the recorded pressure/flow signals, Artificial Neural Networks (ANNs) for the short-term forecasting of pressure/flow signal values, Statistical Process Control (SPC) techniques for short and long term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian Inference Systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (UK) with both real-life pipe burst/other events and engineered (i.e., simulated by opening fire hydrants) pipe burst events. The results obtained illustrate that it can successfully identify these events in a fast and reliable manner with a low false alarm rate

The use of Control Charts by Laypeople and Hospital Decision-Makers for Guiding Decision Making

Graphs presenting healthcare data are increasingly available to support laypeople and hospital staff's decision making. When making these decisions, hospital staff should consider the role of chance—that is, random variation. Given random variation, decision-makers must distinguish signals (sometimes called special-cause data) from noise (common-cause data). Unfortunately, many graphs do not facilitate the statistical reasoning necessary to make such distinctions. Control charts are a less commonly used type of graph that support statistical thinking by including reference lines that separate data more likely to be signals from those more likely to be noise. The current work demonstrates for whom (laypeople and hospital staff) and when (treatment and investigative decisions) control charts strengthen data-driven decision making. We present two experiments that compare people's use of control and non-control charts to make decisions between hospitals (funnel charts vs. league tables) and to monitor changes across time (run charts with control lines vs. run charts without control lines). As expected, participants more accurately identified the outlying data using a control chart than using a non-control chart, but their ability to then apply that information to more complicated questions (e.g., where should I go for treatment?, and should I investigate?) was limited. The discussion highlights some common concerns about using control charts in hospital settings

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. This is a strategic issue for industrial success in the tremendously competitive global market.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Ferrer, A. (2014). Latent Structures based-Multivariate Statistical Process Control: a paradigm shift. Quality Engineering. 26(1):72-91. https://doi.org/10.1080/08982112.2013.846093S7291261Aparisi, F., Jabaioyes, J., & Carrion, A. (1999). Statistical properties of the lsi multivariate control chart. Communications in Statistics - Theory and Methods, 28(11), 2671-2686. doi:10.1080/03610929908832445Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International, 23(5), 517-543. doi:10.1002/qre.829Bharati, M. H., & MacGregor, J. F. (1998). Multivariate Image Analysis for Real-Time Process Monitoring and Control. Industrial & Engineering Chemistry Research, 37(12), 4715-4724. doi:10.1021/ie980334lBharati, M. H., MacGregor, J. F., & Tropper, W. (2003). Softwood Lumber Grading through On-line Multivariate Image Analysis Techniques. Industrial & Engineering Chemistry Research, 42(21), 5345-5353. doi:10.1021/ie0210560Bisgaard, S. (2012). The Future of Quality Technology: From a Manufacturing to a Knowledge Economy & From Defects to Innovations. Quality Engineering, 24(1), 30-36. doi:10.1080/08982112.2011.627010Box, G. E. P. (1954). Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification. The Annals of Mathematical Statistics, 25(2), 290-302. doi:10.1214/aoms/1177728786Camacho, J., & Ferrer, A. (2012). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects. Journal of Chemometrics, 26(7), 361-373. doi:10.1002/cem.2440Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Efron, B., & Gong, G. (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician, 37(1), 36-48. doi:10.1080/00031305.1983.10483087Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Fuchs, C. (1998). Multivariate Quality Control. doi:10.1201/9781482273731Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9Helland, I. S. (1988). On the structure of partial least squares regression. Communications in Statistics - Simulation and Computation, 17(2), 581-607. doi:10.1080/03610918808812681Höskuldsson, A. (1988). PLS regression methods. Journal of Chemometrics, 2(3), 211-228. doi:10.1002/cem.1180020306Hunter, J. S. (1986). The Exponentially Weighted Moving Average. Journal of Quality Technology, 18(4), 203-210. doi:10.1080/00224065.1986.11979014Edward Jackson, J. (1985). Multivariate quality control. Communications in Statistics - Theory and Methods, 14(11), 2657-2688. doi:10.1080/03610928508829069Jackson, J. E., & Mudholkar, G. S. (1979). Control Procedures for Residuals Associated With Principal Component Analysis. Technometrics, 21(3), 341-349. doi:10.1080/00401706.1979.10489779Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Kourti, T. (2006). Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Liu, R. Y. (1995). Control Charts for Multivariate Processes. Journal of the American Statistical Association, 90(432), 1380-1387. doi:10.1080/01621459.1995.10476643Liu, R. Y., Singh, K., & Teng*, J. H. (2004). DDMA-charts: Nonparametric multivariate moving average control charts based on data depth. Allgemeines Statistisches Archiv, 88(2), 235-258. doi:10.1007/s101820400170Liu, R. Y., & Tang, J. (1996). Control Charts for Dependent and Independent Measurements Based on Bootstrap Methods. Journal of the American Statistical Association, 91(436), 1694-1700. doi:10.1080/01621459.1996.10476740LOWRY, C. A., & MONTGOMERY, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800-810. doi:10.1080/07408179508936797MacGregor, J. F. (1997). Using On-Line Process Data to Improve Quality: Challenges for Statisticians. International Statistical Review, 65(3), 309-323. doi:10.1111/j.1751-5823.1997.tb00311.xMason, R. L., Champ, C. W., Tracy, N. D., Wierda, S. J., & Young, J. C. (1997). Assessment of Multivariate Process Control Techniques. Journal of Quality Technology, 29(2), 140-143. doi:10.1080/00224065.1997.11979743Montgomery, D. C., & Woodall, W. H. (1997). A Discussion on Statistically-Based Process Monitoring and Control. Journal of Quality Technology, 29(2), 121-121. doi:10.1080/00224065.1997.11979738Nelson, P. R. C., Taylor, P. A., & MacGregor, J. F. (1996). Missing data methods in PCA and PLS: Score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems, 35(1), 45-65. doi:10.1016/s0169-7439(96)00007-xNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Prats-Montalbán, J. M., Ferrer, A., Malo, J. L., & Gorbeña, J. (2006). A comparison of different discriminant analysis techniques in a steel industry welding process. Chemometrics and Intelligent Laboratory Systems, 80(1), 109-119. doi:10.1016/j.chemolab.2005.08.005Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026Bisgaard, S., Doganaksoy, N., Fisher, N., Gunter, B., Hahn, G., Keller-McNulty, S., … Wu, C. F. J. (2008). The Future of Industrial Statistics: A Panel Discussion. Technometrics, 50(2), 103-127. doi:10.1198/004017008000000136Stoumbos, Z. G., Reynolds, M. R., Ryan, T. P., & Woodall, W. H. (2000). The State of Statistical Process Control as We Proceed into the 21st Century. Journal of the American Statistical Association, 95(451), 992-998. doi:10.1080/01621459.2000.10474292Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24(2), 88-95. doi:10.1080/00224065.1992.12015232Wierda, S. J. (1994). Multivariate statistical process control—recent results and directions for future research. Statistica Neerlandica, 48(2), 147-168. doi:10.1111/j.1467-9574.1994.tb01439.xWold, S. (1978). Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models. Technometrics, 20(4), 397-405. doi:10.1080/00401706.1978.10489693Woodall, W. H. (2000). Controversies and Contradictions in Statistical Process Control. Journal of Quality Technology, 32(4), 341-350. doi:10.1080/00224065.2000.11980013Woodall, W. H., & Montgomery, D. C. (1999). Research Issues and Ideas in Statistical Process Control. Journal of Quality Technology, 31(4), 376-386. doi:10.1080/00224065.1999.11979944Yu, H., & MacGregor, J. F. (2003). Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods. Chemometrics and Intelligent Laboratory Systems, 67(2), 125-144. doi:10.1016/s0169-7439(03)00065-0Yu, H., MacGregor, J. F., Haarsma, G., & Bourg, W. (2003). Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes. Industrial & Engineering Chemistry Research, 42(13), 3036-3044. doi:10.1021/ie020941

