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Books on the topic 'Stochastic orders'

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Published: 4 June 2021
Last updated: 11 March 2023

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1

George, Shanthikumar J., ed. Stochastic Orders. New York, NY: Springer New York, 2007.

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2

Shaked, Moshe, and J. George Shanthikumar, eds. Stochastic Orders. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-34675-5.

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3

Mosler, Karl, and Marco Scarsini. Stochastic Orders and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-49972-2.

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4

1945-, Shaked Moshe, and Shanthikumar J. George, eds. Stochastic orders and their applications. Boston: Academic Press, 1994.

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5

Li, Haijun, and Xiaohu Li, eds. Stochastic Orders in Reliability and Risk. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6892-9.

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6

1947-, Mosler Karl C., and Scarsini M, eds. Stochastic orders and decision under risk. Hayward, Calif: Institute of Mathematical Statistics, 1991.

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7

Mosler, Karl C. Stochastic orders and applications: A classified bibliography. Berlin: Springer-Verlag, 1993.

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8

Strassert, Günter. The balancing principle, strict superiority relations, and a transitive overall final order of options. Karlsruhe: Institut für Regionalwissenschaft der Universität Karlsruhe, 2000.

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9

Kaas, R. Ordering of actuarial risks. Brussels: CAIRE, 1994.

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10

Dario, Basso, ed. Permutation tests for stochastic ordering and ANOVA: Theory and applications with R. London ; New York: Springer, 2009.

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11

Dario, Basso, ed. Permutation tests for stochastic ordering and ANOVA: Theory and applications with R. London ; New York: Springer, 2009.

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12

Multidimensional second order stochastic processes. Singapore: World Scientific, 1997.

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13

Kukanov, Arseniy. Stochastic Models of Limit Order Markets. [New York, N.Y.?]: [publisher not identified], 2013.

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14

Zarzycki, Jan, ed. Nonlinear Prediction Ladder-Filters for Higher-Order Stochastic Sequences. Berlin/Heidelberg: Springer-Verlag, 1985. http://dx.doi.org/10.1007/bfb0007210.

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15

Lan, Guanghui. First-order and Stochastic Optimization Methods for Machine Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1.

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16

Nonlinear prediction ladder-filters for higher-order stochastic sequences. Berlin: Springer-Verlag, 1985.

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17

Kamenskaya, Valentina, and Leonid Tomanov. The fractal-chaotic properties of cognitive processes: age. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1053569.

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In the monograph the literature information about the nature of stochastic processes and their participation in the work of the brain and human behavior. Established that the real cognitive processes and mental functions associated with the procedural side of external events and the stochastic properties of the internal dynamics of brain systems in the form of fluctuations of their parameters, including cardiac rhythm generation and sensorimotor reactions. Experimentally proved that the dynamics of the measured physiological processes is in the range from chaotic regime to a weakly deterministic — fractal mode. Fractal mode determines the maximum order and organization homeostasis of cognitive processes and States, as well as high adaptive ability of the body systems with fractal properties. The fractal-chaotic dynamics is a useful quality to examine the actual physiological and psychological systems - a unique numerical identification of the order and randomness of the processes through calculation of fractal indices. The monograph represents the results of many years of experimental studies of the reflection properties of stochastic sensorimotor reactions, as well as stochastic properties of heart rate in children, Teens and adults in the age aspect in the speech activity and the perception of different kinds of music with its own frequency-spectral structure. Designed for undergraduates, graduate students and researchers that perform research and development on cognitive psychology and neuroscience.
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18

Koski, Timo. Some metric order of entropy-properties of an infinite-dimensional Ornstein-Uhlenbeck process. Åbo: Åbo akademi, 1985.

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19

Mark, Millonas, and Institute for Nonlinear Science, eds. Fluctuations and order: The new synthesis. New York: Springer, 1996.

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20

Cunha, Flavio. The identification and economic content of ordered choice models with stochastic thresholds. Cambridge, MA: National Bureau of Economic Research, 2007.

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21

The asymptotic theory of extreme order statistics. 2nd ed. Malabar, Fla: R.E. Krieger Pub. Co., 1987.

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22

Eckhard, Platen, and Schurz Henri, eds. Numerical solution of SDE through computer experiments. Berlin: Springer-Verlag, 1994.

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23

Kloeden, Peter E. Numerical solution of SDE through computer experiments. 2nd ed. Berlin: Springer, 1997.

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24

Chabi-Yo, Fousseni. The stochastic discount factor: Extending the volatility bound and a new approach to portfolio selection with higher-order moments. Ottawa: Bank of Canada, 2005.

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25

Bergstrom, A. R. Gaussian estimation of mixed order continuous time dynamic models with unobservable stochastic trends from mixed stock and flow data. [Colchester]: University of Essex, Department of Economics, 1995.

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26

Fokker-Planck-Kolmogorov equations. Providence, Rhode Island: American Mathematical Society, 2015.

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27

1945-, Capasso V., Merzbach Ely, and Centro internazionale matematico estivo, eds. Topics in spatial stochastic processes: Lectures given at the C.I.M.E. summer school held in Martina Franca, Italy, July 1-8, 2001. Berlin: Springer, 2003.

