Journal articles on the topic 'Variance reduction'

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1

James, B. A. P. "Variance Reduction Techniques." Journal of the Operational Research Society 36, no. 6 (June 1985): 525. http://dx.doi.org/10.2307/2582825.

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2

Rouhani, Shahrokh. "Variance Reduction Analysis." Water Resources Research 21, no. 6 (June 1985): 837–46. http://dx.doi.org/10.1029/wr021i006p00837.

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3

James, B. A. P. "Variance Reduction Techniques." Journal of the Operational Research Society 36, no. 6 (June 1985): 525–30. http://dx.doi.org/10.1057/jors.1985.88.

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4

Tjärnström, F., and L. Ljung. "L2 Model reduction and variance reduction." Automatica 38, no. 9 (September 2002): 1517–30. http://dx.doi.org/10.1016/s0005-1098(02)00066-3.

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5

Hwang, Chii-Ruey, Raoul Normand, and Sheng-Jhih Wu. "Variance reduction for diffusions." Stochastic Processes and their Applications 125, no. 9 (September 2015): 3522–40. http://dx.doi.org/10.1016/j.spa.2015.03.006.

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6

Tjärnström, Fredrik, and Lennart Ljung. "L 2 Model Reduction and Variance Reduction." IFAC Proceedings Volumes 33, no. 15 (June 2000): 929–34. http://dx.doi.org/10.1016/s1474-6670(17)39872-5.

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7

Newton, Nigel J. "Variance Reduction for Simulated Diffusions." SIAM Journal on Applied Mathematics 54, no. 6 (December 1994): 1780–805. http://dx.doi.org/10.1137/s0036139992236220.

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8

L'Ecuyer, Pierre, and Christiane Lemieux. "Variance Reduction via Lattice Rules." Management Science 46, no. 9 (September 2000): 1214–35. http://dx.doi.org/10.1287/mnsc.46.9.1214.12231.

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9

Berlinet, Alain. "A note on variance reduction." Statistics & Probability Letters 25, no. 4 (December 1995): 357–60. http://dx.doi.org/10.1016/0167-7152(94)00241-5.

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10

Lynch, Thomas B. "Variance Reduction for Sector Sampling." Forest Science 52, no. 3 (June 1, 2006): 251–61. http://dx.doi.org/10.1093/forestscience/52.3.251.

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Abstract Variance reduction techniques used in Monte Carlo integration including control variates and importance sampling can use estimated or actual shapes of vegetated land areas to reduce the variance of estimators from sector sampling. The estimated shapes of forested areas could come from maps, aerial photos, or similar sources. Antithetic variates for variance reduction in Monte Carlo integration can be applied to sector sampling without using any estimated shape or map. Sector sampling selects as samples all trees or other vegetation of interest located in randomly chosen sectors that have a vertex at a common point located in the interior of a forested area. Each sector is associated with an angle of fixed magnitude emanating from the interior point. Sector orientation is based on a randomly selected azimuth originating at the interior point that is the vertex for each sector. This technique is well adapted to application on relatively small areas that have irregularly shaped boundaries. Sector sampling can be demonstrated to be unbiased. Unbiasedness holds with angle reduction for adjustment of sample size and with the variance reduction techniques that are proposed. The method is not restricted to application with trees because sector sampling methods could be applied just as well to other vegetation types or any other objects located in the land area of interest.
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11

Ross, Sheldon M. "Variance Reduction in Simulation via Random Hazards." Probability in the Engineering and Informational Sciences 4, no. 3 (July 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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12

Belomestny, D. V., L. S. Iosipoi, and N. K. Zhivotovskiy. "Variance Reduction in Monte Carlo Estimators via Empirical Variance Minimization." Doklady Mathematics 98, no. 2 (September 2018): 494–97. http://dx.doi.org/10.1134/s1064562418060261.

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13

Belomestny, D., L. Iosipoi, and N. Zhivotovskiy. "Variance Reduction for Monte Carlo Methods." Доклады академии наук 482, no. 6 (October 2018): 627–30. http://dx.doi.org/10.31857/s086956520002903-6.

