Hybrid Censoring Know-How: Models, Methods and Applications focuses on hybrid censoring, an important topic in censoring methodology with numerous applications. The readers will find information on the significance of censored data in theoretical and applied contexts, and descriptions of extensive data sets from life-testing experiments where these forms of data naturally occur. The existing literature on censoring methodology, life-testing procedures, and lifetime data analysis provides only hybrid censoring schemes, with little information about hybrid censoring methodologies, ideas, and statistical inferential methods. This book fills that gap, featuring statistical tools applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography. - Presents many numerical examples to adequately illustrate all inferential methods discussed - Mentions some open problems and possible directions for future work - Reviews developments on Type-II and Type-I HCS, including the most recent research and trends - Explains why hybrid censored sampling is important in practice - Provides details about the use of HCS under different settings and on various designs of HCS - Describes the use of hybrid censoring in other reliability applications such as reliability sampling plans, step-stress testing, and quality control
The goal of this book is to present a modeling framework for the Virtual Organization that is focused on process composition. This framework uses Predicate Calculus Knowledge Bases. Petri Net-based modeling is also discussed. In this context, a Data Mining model is proposed, using a fuzzy mathematical approach, aiming to discover knowledge. A Knowledge-Based framework has been proposed in order to present an all-inclusive knowledge store for static and dynamic properties. Toward this direction, a Knowledge Base is created, and inferences are arrived at. This book features an advisory tool for Mergers and Acquisitions of Organizations using the Fuzzy Data Mining Framework and highlights the novelty of a Knowledge-Based Service-Oriented Architecture approach and development of an Enterprise Architectural model using AI that serves a wide audience. Students of Strategic Management in business schools and postgraduate programs in technology institutes seeking application areas of AI and Data Mining, as well as business/technology professionals in organizations aiming to create value through Mergers and Acquisitions and elsewhere, will benefit from the reading of this book.
Statistical Computing: Existing Methods and Recent Developments attempts to provide a state of the art account of existing methods and recent developments in the so called new field of Statistical Computing. Fourteen different chapters deal with a wide range of topics. This includes introductory topics such as the basic numerical analysis methods, random number generation, graphical techniques used in statistical data analysis and other areas. It also covers the more specialized techniques such as the EM algorithm, genetic algorithms, nonparametric smoothing techniques, resampling methods, and artificial neural network models, to name a few. In addition, the volume also deals with the computational issues involved in the analysis of mixture models, adaptive designs, weighted distributions, and statistical signal processing, topics which are unlikely to be covered in a standard text on Statistical Computing.
This work is on biometric data indexing for large-scale identification systems with a focus on different biometrics data indexing methods. It provides state-of-the-art coverage including different biometric traits, together with the pros and cons for each. Discussion of different multimodal fusion strategies are also included.
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