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28

1973-, Warzel Simone, ed. Random operators: Disorder effects on quantum spectra and dynamics. Providence, Rhode Island: American Mathematical Society, 2015.

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29

Spectral analysis, differential equations, and mathematical physics: A festschrift in honor of Fritz Gesztesy's 60th birthday. Providence, Rhode Island: American Mathematical Society, 2013.

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30

Imaging, multi-scale, and high-contrast partial differential equations: Seoul ICM 2014 Satellite Conference, August 7-9, 2014, Daejeon, Korea. Providence, Rhode Island: American Mathematical Society, 2016.

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31

Shaked, Moshe, and J. George Shanthikumar. Stochastic Orders. Springer, 2010.

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32

Belzunce, Felix, Carolina Martinez Riquelme, and Julio Mulero. Introduction to Stochastic Orders. Elsevier Science & Technology Books, 2015.

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33

Belzunce, Felix, Carolina Martinez Riquelme, and Julio Mulero. Introduction to Stochastic Orders. Elsevier Science & Technology Books, 2015.

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34

Stochastic Orders (Springer Series in Statistics). Springer, 2006.

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35

An Introduction to Stochastic Orders. Elsevier, 2016. http://dx.doi.org/10.1016/c2014-0-04775-2.

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36

Mosler, Karl, Marco Scarsini, R. Dyckerhoff, and H. Holz. Stochastic Orders and Applications: A Classified Bibliography. Springer, 2012.

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37

Stochastic Orders and Applications: A Classified Bibliography. Springer, 2012.

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38

Scarsini, Marco, K. C. Mosler, and Karl C. Mosler. Stochastic Orders & Applications: A Classified Bibliography (Lecture Notes in Economics and Mathematical Systems). Springer, 1993.

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39

Stochastic Orders In Reliability And Risk In Honor Of Professor Moshe Shaked. Springer-Verlag New York Inc., 2013.

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40

Li, Xiaohu, and Haijun Li. Stochastic Orders in Reliability and Risk: In Honor of Professor Moshe Shaked. Springer New York, 2015.

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41

Li, Xiaohu, and Haijun Li. Stochastic Orders in Reliability and Risk: In Honor of Professor Moshe Shaked. Springer London, Limited, 2013.

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42

O’Neal, M. Angela. What Imaging Test Do I Order? Edited by Angela O’Neal. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190609917.003.0012.

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The case illustrates how to approach imaging in pregnancy. Some basic principles of radiation exposure are reviewed. The effects of radiation are divided into either deterministic, dose-related, or stochastic, where exposure determines the probability of an outcome. Deterministic effects have a threshold below which the effect does not occur. The threshold may be small and may vary from person to person; whereas stochastic effects occur by chance and may occur without a threshold level. Cancer caused by radiation is an example of a stochastic effect. The overall safety of magnetic resonance imaging is discussed. Determining which imaging modality to order depends on the acuity of the situation and the information needed to know in order to adequately treat the mother.
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43

Back, Kerry E. Stochastic Discount Factors. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0003.

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SDFs are defined. The first order condition for portfolio choice is interpreted as: an investor’s marginal rate of substitution is an SDF. There is a strictly positive SDF if and only if there are no arbitrage opportunities, and there is some SDF if and only if the law of one price holds. There is a unique SDF if and only if the market is complete. Orthogonal projections are defined. There is a unique SDF in the span of the assets, and it equals the projection of any SDF onto the span of the assets. The Hansen‐Jagannathan bounds are derived. Orthogonal projections are used to show how an investor with quadratic utility hedges labor income risk.
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44

Nonlinear Prediction Ladder-Filters for Higher-Order Stochastic Sequences. Springer, 1985.

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45

Kochar, Subhash C. Stochastic Comparisons with Applications: In Order Statistics and Spacings. Springer International Publishing AG, 2022.

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46

Koshyk, John Nicholas. A nonlinear, stochastic, low order energy balance climate model. 1986.

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47

Lan, Guanghui. First-Order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing AG, 2021.

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48

Lan, Guanghui. First-order and Stochastic Optimization Methods for Machine Learning. Springer, 2020.

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49

Second Order PDE's in Finite & Infinite Dimensions. Springer, 2001.

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50

Henriksen, Niels Engholm, and Flemming Yssing Hansen. Dynamic Solvent Effects: Kramers Theory and Beyond. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805014.003.0011.

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This chapter discusses dynamical solvent effects on the rate constants for chemical reactions in solution. The effect is described by stochastic dynamics, where the influence of the solvent on the reaction dynamics is included by describing the motion along the reaction coordinate as Brownian motion. Two theoretical approaches are discussed: Kramers theory with a constant time-independent solvent friction coefficient and Grote–Hynes theory, a generalization of Kramers theory, based on the generalized Langevin equation with a time-dependent solvent friction coefficient. The expressions for the rate constants have the same form as in transition-state theory, but are multiplied by transmission coefficients that incorporate the dynamical solvent effect. In the limit of fast motion along the reaction coordinate, the solvent molecules can be considered as “frozen,” and the predictions of the Grote–Hynes theory can differ from the Kramers theory by several orders of magnitude.
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