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Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Escassut, Alain. Value distribution in p-adic analysis. World Scientific, 2016.

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2

Resnick, Sidney I. Extreme values, regular variation, andpoint processes. Springer-Verlag, 1987.

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Resnick, Sidney I. Extreme values, regular variation, and point processes. Springer-Verlag, 1987.

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4

Fayolle, Guy. Random Walks in the Quarter-Plane: Algebraic Methods, Boundary Value Problems and Applications. Springer Berlin Heidelberg, 1999.

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5

Taira, Kazuaki. Semigroups, Boundary Value Problems and Markov Processes. Springer Berlin Heidelberg, 2004.

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6

1967-, Thomas M., ed. Statistical analysis of extreme values: From insurance, finance, hydrology and other fields. Birkhäuser, 1997.

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7

1967-, Thomas M., ed. Statistical analysis of extreme values: From insurance, finance, hydrology, and other fields. 2nd ed. Birkhäuser Verlag, 2001.

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8

J, Hüsler, and Reiss R. -D, eds. Laws of small numbers: Extremes and rare events. 2nd ed. Birkhauser Verlag, 2004.

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9

J, Hüsler, and Reiss R. -D, eds. Laws of small numbers: Extremes and rare events. Birkhäuser Verlag, 1994.

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10

Bärbel, Finkenstädt, and Rootzén Holger, eds. Extreme values in finance, telecommunications, and the environment. Chapman & Hall/CRC, 2004.

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11

Zuev, Sergey, Ruslan Maleev, and Viktor Venevcev. Reliability of electrical and electronic equipment of modern vehicles. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2130171.

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The textbook contains information on the reliability of electrical and electronic equipment of vehicles, provides basic terms and definitions, provides information on the reliability of non-removable products, and on the operating conditions of transport electronics products. Information on statistical process management is provided. The types, purpose, and organization of testing, methods for evaluating reliability indicators, and ways to improve reliability (redundancy) are described. The analysis of technological processes by the method of histograms is given. The concept of "reliability" i

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12

Ferreira, Ana, and Laurens de de Haan. Extreme Value Theory: An Introduction. Springer, 2010.

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13

Extreme Value Theory: An Introduction. Springer, 2007.

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14

Longin, Francois. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications. Wiley & Sons, Incorporated, John, 2016.

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15

Longin, Francois. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications. Wiley & Sons, Incorporated, John, 2016.

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16

EXTREME EVENTS IN FINANCE: A HANDBOOK OF EXTREME VALUE THEORY AND ITS APPLICATIONS. JOHN WILEY, 2017.

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17

Longin, Francois. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications. Wiley & Sons, Incorporated, John, 2016.

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18

Longin, Francois. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications. Wiley & Sons, Limited, John, 2016.

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19

Extreme Value Theory: An Introduction (Springer Series in Operations Research and Financial Engineering). Springer, 2006.

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20

Extreme Value Theory and Applications: Proceedings of the Conference on Extreme Value Theory and Applications, Volume 1 Gaithersburg Maryland 1993. Springer, 2011.

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21

Resnick, Sidney I. Extreme Values, Regular Variation and Point Processes. Springer London, Limited, 2013.

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22

Coles, Stuart. An Introduction to Statistical Modeling of Extreme Values. Springer, 2011.

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23

Coles, Stuart. An Introduction to Statistical Modeling of Extreme Values. Springer, 2014.

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24

Elliptic Boundary Value Problems and Construction of Lp-Strong Feller Processes with Singular Drift and Reflection. Springer Spektrum, 2014.

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25

Baur, Benedict. Elliptic Boundary Value Problems and Construction of Lp-Strong Feller Processes with Singular Drift and Reflection. Spektrum Akademischer Verlag GmbH, 2014.

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26

Yan, Jun, and Dipak Dey. Extreme Value Modeling and Risk Analysis. Taylor & Francis Group, 2020.

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27

Taira, Kazuaki. Semigroups, Boundary Value Problems and Markov Processes. Springer, 2016.

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28

Taira, Kazuaki. Semigroups, Boundary Value Problems and Markov Processes. Springer, 2014.

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29

Semigroups, Boundary Value Problems and Markov Processes. Springer, 2014.

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30

Dey, Dipak K., and Jun Yan. Extreme Value Modeling and Risk Analysis: Methods and Applications. Taylor & Francis Group, 2016.

