Academic literature on the topic 'Stochastic orders'
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Journal articles on the topic "Stochastic orders"
Gollier, Christian. "Variance stochastic orders." Journal of Mathematical Economics 80 (January 2019): 1–8. http://dx.doi.org/10.1016/j.jmateco.2018.10.003.
Full textHoran, Sean. "Stochastic semi-orders." Journal of Economic Theory 192 (March 2021): 105171. http://dx.doi.org/10.1016/j.jet.2020.105171.
Full textShaked, Moshe, Miguel A. Sordo, and Alfonso Suárez-Llorens. "Global Dependence Stochastic Orders." Methodology and Computing in Applied Probability 14, no. 3 (September 24, 2011): 617–48. http://dx.doi.org/10.1007/s11009-011-9253-8.
Full textLópez-Díaz, María Concepción, Miguel López-Díaz, and Sergio Martínez-Fernández. "On stochastic orders defined by other stochastic orders and transformations of probabilities." Mathematical Inequalities & Applications, no. 4 (2022): 925–39. http://dx.doi.org/10.7153/mia-2022-25-59.
Full textLópez-Díaz, María Concepción, Miguel López-Díaz, and Sergio Martínez-Fernández. "Directional Stochastic Orders with an Application to Financial Mathematics." Mathematics 9, no. 4 (February 14, 2021): 380. http://dx.doi.org/10.3390/math9040380.
Full textFERNÁNDEZ, F. R., J. PUERTO, and M. J. ZAFRA. "CORES OF STOCHASTIC COOPERATIVE GAMES WITH STOCHASTIC ORDERS." International Game Theory Review 04, no. 03 (September 2002): 265–80. http://dx.doi.org/10.1142/s0219198902000690.
Full textBartoszewicz, Jarosław. "Dispersive functions and stochastic orders." Applicationes Mathematicae 24, no. 4 (1997): 429–44. http://dx.doi.org/10.4064/am-24-4-429-444.
Full textColangelo, Antonio, Marco Scarsini, and Moshe Shaked. "Some positive dependence stochastic orders." Journal of Multivariate Analysis 97, no. 1 (January 2006): 46–78. http://dx.doi.org/10.1016/j.jmva.2004.11.006.
Full textRajan, D., and D. Vijayabalan. "Some characterizations of stochastic orders." Malaya Journal of Matematik 06, no. 03 (July 1, 2018): 614–18. http://dx.doi.org/10.26637/mjm0603/0023.
Full textBulinskaya, E. V. "Stochastic orders and inventory problems." International Journal of Production Economics 88, no. 2 (March 2004): 125–35. http://dx.doi.org/10.1016/j.ijpe.2003.11.002.
Full textDissertations / Theses on the topic "Stochastic orders"
Dong, Jing, and 董靜. "On upper comonotonicity and stochastic orders." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43085453.
Full textDong, Jing. "On upper comonotonicity and stochastic orders." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43085453.
Full textWong, Tityik 1962. "Contributions to the theory of stochastic orders." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/290627.
Full textXu, Maochao. "Stochastic Orders in Heterogeneous Samples with Applications." PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/391.
Full textZeng, Xin. "Comparative Statics Analysis of Some Operations Management Problems." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/39178.
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Liu, Yunfeng. "Tests of Bivariate Stochastic Order." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20257.
Full textNaujokat, Felix. "Stochastic control in limit order markets." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/16387.
