A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon
Disease screening and disease surveillance (DSDS) constitute two critical areas in public health, each presenting distinctive challenges primarily due to their sequential decision-making nature and complex data structures. Statistical Methods for Dynamic Disease Screening and Spatio-Temporal Disease Surveillance explores numerous recent analytic methodologies that enhance traditional techniques. The author, a prominent researcher specializing in innovative sequential decision-making techniques, demonstrates how these novel methods effectively address the challenges of DSDS. After a concise introduction that lays the groundwork for comprehending the challenges inherent in DSDS, the book delves into fundamental statistical concepts and methods relevant to DSDS. This includes exploration of statistical process control (SPC) charts specifically crafted for sequential decision-making purposes. The subsequent chapters systematically outline recent advancements in dynamic screening system (DySS) methods, fine-tuned for effective disease screening. Additionally, the text covers both traditional and contemporary analytic methods for disease surveillance. It further introduces two recently developed R packages designed for implementing DySS methods and spatio-temporal disease surveillance techniques pioneered by the author's research team. Features • Presents Recent Analytic Methods for DSDS: The book introduces analytic methods for DSDS based on SPC charts. These methods effectively utilize all historical data, accommodating the complex data structure inherent in sequential decision-making processes. • Introduces Recent R Packages: Two recent R packages, DySS and SpTe2M, are introduced. The book not only presents these packages but also demonstrates key DSDS methods using them. • Examines Recent Research Results: The text delves into the latest research findings across various domains, including dynamic disease screening, nonparametric spatio-temporal data modeling and monitoring, and spatio-temporal disease surveillance. • Accessible Description of Methods: Major methods are described in a manner accessible to individuals without advanced knowledge in mathematics and statistics. The goal is to facilitate a clear understanding of ideas and easy implementation. • Real-Data Examples: To aid comprehension, the book provides several real-data examples illustrating key concepts and methods. • Hands-on Exercises: Each chapter includes exercises to encourage hands-on practice, allowing readers to engage directly with the presented methods.
The first text to bridge the gap between image processing andjump regression analysis Recent statistical tools developed to estimate jump curves andsurfaces have broad applications, specifically in the area of imageprocessing. Often, significant differences in technicalterminologies make communication between the disciplines of imageprocessing and jump regression analysis difficult. Ineasy-to-understand language, Image Processing and JumpRegression Analysis builds a bridge between the worlds ofcomputer graphics and statistics by addressing both the connectionsand the differences between these two disciplines. The authorprovides a systematic analysis of the methodology behindnonparametric jump regression analysis by outlining procedures thatare easy to use, simple to compute, and have proven statisticaltheory behind them. Key topics include: Conventional smoothing procedures Estimation of jump regression curves Estimation of jump location curves of regression surfaces Jump-preserving surface reconstruction based on localsmoothing Edge detection in image processing Edge-preserving image restoration With mathematical proofs kept to a minimum, this book isuniquely accessible to a broad readership. It may be used as aprimary text in nonparametric regression analysis and imageprocessing as well as a reference guide for academicians andindustry professionals focused on image processing or curve/surfaceestimation.
A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon
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