Analysis of Step-Stress Models: Existing Results and Some Recent Developments describes, in detail, the step-stress models and related topics that have received significant attention in the last few years. Although two books, Bagdonavicius and Nikulin (2001) and Nelson (1990), on general accelerated life testing models are available, no specific book is available on step-stress models. Due to the importance of this particular topic, Balakrishnan (2009) provided an excellent review for exponential step-stress models. The scope of this book is much more, providing the inferential issues for different probability models, both from the frequentist and Bayesian points-of-view.- Explains the different distributions of the Cumulative Exposure Mode- Covers many different models used for step-stress analysis- Discusses Step-stress life testing under the competing or complementary risk model

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

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

Analysis of Step-Stress Models: Existing Results and Some Recent Developments describes, in detail, the step-stress models and related topics that have received significant attention in the last few years. Although two books, Bagdonavicius and Nikulin (2001) and Nelson (1990), on general accelerated life testing models are available, no specific book is available on step-stress models. Due to the importance of this particular topic, Balakrishnan (2009) provided an excellent review for exponential step-stress models. The scope of this book is much more, providing the inferential issues for different probability models, both from the frequentist and Bayesian points-of-view.

Explains the different distributions of the Cumulative Exposure Mode Covers many different models used for step-stress analysis Discusses Step-stress life testing under the competing or complementary risk model

Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research.

This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.

This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.

Analysis of Step-Stress Models: Existing Results and Some Recent Developments describes, in detail, the step-stress models and related topics that have received significant attention in the last few years. Although two books, Bagdonavicius and Nikulin (2001) and Nelson (1990), on general accelerated life testing models are available, no specific book is available on step-stress models. Due to the importance of this particular topic, Balakrishnan (2009) provided an excellent review for exponential step-stress models. The scope of this book is much more, providing the inferential issues for different probability models, both from the frequentist and Bayesian points-of-view.- Explains the different distributions of the Cumulative Exposure Mode- Covers many different models used for step-stress analysis- Discusses Step-stress life testing under the competing or complementary risk model

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

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

Analysis of Step-Stress Models: Existing Results and Some Recent Developments describes, in detail, the step-stress models and related topics that have received significant attention in the last few years. Although two books, Bagdonavicius and Nikulin (2001) and Nelson (1990), on general accelerated life testing models are available, no specific book is available on step-stress models. Due to the importance of this particular topic, Balakrishnan (2009) provided an excellent review for exponential step-stress models. The scope of this book is much more, providing the inferential issues for different probability models, both from the frequentist and Bayesian points-of-view.

Explains the different distributions of the Cumulative Exposure Mode Covers many different models used for step-stress analysis Discusses Step-stress life testing under the competing or complementary risk model

Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research.

This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.

This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.