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Published: 30 May 2022

Last updated: 31 May 2022

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1

Pudney, Stephen. The statistical reliability of micro-simulation estimates: Results for a UK tax-benefit model. Cambridge: University of Cambridge, Department of Applied Economics, 1992.

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2

Portinale, Luigi. Modeling and analysis of dependable systems: A probabilistic graphical model perspective. New Jersey: World Scientific, 2015.

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3

Kotz, Samuel. The stress-strength model and its generalizations: Theory and applications. Singapore: World Scientific, 2003.

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4

Kotz, Samuel. The stress-strength model and its generalizations: Theory and applications. Singapore: World Scientific, 2003.

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5

Kotz, Samuel. The stress-strength model and its generalizations: Theory and applications. River Edge, NJ: World Scientific, 2003.

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6

Reliability: Probabilistic models and statistical methods. Englewood Cliffs, N.J: Prentice Hall, 1995.

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7

System Reliability Theory: Models, Statistical Methods, and Applications: Models, statistical methods, and applications. 2nd ed. Hoboken, NJ: Wiley-Interscience, 2004.

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8

Marcus Cornelus Jacobus van Pul. Statistical analysis of software reliability models. Amsterdam: Centrum voor Wiskunde en Informatica, 1993.

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9

Pul, Marcus Cornelus Jacobus van. Statistical analysis of software reliability models. Amsterdam: Centrum voor Wiskunde en Informatica, 1993.

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10

Ionescu, D. C., and N. Limnios, eds. Statistical and Probabilistic Models in Reliability. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1782-4.

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11

Marvin, Rausand, ed. System reliability theory: Models and statistical methods. New York: Wiley, 1994.

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12

Rykov, V. V., N. Balakrishnan, and M. S. Nikulin, eds. Mathematical and Statistical Models and Methods in Reliability. Boston, MA: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4971-5.

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13

Zacks, Shelemyahu. Introduction to reliability analysis: Probability models and statistical methods. London: Springer-Verlag, 1992.

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14

Zacks, Shelemyahu. Introduction to reliability analysis: Probability models and statistical methods. New York: Springer-Verlag, 1992.

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15

Couallier, Vincent, Léo Gerville-Réache, Catherine Huber-Carol, Nikolaos Limnios, and Mounir Mesbah, eds. Statistical Models and Methods for Reliability and Survival Analysis. Hoboken, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118826805.

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16

Moss, T. R. Reliability data handbook. London: Professional Engineering, 2005.

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17

Statistical quality control in high reliability systems. New York: Garland Pub., 1994.

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18

Zacks, Shelemyahu. Introduction to reliabilityanalysis: Probability models and statistical methods. New York: Springer-Verlag, 1992.

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19

Life time data: Statistical models and methods. Singapore: World Scientific, 2006.

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20

Reliability analysis and prediction: A methodology oriented treatment. Amsterdam: Elsevier, 1992.

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21

Nash, Franklin R. Estimating device reliability: Assessment of credibility. Boston: Kluwer Academic Publishers, 1993.

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22

Max, Engelhardt, ed. Statistical analysis of reliability and life-testing models: Theory and methods. 2nd ed. New York: M. Dekker, 1991.

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23

Yoseph, Shpungin, ed. Models of network reliability: Analysis, combinatorics, and Monte Carlo. Boca Raton, FL: CRC Press, 2010.

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24

Gert︠s︡bakh, I. B. Models of network reliability: Analysis, combinatorics, and Monte Carlo. Boca Raton: CRC Press, 2010.

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25

Gert︠s︡bakh, I. B. Models of network reliability: Analysis, combinatorics, and Monte Carlo. Boca Raton, FL: CRC Press, 2010.

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26

Gert︠s︡bakh, I. B. Models of network reliability: Analysis, combinatorics and Monte Carlo. Boca Raton, FL: CRC Press, 2010.

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27

Linnik, Vladimir. Destruction of coal seams during mining by dredging machines. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1218150.

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The monograph is devoted to the issues of scientific substantiation of ways to improve the efficiency of the functioning of the executive bodies of coal mining machines used in the underground mining of coal seams, which are of great practical importance. The results of studies on the reliability of destructive organs are new in the formulation and not previously published in the monograph format. A model is described and a physical interpretation of the failure patterns of auger assemblies and elements is given, methods for assessing the reliability and efficiency of using augers and cutting tools for specific operating conditions using traditional probabilistic and new energy approaches are proposed. It is addressed to engineering and technical workers of design institutions, factories and mines engaged in the design and operation of cleaning combines and plows.

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28

Wei-Bin, Zeng, and Wu Yanhong, eds. Probability and statistical models: Foundations for problems in reliability and financial mathematics. [Boston]: Birkhäuser, 2010.

