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Journal articles on the topic 'Control Variate'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Nelson, Barry L. "Control Variate Remedies." Operations Research 38, no. 6 (1990): 974–92. http://dx.doi.org/10.1287/opre.38.6.974.

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2

Bauer, Kenneth W., and James R. Wilson. "Control-variate selection criteria." Naval Research Logistics 39, no. 3 (1992): 307–21. http://dx.doi.org/10.1002/nav.3220390303.

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3

Nelson, Barry L. "On control variate estimators." Computers & Operations Research 14, no. 3 (1987): 219–25. http://dx.doi.org/10.1016/0305-0548(87)90024-4.

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4

SCHMEISER, BRUCE W., MICHAEL R. TAAFFE, and JIN WANG. "Biased control-variate estimation." IIE Transactions 33, no. 3 (2001): 219–28. http://dx.doi.org/10.1080/07408170108936824.

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5

Pasupathy, Raghu, Bruce W. Schmeiser, Michael R. Taaffe, and Jin Wang. "Control-variate estimation using estimated control means." IIE Transactions 44, no. 5 (2012): 381–85. http://dx.doi.org/10.1080/0740817x.2011.610430.

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6

Ma, JunMei, and Chenglong Xu. "An efficient control variate method for pricing variance derivatives." Journal of Computational and Applied Mathematics 235, no. 1 (2010): 108–19. http://dx.doi.org/10.1016/j.cam.2010.05.017.

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7

Henderson, Shane G., and Burt Simon. "Adaptive simulation using perfect control variates." Journal of Applied Probability 41, no. 03 (2004): 859–76. http://dx.doi.org/10.1017/s0021900200020593.

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We introduce adaptive-simulation schemes for estimating performance measures for stochastic systems based on the method of control variates. We consider several possible methods for adaptively tuning the control-variate estimators, and describe their asymptotic properties. Under certain assumptions, including the existence of a ‘perfect control variate’, all of the estimators considered converge faster than the canonical rate ofn−1/2, wherenis the simulation run length. Perfect control variates for a variety of stochastic processes can be constructed from ‘approximating martingales’. We prove

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8

Henderson, Shane G., and Burt Simon. "Adaptive simulation using perfect control variates." Journal of Applied Probability 41, no. 3 (2004): 859–76. http://dx.doi.org/10.1239/jap/1091543430.

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We introduce adaptive-simulation schemes for estimating performance measures for stochastic systems based on the method of control variates. We consider several possible methods for adaptively tuning the control-variate estimators, and describe their asymptotic properties. Under certain assumptions, including the existence of a ‘perfect control variate’, all of the estimators considered converge faster than the canonical rate of n−1/2, where n is the simulation run length. Perfect control variates for a variety of stochastic processes can be constructed from ‘approximating martingales’. We pro

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9

Gobet, Emmanuel, and Céline Labart. "Solving BSDE with Adaptive Control Variate." SIAM Journal on Numerical Analysis 48, no. 1 (2010): 257–77. http://dx.doi.org/10.1137/090755060.

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10

Fan, Shaohua, Stephen Chenney, Bo Hu, Kam-Wah Tsui, and Yu-chi Lai. "Optimizing Control Variate Estimators for Rendering." Computer Graphics Forum 25, no. 3 (2006): 351–57. http://dx.doi.org/10.1111/j.1467-8659.2006.00954.x.

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11

Amano, Tomoyuki, and Masanobu Taniguchi. "Control variate method for stationary processes." Journal of Econometrics 165, no. 1 (2011): 20–29. http://dx.doi.org/10.1016/j.jeconom.2011.05.003.

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12

Fox, Jamie, and Giray Ökten. "Polynomial Chaos as a Control Variate Method." SIAM Journal on Scientific Computing 43, no. 3 (2021): A2268—A2294. http://dx.doi.org/10.1137/20m1336515.

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13

Han, Chuan-Hsiang, and Yongzeng Lai. "Generalized control variate methods for pricing Asian options." Journal of Computational Finance 14, no. 2 (2010): 87–118. http://dx.doi.org/10.21314/jcf.2010.212.

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14

Pajot, Anthony, Loïc Barthe, and Mathias Paulin. "Globally Adaptive Control Variate for Robust Numerical Integration." SIAM Journal on Scientific Computing 36, no. 4 (2014): A1708—A1730. http://dx.doi.org/10.1137/130937846.