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14

Avramidis, Athanassios N., and James R. Wilson. "Integrated Variance Reduction Strategies for Simulation." Operations Research 44, no. 2 (April 1996): 327–46. http://dx.doi.org/10.1287/opre.44.2.327.

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15

Wheway, Virginia. "Variance reduction trends on ‘boosted’ classifiers." Journal of Applied Mathematics and Decision Sciences 8, no. 3 (January 1, 2004): 141–54. http://dx.doi.org/10.1155/s1173912604000094.

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Ensemble classification techniques such as bagging, (Breiman, 1996a), boosting (Freund & Schapire, 1997) and arcing algorithms (Breiman, 1997) have received much attention in recent literature. Such techniques have been shown to lead to reduced classification error on unseen cases. Even when the ensemble is trained well beyond zero training set error, the ensemble continues to exhibit improved classification error on unseen cases. Despite many studies and conjectures, the reasons behind this improved performance and understanding of the underlying probabilistic structures remain open and challenging problems. More recently, diagnostics such as edge and margin (Breiman, 1997; Freund & Schapire, 1997; Schapire et al., 1998) have been used to explain the improvements made when ensemble classifiers are built. This paper presents some interesting results from an empirical study performed on a set of representative datasets using the decision tree learner C4.5 (Quinlan, 1993). An exponential-like decay in the variance of the edge is observed as the number of boosting trials is increased. i.e. boosting appears to ‘homogenise’ the edge. Some initial theory is presented which indicates that a lack of correlation between the errors of individual classifiers is a key factor in this variance reduction.
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16

Cheng, Ming-Yen, and Liang Peng. "Variance Reduction in Multiparameter Likelihood Models." Journal of the American Statistical Association 102, no. 477 (March 2007): 293–304. http://dx.doi.org/10.1198/016214506000000807.

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17

Genuer, Robin. "Variance reduction in purely random forests." Journal of Nonparametric Statistics 24, no. 3 (September 2012): 543–62. http://dx.doi.org/10.1080/10485252.2012.677843.

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18

Aravkin, Aleksandr, and Damek Davis. "Trimmed Statistical Estimation via Variance Reduction." Mathematics of Operations Research 45, no. 1 (February 2020): 292–322. http://dx.doi.org/10.1287/moor.2019.0992.

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19

Badawi, R. D., M. P. Miller, D. L. Bailey, and P. K. Marsden. "Randoms variance reduction in 3D PET." Physics in Medicine and Biology 44, no. 4 (January 1, 1999): 941–54. http://dx.doi.org/10.1088/0031-9155/44/4/010.

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20

MacEachern, Steven N., and Mario Peruggia. "Subsampling the Gibbs sampler: variance reduction." Statistics & Probability Letters 47, no. 1 (March 2000): 91–98. http://dx.doi.org/10.1016/s0167-7152(99)00142-x.

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21

Duncan, A. B., T. Lelièvre, and G. A. Pavliotis. "Variance Reduction Using Nonreversible Langevin Samplers." Journal of Statistical Physics 163, no. 3 (March 22, 2016): 457–91. http://dx.doi.org/10.1007/s10955-016-1491-2.

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22

Lu, Jianguo, and Hao Wang. "Variance reduction in large graph sampling." Information Processing & Management 50, no. 3 (May 2014): 476–91. http://dx.doi.org/10.1016/j.ipm.2014.02.003.

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23

Wheway, Virginia. "Variance Reduction Trends on "Boosted" Classifiers." Journal of Applied Mathematics and Decision Sciences 8, no. 3 (September 1, 2004): 141–54. http://dx.doi.org/10.1207/s15327612jamd0803_1.

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24

Eccleston, K. W., and C. K. Quek. "Reduction of microwave amplifier gain variance." Microwave and Optical Technology Letters 16, no. 3 (October 20, 1997): 145–49. http://dx.doi.org/10.1002/(sici)1098-2760(19971020)16:3<145::aid-mop6>3.0.co;2-m.