Technology-enhanced learning in higher education : How to enhance student engagement through blended learning

Blended learning has risen in popularity in the last two decades as it has been shown to be an effective approach for accommodating an increasingly diverse student population in higher education and enriching the learning environment by incorporating online teaching resources. Blending significant elements of the learning environment such as face-to-face, online and self-paced learning leads to better student experiences and outcomes and more efficient teaching and course management practices if combined appropriately. Hence, an appropriate systematic and dynamic approach of blended learning design is crucial for a positive outcome, starting with planning for integrating blended elements into a course and creating blended activities and implementing them. Evaluating their effectiveness and knowing in which environments they work better and improving the blended activities designed from both the student’s and instructor’s perspective are critical for the next delivery of the course. This article aims to increase awareness of higher education educators about how traditional face-to-face learning can be transformed into blended courses so as to develop student engagement with both in-class and online approaches, whilst being time effective for the instructor

Competing models of quality management and financial performance improvement.

Six competing models of quality management and financial performance improvement are hypothesized and statistically tested, using data from a survey of general managers of 288 four- and five-star hotels in Egypt and structural equation modeling. The comparative analysis of the conceptually and structurally different models suggests that financial performance can be improved when quality management is viewed holistically as a commonality of its interconnected practices (top management leadership; employee management; customer focus; supplier management; process management; quality data and reporting). Managers must therefore integrate stakeholders into design and implementation of effective quality management systems. This study: advances knowledge of the roles of alternative models of quality management in improving financial performance; deepens our understanding of the main features of a quality management system capable of enhancing organizational performance; and contributes to ongoing debates in quality and service management literature on factors that impact financial performance