Topic
Reliability (statistics)
About: Reliability (statistics) is a research topic. Over the lifetime, 78671 publications have been published within this topic receiving 1034804 citations.
Papers published on a yearly basis
Papers
More filters
Book•
01 Jun 1981
TL;DR: A number of new classes of life distributions arising naturally in reliability models are treated systematically and each provides a realistic probabilistic description of a physical property occurring in the reliability context, thus permitting more realistic modeling of commonly occurring reliability situations.
Abstract: : This is the first of two books on the statistical theory of reliability and life testing. The present book concentrates on probabilistic aspects of reliability theory, while the forthcoming book will focus on inferential aspects of reliability and life testing, applying the probabilistic tools developed in this volume. This book emphasizes the newer, research aspects of reliability theory. The concept of a coherent system serves as a unifying theme for much of the book. A number of new classes of life distributions arising naturally in reliability models are treated systematically: the increasing failure rate average, new better than used, decreasing mean residual life, and other classes of distributions. As the names would seem to indicate, each such class of life distributions provides a realistic probabilistic description of a physical property occurring in the reliability context. Also various types of positive dependence among random variables are considered, thus permitting more realistic modeling of commonly occurring reliability situations.
3,876 citations
Book•
01 Jan 2006
TL;DR: In this paper, the authors present an analysis of the IEEE Reliability Test System (IRTS) and evaluate the reliability worth of the test system with Monte Carlo simulation and three-order equations for overlapping events.
Abstract: Introduction. Generating Capacity-Basic Probability Methods. Generating Capacity-Frequency and Duration Method. Interconnected Systems. Operating Reserve. Composite Generation and Transmission Systems. Distribution Systems-Basic Techniques and Radial Networks. Distribution Systems-Parallel and Meshed Networks. Distribution Systems-Extended Techniques. Substations and Switching Stations. Plant and Station Availability. Applications of Monte Carlo Simulation. Evaluation of Reliability Worth. Epilogue. Appendix 1: Definitions. Appendix 2: Analysis of the IEEE Reliability Test System. Appendix 3: Thirdorder Equations for Overlapping Events. Solutions to Problems. Index.
3,712 citations
TL;DR: The issue of statistical testing of kappa is considered, including the use of confidence intervals, and appropriate sample sizes for reliability studies using kappa are tabulated.
Abstract: Purpose. This article examines and illustrates the use and interpretation of the kappa statistic in musculoskeletal research. Summary of Key Points. The reliability of clinicians' ratings is an important consideration in areas such as diagnosis and the interpretation of examination findings. Often, these ratings lie on a nominal or an ordinal scale. For such data, the kappa coefficient is an appropriate measure of reliability. Kappa is defined, in both weighted and unweighted forms, and its use is illustrated with examples from musculoskeletal research. Factors that can influence the magnitude of kappa (prevalence, bias, and nonindependent ratings) are discussed, and ways of evaluating the magnitude of an obtained kappa are considered. The issue of statistical testing of kappa is considered, including the use of confidence intervals, and appropriate sample sizes for reliability studies using kappa are tabulated. Conclusions. The article concludes with recommendations for the use and interpretation of kappa.
3,427 citations
TL;DR: This work proposes Krippendorff's alpha as the standard reliability measure, general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data.
Abstract: In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.
3,381 citations
TL;DR: It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability and encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis.
Abstract: Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding ‘analytical goals’ (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators.
3,284 citations