Hon Keung Tony Ng – författare

This book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality technologies to ensure high quality products and to reduce consumer and producer risks. Numerous statistical techniques and methodologies for quality control and improvement have been developed in recent years to help resolve current product quality issues in today’s fast changing environment. Featuring contributions from top experts in the field, this book covers three major topics: statistical process control, acceptance sampling plans, and reliability testing and designs. The topics covered in the book are timely and have a high potential impact and influence to academics, scholars, students and professionals in statistics, engineering, manufacturing and health.

This book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality technologies to ensure high quality products and to reduce consumer and producer risks.

This edited collection brings together internationally recognized experts in a range of areas of statistical science to honor the contributions of the distinguished statistician, Barry C.

This edited collection brings together internationally recognized experts in a range of areas of statistical science to honor the contributions of the distinguished statistician, Barry C.

Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners.Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more.The included chapters present current methods, theories, and applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research.

Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners.Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more.The included chapters present current methods, theories, and applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research.

educational and behavioral statistics.This collection will be of interest to researchers, advanced students, and professionals in the public and private sectors who would like to learn more about latest advancements in statistical sampling, particularly those who work in educational and behavioral statistics.

educational and behavioral statistics.This collection will be of interest to researchers, advanced students, and professionals in the public and private sectors who would like to learn more about latest advancements in statistical sampling, particularly those who work in educational and behavioral statistics.

?This book presents a critical overview of statistical fiber bundle models, including existing models and potential new ones.

This book provides a systematic treatment of efficient methods for modeling, analyzing, and designing degradation tests, with particular emphasis on stochastic-process-based semiparametric and nonparametric approaches motivated by practical applications. Statistical Degradation Data Analysis: Semiparametric and Nonparametric Stochastic Process Approaches compares parametric, semiparametric, and nonparametric methods through Monte Carlo simulation studies and real data examples, and demonstrates how these methodologies can be applied across a range of disciplines. The book also discusses extensions and open problems in this area.In engineering and the sciences, degradation refers to the gradual and irreversible decline in the performance, reliability, or remaining life of a system or asset. Because many systems are equipped with sensors that collect degradation measurements over time, statistical degradation modeling plays an important role in understanding the evolution of such processes and supporting reliability assessment.A common approach to degradation data analysis is stochastic process modeling. Classical models such as the Wiener, gamma, and inverse Gaussian processes have been widely studied and applied. However, these parametric models require specific assumptions on the distributions of degradation increments and may perform poorly when those assumptions are violated. To address this limitation, semiparametric and nonparametric methods, which rely on fewer distributional assumptions, can provide more robust and reliable alternatives.This book is intended for senior undergraduates, graduate students, researchers, and practitioners. It can also serve as a reference for courses in lifetime data analysis or reliability engineering. Computer programs for numerical examples are provided to facilitate replication and practical implementation.

This volume presents an eclectic mix of original research articles in areas covering the analysis of ordered data, stochastic modeling and biostatistics. These areas were featured in a conference held at the University of Texas at Dallas from March 7 to 9, 2014 in honor of Professor H. N. Nagaraja’s 60th birthday and his distinguished contributions to statistics. The articles were written by leading experts who were invited to contribute to the volume from among the conference participants. The volume is intended for all researchers with an interest in order statistics, distribution theory, analysis of censored data, stochastic modeling, time series analysis, and statistical methods for the health sciences, including statistical genetics.

The volume is intended for all researchers with an interest in order statistics, distribution theory, analysis of censored data, stochastic modeling, time series analysis, and statistical methods for the health sciences, including statistical genetics.

This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines such as reliability, public health sciences, and finance. For example, information on products’ reliability can be obtained by analyzing degradation data. In addition, statistical modeling and inference techniques have been developed on the basis of different degradation measures. The book brings together experts engaged in statistical modeling and inference, presenting and discussing important recent advances in degradation data analysis and related applications. The topics covered are timely and have considerable potential to impact both statistics and reliability engineering.