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29

Reliability and risk: A Bayesian perspective. Chichester, UK: J. Wiley & Sons, 2007.

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30

Nelson, Wayne. Accelerated testing: Statistical models, test plans and data analyses. New York: Wiley, 1990.

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31

Haroutunian, Evgueni A. Reliability criteria in information theory and in statistical hypothesis testing. Hanover, MA: Now Publishers, 2008.

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32

Pampuro, V. I. Strukturnai͡a︡ informat͡s︡ionnai͡a︡ teorii͡a︡ nadezhnosti sistem. Kiev: Nauk. dumka, 1992.

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33

Reliability in scientific research: Improving the dependability of measurements, calculations, equipment, and software. Cambridge: Cambridge University Press, 2011.

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34

Reiser, Benjamin. Confidence limits for stress-strength models with covariates. Toronto: University of Toronto, Dept. of Statistics, 1985.

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35

N, Balakrishnan, Nikulin M. S, and SpringerLink (Online service), eds. Mathematical and Statistical Models and Methods in Reliability: Applications to Medicine, Finance, and Quality Control. Boston: Springer Science+Business Media, LLC, 2010.

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36

Burkov, Aleksey. Technical operation of electric ships. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1048423.

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The book investigates the issues related to improving the efficiency of technical operation of ship electric drives, developed their classification. Identified ship's drives, having low reliability, designed and implemented technical solutions to increase their reliability. The appropriateness of the integrated assessment within the tasks of a mathematical and physical modeling. Developed and implemented mathematical and physical models for studies of electric drives. The proposed method, an algorithmic software, and made payments of contactors for work in the proposed technical solutions. Designed for those who specializiruetsya in the field of the theory and practice of ship electric drives. Useful for the learning process in the system of higher Maritime education.

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37

The new Weibull handbook: Reliability & statistical analysis for predicting life, safety, risk, support costs, failures, and forecasting warranty claims, substantiation and accelerated testing, using Weibull, Log normal, crow-AMSAA, probit, and Kaplan-Meier models. 5th ed. North Palm Beach, Fla: R.B. Abernethy, 2006.

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38

Response modeling methodology: Empirical modeling for engineering and science. Hackensack, NJ: World Scientific, 2005.

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39

G, Schneeweiss Winfrid, ed. The modeling world of reliability / safety engineering: Choosing the best method and the best tool for quantitative analysis. Hagen, Germany: LiLoLe - Verlag, 2005.

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40

S, Dhillon B. Reliability technology, human error, and quality in health care. Boca Raton: Taylor & Francis, 2007.

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41

Ayyub, Bilal M. Uncertainty analysis in engineering and sciences: Fuzzy logic, statistics, and neural network approach. Boston: Kluwer Academic Publishers, 1997.

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42

Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

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43

Yi-Chao, Chen, and United States. National Aeronautics and Space Administration., eds. An analytical model for footprint dispersions and its application to mission design: Report submitted to NASA-Johnson Space Center. Houston, TX: UH Systems Design Laboratory, 1992.

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44

An analytical model for footprint dispersions and its application to mission design: Report submitted to NASA-Johnson Space Center. Houston, TX: UH Systems Design Laboratory, 1992.

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45

Cumming, Jonathan A., and Michael Goldstein. Bayesian analysis and decisions in nuclear power plant maintenance. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.9.

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This article discusses the results of a study in Bayesian analysis and decision making in the maintenance and reliability of nuclear power plants. It demonstrates the use of Bayesian parametric and semiparametric methodology to analyse the failure times of components that belong to an auxiliary feedwater system in a nuclear power plant at the South Texas Project (STP) Electric Generation Station. The parametric models produce estimates of the hazard functions that are compared to the output from a mixture of Polya trees model. The statistical output is used as the most critical input in a stochastic optimization model which finds the optimal replacement time for a system that randomly fails over a finite horizon. The article first introduces the model for maintenance and reliability analysis before presenting the optimization results. It also examines the nuclear power plant data to be used in the Bayesian models.

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46

C, Ionescu D., and Limnios N, eds. Statistical and probabilistic models in reliability. Boston: Birkhäuser, 1999.

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47

Leemis, Lawrence. Reliability: Probabilistic Models and Statistical Methods. Prentice Hall, 1994.

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48

Leemis, Lawrence. Reliability: Probabilistic Models and Statistical Methods. Prentice Hall, 1994.

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49

Statistical and Probabilistic Models in Reliability. Birkhäuser, 2011.

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50

Rausand, Marvin, and Arnljot H�yland. System Reliability Theory: Models and Statistical Methods. Wiley & Sons, Incorporated, John, 2009.

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