Full textIn this thesis we study a class of stochastic control problems and analyse optimal trading strategies in limit order markets. The first chapter addresses the problem of curve following. We consider an investor who wants to keep his stock holdings close to a stochastic target function. We construct the optimal strategy (comprising market and passive orders) which balances the penalty for deviating and the cost of trading. We first prove existence and uniqueness of an optimal control. The optimal trading strategy is then characterised in terms of the solution to a coupled FBSDE involving jumps via a stochastic maximum principle. We give a second characterisation in terms of buy and sell regions. The application of portfolio liquidation is studied in detail. In the second chapter, we extend our results to singular market orders using techniques of singular stochastic control. We first show existence and uniqueness of an optimal control. We then derive a version of the stochastic maximum principle which yields a characterisation of the optimal trading strategy in terms of a nonstandard coupled FBSDE. We show that the optimal control can be characterised via buy, sell and no-trade regions. We describe precisely when it is optimal to cross the bid ask spread. We also show that the controlled system can be described in terms of a reflected BSDE. As an application, we solve the portfolio liquidation problem with passive orders. When markets are illiquid, option holders may have an incentive to increase their portfolio value by using their impact on the dynamics of the underlying. In Chapter 3, we consider a model with competing players that hold European options and whose trading has an impact on the price of the underlying. We establish existence and uniqueness of equilibrium results and show that the equilibrium dynamics can be characterised in terms of a coupled system of non-linear PDEs. Finally, we show how market manipulation can be reduced.
Locatelli, Marco. "Order reduction strategies for stochastic Galerkin matrix equations." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15881/.
Full textDutt, Arkopal. "High order stochastic transport and Lagrangian data assimilation." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115663.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 103-113).
Ocean currents transport a variety of natural (e.g. water masses, phytoplankton, zooplankton, sediments, etc.) and man-made materials (e.g. pollutants, floating debris, particulate matter, etc.). Understanding such uncertain Lagrangian transport is imperative for reducing environmental damage due to natural hazards and for allowing rigorous risk analysis and effective search and rescue. While secondary variables and trajectories have classically been used for the analyses of such transports, Lagrangian Coherent Structures (LCSs) provide a robust and objective description of the important material lines. To ensure accurate and useful Lagrangian hazard scenario predictions and prevention, the first goal of this thesis is to obtain accurate probabilistic prediction of the underlying stochastic velocity fields using the Dynamically Orthogonal (DO) approach. The second goal is to merge data from both Eulerian and Lagrangian observations with predictions such that the whole information content of observations is utilized. In the first part of this thesis, we develop high-order numerical schemes for the DO equations that ensure efficiency, accuracy, stability, and consistency between the Monte Carlo (MC) and DO solutions. We discuss the numerical challenges in applying the DO equations to the unsteady stochastic Navier-Stokes equations. In order to maintain consistent evaluation of advection terms, we utilize linear centered advection schemes with fully explicit and linear Shapiro filters. We then discuss how to combine the semi-implicit projection method with new high order implicitexplicit (IMEX) linear multi-step and multistage IMEX-RK time marching schemes for the coupled DO equations to ensure further stability and accuracy. We also review efficient numerical re-orthonormalization strategies during time marching. We showcase our results with stochastic test cases of stochastic passive tracer advection in a deterministic swirl flow, stochastic flow past a cylinder, and stochastic lid-driven cavity flow. We show that our schemes improve the consistency between reconstructed DO realizations and the corresponding MC realizations, and that we achieve the expected order of accuracy. In the second part of the work, we first undertake a study of different Lagrangian instruments and outline how the DO methodology can be applied to obtain Lagrangian variables of stochastic flow maps and LCS in uncertain flows. We then review existing methods for Bayesian Lagrangian data assimilation (DA). Disadvantages of earlier methods include the use of approximate measurement models to directly link Lagrangian variables with Eulerian variables, the challenges in respecting the Lagrangian nature of variables, and the assumptions of linearity or of Gaussian statistics during prediction or assimilation. To overcome these, we discuss how the Gaussian Mixture Model (GMM) DO Filter can be extended to fully coupled Eulerian-Lagrangian data assimilation. We define an augmented state vector of the Eulerian and Lagrangian state variables that directly exploits the full mutual information and complete the Bayesian DA in the joint Eulerian-Lagrangian stochastic subspace. Results of such coupled Eulerian-Lagrangian DA are discussed using test cases based on a double gyre flow with random frequency.
by Arkopal Dutt.
S.M.
Kosuch, Stefanie. "Stochastic Optimization Problems with Knapsack Constraint." Paris 11, 2010. http://www.theses.fr/2010PA112154.