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31

Dey, Dipak K., and Jun Yan. Extreme Value Modeling and Risk Analysis: Methods and Applications. Taylor & Francis Group, 2016.

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32

Extreme Value Modeling and Risk Analysis: Methods and Applications. Taylor & Francis Group, 2016.

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33

Fayolle, Guy, Roudolf Iasnogorodski, and Vadim Malyshev. Random Walks in the Quarter Plane: Algebraic Methods, Boundary Value Problems, Applications to Queueing Systems and Analytic Combinatorics. Springer, 2017.

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34

Fayolle, Guy, Roudolf Iasnogorodski, and Vadim Malyshev. Random Walks in the Quarter Plane: Algebraic Methods, Boundary Value Problems, Applications to Queueing Systems and Analytic Combinatorics. Springer, 2018.

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35

Statistical Analysis of Extreme Values: With Applications to Insurance, Finance, Hydrology and Other Fields. 3rd ed. Birkhäuser Basel, 2007.

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36

What is a P-value anyway? : 34 stories to help you actually understand statistics. Boston : Addison- Wesley, 2010.

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37

Extreme Values, Regular Variation, and Point Processes. Springer, 2007.

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38

Heavy-tail phenomena: Probabilistic and statistical modeling. Springer, 2007.

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39

Optimal Stopping and Free-Boundary Problems (Lectures in Mathematics. ETH Zürich). Birkhäuser, 2006.

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40

Adler, Matthew D. Risk, Death, and Well-Being. Oxford University PressNew York, NY, 2025. https://doi.org/10.1093/9780197505984.001.0001.

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Abstract A wide range of governmental policies seek to reduce individuals’ fatality risks—risks that arise from air and water pollution, pathogens, food ingredients and contaminants, motor vehicles, infrastructure, radiation, workplace accidents, alcohol and recreational drugs, firearms, consumer products, tobacco, natural disasters, and other sources. This book provides a rigorous treatment of the ethics of fatality risk regulation. It does so through the lens of welfare-consequentialism—specifically, lifetime welfarism, with a focus on utilitarianism and prioritarianism. The ethical ranking

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41

Ray, Sumantra (Shumone), Sue Fitzpatrick, Rajna Golubic, Susan Fisher, and Sarah Gibbings, eds. Navigating research methods: basic concepts in biostatistics and epidemiology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199608478.003.0002.

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This chapter provides an overview of the basic concepts in biostatistics and epidemiology. Section 1: Basic concepts in biostatistics The concepts in biostatistics include: 1. descriptive statistical methods (which comprise of frequency distribution, distribution shapes, and measures of central tendency and dispersion); and 2. inferential statistics which is applied to make inferences about a population from the sample data. Non-probability and probability sampling methods are outlined. This section provides simple explanation of the complex concepts of significance tests and confidence interv

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42

Cheng, Russell. Change-Point Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0011.

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This chapter investigates change-point (hazard rate) probability models for the random survival time in some population of interest. A parametric probability distribution is assumed with parameters to be estimated from a sample of observed survival times. If a change-point parameter, denoted by τ‎, is included to represent the time at which there is a discrete change in hazard rate, then the model is non-standard. The profile log-likelihood, with τ‎ as profiling parameter, has a discontinuous jump at every τ‎ equal to a sampled value, becoming unbounded as τ‎ tends to the largest observation.

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43

Cheng, Russell. Examples of Embedded Distributions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0006.

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This chapter gives examples of probability distributions that, in their conventional parametrization, contain embedded models. Embeddedness is not intrinsic but depends on the parametrization. The simplest way to reveal and remove embeddedness is to reparametrize and make the log-likelihood, L, expandable as a Maclaurin series of one parameter, α‎: L = L0 + L1α‎ + L2α‎2 + … with L0 the log-likelihood of the embedded model hidden in the original parametrization. The quantity L1, rescaled using the information matrix, is the score statistic which can be used for formally comparing the original a

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44

Bandyopadhyay, Arindam. Basic Statistics for Risk Management in Banks and Financial Institutions. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849014.001.0001.

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The book provides an engaging account of theoretical, empirical, and practical aspects of various statistical methods in measuring risks of financial institutions, especially banks. In this book, the author demonstrates how banks can apply many simple but effective statistical techniques to analyse risks they face in business and safeguard themselves from potential vulnerability. It covers three primary areas of banking risks—credit, market, and operational risk, and in a uniquely intuitive, step-by-step manner, the author provides hands-on details on the primary statistical tools that can be

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