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15

Tsai, Shing Chih. "Control-variate methods for comparison with a standard." Journal of Statistical Computation and Simulation 81, no. 11 (2011): 1703–16. http://dx.doi.org/10.1080/00949655.2010.499874.

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16

Lejay, Antoine, and Victor Reutenauer. "A variance reduction technique using a quantized Brownian motion as a control variate." Journal of Computational Finance 16, no. 2 (2012): 61–84. http://dx.doi.org/10.21314/jcf.2012.242.

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17

Crespo, Miguel, Adrian Jarabo, and Adolfo Muñoz. "Primary-space Adaptive Control Variates Using Piecewise-polynomial Approximations." ACM Transactions on Graphics 40, no. 3 (2021): 1–15. http://dx.doi.org/10.1145/3450627.

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We present an unbiased numerical integration algorithm that handles both low-frequency regions and high-frequency details of multidimensional integrals. It combines quadrature and Monte Carlo integration by using a quadrature-based approximation as a control variate of the signal. We adaptively build the control variate constructed as a piecewise polynomial, which can be analytically integrated, and accurately reconstructs the low-frequency regions of the integrand. We then recover the high-frequency details missed by the control variate by using Monte Carlo integration of the residual. Our wo

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18

Tian, Yi. "Monte Carlo method with control variate for integral equations." Thermal Science 22, no. 4 (2018): 1765–71. http://dx.doi.org/10.2298/tsci1804765t.

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This paper combines the successive substitution method and Monte Carlo method with a control variate to solve Fredholm integral equations of the second kind. Some examples are gives to elucidate the solution process and the results reveal the efficiency of the method.

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19

Huang, Biao, Steven X. Ding, and Nina Thornhill. "Alternative solutions to multi-variate control performance assessment problems." Journal of Process Control 16, no. 5 (2006): 457–71. http://dx.doi.org/10.1016/j.jprocont.2005.09.003.

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20

Kamizono, Kenji, Takeaki Kariya, Regina Y. Liu, and Teruo Nakatsuma. "A New Control Variate Estimator for an Asian Option." Asia-Pacific Financial Markets 11, no. 2 (2004): 143–60. http://dx.doi.org/10.1007/s10690-006-9007-8.

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21

Nawfel, Jena L., Kevin B. Englehart, and Erik J. Scheme. "A Multi-Variate Approach to Predicting Myoelectric Control Usability." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 1312–27. http://dx.doi.org/10.1109/tnsre.2021.3094324.

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22

MEHRDOUST, FARSHID, and IDIN NOORANI. "AN EFFICIENT VARIANCE REDUCTION-BASED SIMULATION ALGORITHM FOR PRICING ARITHMETIC ASIAN OPTIONS." Annals of Financial Economics 15, no. 01 (2019): 2050001. http://dx.doi.org/10.1142/s2010495220500013.

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This paper proposes a new hybrid algorithm to price the arithmetic Asian options under the geometric Brownian motion (GBM). The proposed algorithm is based on the control variate technique, such that the control variable is a combination of the barrier arithmetic Asian option and the geometric Asian option, which each one will be estimated by the importance sampling and the control variate techniques, respectively. Besides, we drive a conditional expectation for the estimator that it can reduce variance of simulations. The merits of the proposed algorithm for pricing arithmetic Asian options a

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23

ARTANADI, NI NYOMAN AYU, KOMANG DHARMAWAN, and KETUT JAYANEGARA. "PENENTUAN HARGA OPSI BELI TIPE ASIA DENGAN METODE MONTE CARLO-CONTROL VARIATE." E-Jurnal Matematika 6, no. 1 (2017): 29. http://dx.doi.org/10.24843/mtk.2017.v06.i01.p145.

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Option is a contract between the writer and the holder which entitles the holder to buy or sell an underlying asset at the maturity date for a specified price known as an exercise price. Asian option is a type of financial derivatives which the payoff taking the average value over the time series of the asset price. The aim of the study is to present the Monte Carlo-Control Variate as an extension of Standard Monte Carlo applied on the calculation of the Asian option price. Standard Monte Carlo simulations 10.000.000 generate standard error 0.06 and the option price convergent at Rp.160.00 whi

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24

Ross, Sheldon M. "Variance Reduction in Simulation via Random Hazards." Probability in the Engineering and Informational Sciences 4, no. 3 (1990): 229–309. http://dx.doi.org/10.1017/s0269964800001613.

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The use of the total hazard variable in simulation is studied. When estimating the mean time until a certain event occurs, this variable can be a powerful variance-reducing control variate. When estimating a small probability, it can be utilized as a direct estimator. Applications to reliability, quality control, and queueing are given.