Full textGiven a set of objects each having a particular weight and value. The knapsack problem consists of choosing among these items a subset such that (i) the total weight of the chosen items does respect a given weight constraint (the capacity of the knapsack) and (ii) the total value of the chosen items is maximized. In this thesis, we study four stochastic optimization problems with knapsack constraint: the simple recourse knapsack problem, the chance-constrained knapsack problem, the two-stage knapsack problem and a bilievel problem with knapsack chance-constraint. All problems have in common that the item weights in the knapsack constraints are assumed to be random. We propose to solve the simple recourse and the chance-constrained knapsack problems using a branch-&-bound algorithm as framework. Upper bounds are obtained by solving relaxed, i. E. Continuous sub-problems. The latter is done by applying a stochastic gradient algorithm. Concerning the two-stage knapsack problem, we treat, in the first instance, the model where the item weights are assumed to be normally distributed and propose upper and lower bounds on the optimal solution value. Then, we study the problem with discretely distributed weights and show that its deterministic equivalent reformulation does not admit a constant factor approximation algorithm unless P=NP. The studied bilevel problem with knapsack chance-constraint is first of all reformulated as a deterministic equivalent bilinear problem. As the latter is generally hard to solve exactly, we propose to solve a relaxation using a novel iterative algorithm
Books on the topic "Stochastic orders"
George, Shanthikumar J., ed. Stochastic Orders. New York, NY: Springer New York, 2007.
Find full textShaked, 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.
Full textMosler, 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.
Full text1945-, Shaked Moshe, and Shanthikumar J. George, eds. Stochastic orders and their applications. Boston: Academic Press, 1994.
Find full textLi, 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.
Full text1947-, Mosler Karl C., and Scarsini M, eds. Stochastic orders and decision under risk. Hayward, Calif: Institute of Mathematical Statistics, 1991.
Find full textMosler, Karl C. Stochastic orders and applications: A classified bibliography. Berlin: Springer-Verlag, 1993.
Find full textStrassert, Günter. The balancing principle, strict superiority relations, and a transitive overall final order of options. Karlsruhe: Institut für Regionalwissenschaft der Universität Karlsruhe, 2000.
Find full textKaas, R. Ordering of actuarial risks. Brussels: CAIRE, 1994.
Find full textDario, Basso, ed. Permutation tests for stochastic ordering and ANOVA: Theory and applications with R. London ; New York: Springer, 2009.
Find full textBook chapters on the topic "Stochastic orders"
Kochar, Subhash C. "Variability Orders." In Stochastic Comparisons with Applications, 63–89. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12104-3_3.
Full textKochar, Subhash C. "Magnitude Orders." In Stochastic Comparisons with Applications, 21–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12104-3_2.
Full textKochar, Subhash C. "Dependence Orders." In Stochastic Comparisons with Applications, 115–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12104-3_5.
Full textNair, N. Unnikrishnan, P. G. Sankaran, and N. Balakrishnan. "Stochastic Orders in Reliability." In Quantile-Based Reliability Analysis, 281–326. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-0-8176-8361-0_8.
Full textLillo, Rosa E., Asok K. Nanda, and Moshe Shaked. "Some Shifted Stochastic Orders." In Recent Advances in Reliability Theory, 85–103. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1384-0_6.
Full textKochar, Subhash C. "Skewness and Relative Aging Orders." In Stochastic Comparisons with Applications, 91–114. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12104-3_4.
Full textOhnishi, Masamitsu. "Stochastic Orders in Reliability Theory." In Stochastic Models in Reliability and Maintenance, 31–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-540-24808-8_2.
Full textLe Breton, Michel. "Stochastic orders in welfare economics." In Stochastic orders and decision under risk, 190–206. Hayward, CA: Institute of Mathematical Statistics, 1991. http://dx.doi.org/10.1214/lnms/1215459857.
Full textGaede, Karl-Walter. "Stochastic orderings in reliability." In Stochastic orders and decision under risk, 123–40. Hayward, CA: Institute of Mathematical Statistics, 1991. http://dx.doi.org/10.1214/lnms/1215459853.
Full textDi Crescenzo, Antonio, and Maria Longobardi. "Stochastic Comparisons of Cumulative Entropies." In Stochastic Orders in Reliability and Risk, 167–82. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6892-9_8.
