Relevant states and memory in Markov chain bootstrapping and simulation
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DOI: 10.1016/j.ejor.2016.06.006
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- Cerqueti, Roy & Falbo, Paolo & Pelizzari, Cristian, 2013. "Relevant States and Memory in Markov Chain Bootstrapping and Simulation," MPRA Paper 46250, University Library of Munich, Germany.
References listed on IDEAS
- Joel L. Horowitz, 2003. "Bootstrap Methods for Markov Processes," Econometrica, Econometric Society, vol. 71(4), pages 1049-1082, July.
- Stanislav Anatolyev & Andrey Vasnev, 2002. "Markov chain approximation in bootstrapping autoregressions," Economics Bulletin, AccessEcon, vol. 3(19), pages 1-8.
- Efstathios Paparoditis & Dimitris N. Politis, 2002. "The tapered block bootstrap for general statistics from stationary sequences," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 131-148, June.
- Ryan Sullivan & Allan Timmermann & Halbert White, 1999.
"Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap,"
Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
- Sullivan, Ryan & Timmermann, Allan & White, Halbert, 1998. "Data snooping, technical trading, rule performance, and the bootstrap," LSE Research Online Documents on Economics 119144, London School of Economics and Political Science, LSE Library.
- Sullivan, Ryan & Timmermann, Allan G & White, Halbert, 1998. "Data-Snooping, Technical Trading Rule Performance and the Bootstrap," CEPR Discussion Papers 1976, C.E.P.R. Discussion Papers.
- Allan Timmermann & Halbert White & Ryan Sullivan, 1998. "Data-Snooping, Technical Trading, Rule Performance and the Bootstrap," FMG Discussion Papers dp303, Financial Markets Group.
- M. Rajarshi, 1990. "Bootstrap in Markov-sequences based on estimates of transition density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(2), pages 253-268, June.
- Chao, Gary H., 2013. "Production and availability policies through the Markov Decision Process and myopic methods for contractual and selective orders," European Journal of Operational Research, Elsevier, vol. 225(3), pages 383-392.
- Buhlmann, Peter & Kunsch, Hans R., 1999. "Block length selection in the bootstrap for time series," Computational Statistics & Data Analysis, Elsevier, vol. 31(3), pages 295-310, September.
- repec:ebl:ecbull:v:3:y:2002:i:19:p:1-8 is not listed on IDEAS
- White, Chelsea C. & White, Douglas J., 1989. "Markov decision processes," European Journal of Operational Research, Elsevier, vol. 39(1), pages 1-16, March.
- White, D. J., 1987. "Infinite horizon Markov decision processes with unknown or variable discount factors," European Journal of Operational Research, Elsevier, vol. 28(1), pages 96-100, January.
- José M. Bernardo & Raúl Rueda, 2002. "Bayesian Hypothesis Testing: a Reference Approach," International Statistical Review, International Statistical Institute, vol. 70(3), pages 351-372, December.
- Ohno, Katsuhisa & Boh, Toshitaka & Nakade, Koichi & Tamura, Takayoshi, 2016. "New approximate dynamic programming algorithms for large-scale undiscounted Markov decision processes and their application to optimize a production and distribution system," European Journal of Operational Research, Elsevier, vol. 249(1), pages 22-31.
- Cerqueti, Roy & Falbo, Paolo & Guastaroba, Gianfranco & Pelizzari, Cristian, 2013. "A Tabu Search heuristic procedure in Markov chain bootstrapping," European Journal of Operational Research, Elsevier, vol. 227(2), pages 367-384.
- Buhlmann P., 2002. "Sieve Bootstrap With Variable-Length Markov Chains for Stationary Categorical Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 443-471, June.
- Pandelis, Dimitrios G., 2010. "Markov decision processes with multidimensional action spaces," European Journal of Operational Research, Elsevier, vol. 200(2), pages 625-628, January.
- Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
- Paparoditis, Efstathios & Politis, Dimitris N., 2001. "A Markovian Local Resampling Scheme For Nonparametric Estimators In Time Series Analysis," Econometric Theory, Cambridge University Press, vol. 17(3), pages 540-566, June.
- Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992.
"Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,"
Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
- Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
- Hall, Peter, 1985. "Resampling a coverage pattern," Stochastic Processes and their Applications, Elsevier, vol. 20(2), pages 231-246, September.
- Patrice Bertail & Stéphan Clémençon, 2007. "Second-order properties of regeneration-based bootstrap for atomic Markov chains," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 109-122, May.
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- Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2017. "A mixed integer linear program to compress transition probability matrices in Markov chain bootstrapping," Annals of Operations Research, Springer, vol. 248(1), pages 163-187, January.
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More about this item
Keywords
Bootstrapping; Information theory; Markov chains; Optimization; Simulation;All these keywords.
JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
Statistics
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