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25

Hull, John, and Alan White. "The Use of the Control Variate Technique in Option Pricing." Journal of Financial and Quantitative Analysis 23, no. 3 (1988): 237. http://dx.doi.org/10.2307/2331065.

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26

Tsai, Shing Chih. "Selecting the best simulated system with weighted control-variate estimators." Mathematics and Computers in Simulation 82, no. 4 (2011): 705–17. http://dx.doi.org/10.1016/j.matcom.2011.09.008.

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27

Gorodetsky, Alex A., Gianluca Geraci, Michael S. Eldred, and John D. Jakeman. "A generalized approximate control variate framework for multifidelity uncertainty quantification." Journal of Computational Physics 408 (May 2020): 109257. http://dx.doi.org/10.1016/j.jcp.2020.109257.

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28

Shiraya, Kenichiro, Hiroki Uenishi, and Akira Yamazaki. "A general control variate method for Lévy models in finance." European Journal of Operational Research 284, no. 3 (2020): 1190–200. http://dx.doi.org/10.1016/j.ejor.2020.01.043.

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29

Sonnendrücker, Eric, Abigail Wacher, Roman Hatzky, and Ralf Kleiber. "A split control variate scheme for PIC simulations with collisions." Journal of Computational Physics 295 (August 2015): 402–19. http://dx.doi.org/10.1016/j.jcp.2015.04.004.

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30

MÜHLENBEIN, HEINZ, and THOMAS AUS DER FÜNTEN. "AN INVESTIGATION OF THE PHASE TRANSITIONS OF A FAMILY OF PROBABILISTIC AUTOMATA." Advances in Complex Systems 07, no. 01 (2004): 93–123. http://dx.doi.org/10.1142/s0219525904000081.

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We investigate a family of totalistic probabilistic cellular automata (PCA) which depend on three parameters. For the uniform random neighborhood and for the symmetric 1D PCA the exact stationary distribution is computed for all finite n. This result is used to evaluate approximations (uni-variate and bi-variate marginals). It is proven that the uni-variate approximation (also called mean-field) is exact for the uniform random neighborhood PCA. The exact results and the approximations are used to investigate phase transitions. We compare the results of two order parameters, the uni-variate mar

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31

Lee, Changkyu, Sang Wook Choi, and In-Beum Lee. "Variable reconstruction and sensor fault identification using canonical variate analysis." Journal of Process Control 16, no. 7 (2006): 747–61. http://dx.doi.org/10.1016/j.jprocont.2005.12.001.

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32

El Filali Ech-Chafiq, Zineb, Jérôme Lelong, and Adil Reghai. "Automatic control variates for option pricing using neural networks." Monte Carlo Methods and Applications 27, no. 2 (2021): 91–104. http://dx.doi.org/10.1515/mcma-2020-2081.

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Abstract Many pricing problems boil down to the computation of a high-dimensional integral, which is usually estimated using Monte Carlo. In fact, the accuracy of a Monte Carlo estimator with M simulations is given by σ M {\frac{\sigma}{\sqrt{M}}} . Meaning that its convergence is immune to the dimension of the problem. However, this convergence can be relatively slow depending on the variance σ of the function to be integrated. To resolve such a problem, one would perform some variance reduction techniques such as importance sampling, stratification, or control variates. In this paper, we wil

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33

Samuel, Raphael T., and Yi Cao. "Kernel Canonical Variate Analysis for Nonlinear Dynamic Process Monitoring." IFAC-PapersOnLine 48, no. 8 (2015): 605–10. http://dx.doi.org/10.1016/j.ifacol.2015.09.034.

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34

Xu, Chenglong, Wei Guan, and Yijuan Liang. "A Comparison of Control Variate Methods for Pricing Interest Rate Derivatives in the LIBOR Market Model." Journal of Systems Science and Information 3, no. 1 (2015): 48–58. http://dx.doi.org/10.1515/jssi-2015-0048.

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AbstractThis paper studies the control variate method for pricing interest rate derivatives driven by the LIBOR market model. Several control variates are constructed based on distinctive approximations for the LIBOR market model. Numerical results show the great efficiency of our methods. The idea in this paper can also be extended to price other interest rate derivatives under the LIBOR market model, such asSwaptions, Caps, some path dependent interest rate derivatives, and so forth.

