Statistical Modeling for Degradation Data

Chen, Ding-geng (EDT); Lio, Yuhlong (EDT); Ng, Hon Keung Tony (EDT); Tsai, Tzong-ru (EDT)

ISBN 10: 9811051933 / ISBN 13: 9789811051937

Verlag: Springer, 2017

Beschreibung

Unread book in perfect condition. Bestandsnummer des Verkäufers 29470447

Bibliografische Details

Titel: Statistical Modeling for Degradation Data

Verlag: Springer

Erscheinungsdatum: 2017

Einband: Hardcover

Zustand: As New

Über diesen Titel

Inhaltsangabe:

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. <BR><BR>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. <BR><BR><BR>

Reseña del editor:

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.