Full textConference papers on the topic "Stochastic orders"
Yaping Zhao, Xiaoyun Xu, and Haidong Li. "Effective throughput maximization of stochastic customer orders with inventory constraints." In 2016 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2016. http://dx.doi.org/10.1109/coase.2016.7743571.
Full textMontes, Ignacio, Enrique Miranda, and Susana Montes. "Ranking fuzzy sets and fuzzy random variables by means of stochastic orders." In 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/ifsa-eusflat-15.2015.86.
Full textLin, Pin-Hsun, Eduard A. Jorswieck, Carsten R. Janda, Martin Mittelbach, and Rafael F. Schaefer. "On Stochastic Orders and Fading Gaussian Multi-User Channels with Statistical CSIT." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849386.
Full textBiel, Martin. "Optimal Day-Ahead Orders Using Stochastic Programming and Noise-Driven Recurrent Neural Networks." In 2021 IEEE Madrid PowerTech. IEEE, 2021. http://dx.doi.org/10.1109/powertech46648.2021.9494929.
Full textLin, Pin-Hsun, Eduard A. Jorswieck, Rafael F. Schaefer, Carsten Janda, and Martin Mittelbach. "Degradedness and stochastic orders of fast fading Gaussian broadcast channels with statistical channel state information at the transmitter." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472397.
Full textWu, Jie, Carl M. Larsen, and Halvor Lie. "Estimation of Hydrodynamic Coefficients for VIV of Slender Beam at High Mode Orders." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20327.
Full textAdams, N., C. Bovet, E. Rossa, and A. Simonin. "Picosecond Single Shot Pulse Shape Measurement by Stochastic Sampling of Detected Photon Times." In International Conference on Ultrafast Phenomena. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/up.1992.thc29.
Full textYang, Zeyuan, Ming Li, Yadong Wu*, Jie Tian, and Hua Ouyang. "Time-Resolved Mode Characteristics Of Rotating Instability And Rotating Stall In An Axial Compressor." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-66.
Full textTrevizan, Felipe, Sylvie Thiebaux, Pedro Santana, and Brian Williams. "I-dual: Solving Constrained SSPs via Heuristic Search in the Dual Space." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/701.
Full textMontomoli, F., D. Amirante, N. Hills, S. Shahpar, and M. Massini. "Uncertainty Quantification, Rare Events and Mission Optimization: Stochastic Variations of Metal Temperature During a Transient." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25398.
Full textReports on the topic "Stochastic orders"
Xu, Maochao. Stochastic Orders in Heterogeneous Samples with Applications. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.391.
Full textLototsky, S. V., B. L. Rozovskii, and X. Wan. Elliptic Equations of Higher Stochastic Order. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada597555.
Full textBonney, Matthew S., and Matthew R. W. Brake. Determining Reduced Order Models for Optimal Stochastic Reduced Order Models. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1212810.
Full textKim, Jee S., Frank Proschan, and Jayaram Sethuraman. Stochastic Comparisons of Order Statistics, with Applications in Reliability. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada189408.
Full textAli, Naseem. Thermally (Un-) Stratified Wind Plants: Stochastic and Data-Driven Reduced Order Descriptions/Modeling. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6518.
Full textHastings, D. E. Stochastic model of a first-order nonequilibrium phase transition in a magnetic fusion device. Office of Scientific and Technical Information (OSTI), August 1985. http://dx.doi.org/10.2172/5305891.
Full textCunha, Flavio, James Heckman, and Salvador Navarro. The Identification and Economic Content of Ordered Choice Models with Stochastic Thresholds. Cambridge, MA: National Bureau of Economic Research, July 2007. http://dx.doi.org/10.3386/t0340.
Full textZhang, Guannan, Clayton G. Webster, and Max D. Gunzburger. An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1055118.
Full textСоловйов, Володимир Миколайович, and D. N. Chabanenko. Financial crisis phenomena: analysis, simulation and prediction. Econophysic’s approach. Гумбольдт-Клуб Україна, November 2009. http://dx.doi.org/10.31812/0564/1138.
Full textPerdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.
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