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35

Dingeç, Kemal Dinçer, and Wolfgang Hörmann. "Using the continuous price as control variate for discretely monitored options." Mathematics and Computers in Simulation 82, no. 4 (2011): 691–704. http://dx.doi.org/10.1016/j.matcom.2011.09.007.

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36

Fouque, Jean-Pierre, and Chuan-Hsiang Han. "A martingale control variate method for option pricing with stochastic volatility." ESAIM: Probability and Statistics 11 (February 2007): 40–54. http://dx.doi.org/10.1051/ps:2007005.

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37

Deceuninck, Matthias, Stijn De Vuyst, and Dieter Fiems. "An efficient control variate method for appointment scheduling with patient unpunctuality." Simulation Modelling Practice and Theory 90 (January 2019): 116–29. http://dx.doi.org/10.1016/j.simpat.2018.11.001.

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38

Dimarco, Giacomo, and Lorenzo Pareschi. "Multi-scale control variate methods for uncertainty quantification in kinetic equations." Journal of Computational Physics 388 (July 2019): 63–89. http://dx.doi.org/10.1016/j.jcp.2019.03.002.

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39

Dingeç, Kemal Dinçer, and Wolfgang Hörmann. "A general control variate method for option pricing under Lévy processes." European Journal of Operational Research 221, no. 2 (2012): 368–77. http://dx.doi.org/10.1016/j.ejor.2012.03.046.

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40

Fallah Nezhad, Mohammad Saber, and Seyed Taghi Akhavan Niaki. "A new monitoring design for uni-variate statistical quality control charts." Information Sciences 180, no. 6 (2010): 1051–59. http://dx.doi.org/10.1016/j.ins.2009.11.033.

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41

Li, Genyuan, and Herschel Rabitz. "Ratio control variate method for efficiently determining high-dimensional model representations." Journal of Computational Chemistry 27, no. 10 (2006): 1112–18. http://dx.doi.org/10.1002/jcc.20435.

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42

Han, Xiaojia, Jing Jiang, Aidong Xu, Xinhong Huang, Chao Pei, and Yue Sun. "Fault Detection of Pneumatic Control Valves Based on Canonical Variate Analysis." IEEE Sensors Journal 21, no. 12 (2021): 13603–15. http://dx.doi.org/10.1109/jsen.2021.3070035.

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43

Legoll, Frédéric, and William Minvielle. "A Control Variate Approach Based on a Defect-Type Theory for Variance Reduction in Stochastic Homogenization." Multiscale Modeling & Simulation 13, no. 2 (2015): 519–50. http://dx.doi.org/10.1137/140980120.

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44

Kucherenko, S., B. Delpuech, B. Iooss, and S. Tarantola. "Application of the control variate technique to estimation of total sensitivity indices." Reliability Engineering & System Safety 134 (February 2015): 251–59. http://dx.doi.org/10.1016/j.ress.2014.07.008.

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45

Larimore, Wallace E. "Optimal Reduced Rank Modeling, Prediction, Monitoring and Control using Canonical Variate Analysis." IFAC Proceedings Volumes 30, no. 9 (1997): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)43140-5.

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46

Ahmed, Mohamed A., Donald Gross, and Douglas R. Miller. "Control Variate Models for Estimating Transient Performance Measures in Repairable Item Systems." Management Science 38, no. 3 (1992): 388–99. http://dx.doi.org/10.1287/mnsc.38.3.388.

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47

Charlton, T. S., M. Rouainia, and R. J. Dawson. "Control Variate Approach for Efficient Stochastic Finite-Element Analysis of Geotechnical Problems." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 4, no. 3 (2018): 04018031. http://dx.doi.org/10.1061/ajrua6.0000983.

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48

Chung, San-Lin, and Mark B. Shackleton. "On the use and improvement of Hull and White's control variate technique." Applied Financial Economics 15, no. 16 (2005): 1171–79. http://dx.doi.org/10.1080/09603100500359195.

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49

Kleiber, R., R. Hatzky, A. Könies, K. Kauffmann, and P. Helander. "An improved control-variate scheme for particle-in-cell simulations with collisions." Computer Physics Communications 182, no. 4 (2011): 1005–12. http://dx.doi.org/10.1016/j.cpc.2010.12.045.

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50

Matsuoka, Seikichi, and Shinsuke Satake. "Application of an improved control-variate scheme to local neoclassical transport simulations." Computer Physics Communications 185, no. 9 (2014): 2313–21. http://dx.doi.org/10.1016/j.cpc.2014.05.001.

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