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25

Kher, Hemant V., and Lawrence D. Fredendall. "Comparing variance reduction to managing system variance in a job shop." Computers & Industrial Engineering 46, no. 1 (March 2004): 101–20. http://dx.doi.org/10.1016/j.cie.2003.11.002.

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26

Zhang, Jinjing, Fei Hu, Xiaofei Xu, and Li Li. "Stochastic gradient descent with variance reduction technique." Web Intelligence 16, no. 3 (September 11, 2018): 187–94. http://dx.doi.org/10.3233/web-180386.

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27

Wang, Jr-Yan. "Variance Reduction for Multivariate Monte Carlo Simulation." Journal of Derivatives 16, no. 1 (August 31, 2008): 7–28. http://dx.doi.org/10.3905/jod.2008.710895.

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28

Fox, Gordon A., and Bruce E. Kendall. "DEMOGRAPHIC STOCHASTICITY AND THE VARIANCE REDUCTION EFFECT." Ecology 83, no. 7 (July 2002): 1928–34. http://dx.doi.org/10.1890/0012-9658(2002)083[1928:dsatvr]2.0.co;2.

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29

Srikant, Rayadurgam, and Ward Whitt. "Variance Reduction in Simulations of Loss Models." Operations Research 47, no. 4 (August 1999): 509–23. http://dx.doi.org/10.1287/opre.47.4.509.

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30

Booth, Thomas E. "Ex Post Facto Monte Carlo Variance Reduction." Nuclear Science and Engineering 148, no. 3 (November 2004): 391–402. http://dx.doi.org/10.13182/nse04-a2465.

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31

Zhao, Junlong, Yuliang Yin, and Xingzhong Xu. "Penalized Weighted Variance Estimate for Dimension Reduction." Communications in Statistics - Theory and Methods 41, no. 3 (February 2012): 453–73. http://dx.doi.org/10.1080/03610926.2010.526746.

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32

Lewis, Peter A. W., Richard L. Ressler, and R. Kevin Wood. "Variance Reduction Using Nonlinear Controls and Transformations." Communications in Statistics - Simulation and Computation 18, no. 2 (January 1989): 655–72. http://dx.doi.org/10.1080/03610918908812783.

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33

METH, BRACHA. "Reduction of Outcome Variance: Optimality and Incentives." Contemporary Accounting Research 13, no. 1 (March 1996): 309–28. http://dx.doi.org/10.1111/j.1911-3846.1996.tb00502.x.

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34

Bermin, Hans-Peter, Arturo Kohatsu-Higa, and Miquel Montero. "Local Vega Index and Variance Reduction Methods." Mathematical Finance 13, no. 1 (January 2003): 85–97. http://dx.doi.org/10.1111/1467-9965.00007.

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35

Bonvin, John, and Marco Picasso. "Variance reduction methods for CONNFFESSIT-like simulations." Journal of Non-Newtonian Fluid Mechanics 84, no. 2-3 (August 1999): 191–215. http://dx.doi.org/10.1016/s0377-0257(98)00179-7.

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36

Bottarelli, Lorenzo, and Marco Loog. "Gaussian process variance reduction by location selection." Pattern Recognition Letters 125 (July 2019): 727–34. http://dx.doi.org/10.1016/j.patrec.2019.07.013.

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37

Ross, Sheldon M. "Simulating Average Delay–Variance Reduction by Conditioning." Probability in the Engineering and Informational Sciences 2, no. 3 (July 1988): 309–12. http://dx.doi.org/10.1017/s0269964800000851.

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An improved simulation estimator of the expected total delay of the first n customers in the systems GI/M/k and GI/G/I is obtained by replacing the (raw data) delay of customer i by its expected delay given the state of the system upon its arrival. It is improved in the sense that the sum of the first n such values has the same mean and smaller variance than the usual (raw) estimator. The only constraint on the arrival process is that it be independent of the process of service times.
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38

Ross, Sheldon M., and Kyle Y. Lin. "APPLYING VARIANCE REDUCTION IDEAS IN QUEUING SIMULATIONS." Probability in the Engineering and Informational Sciences 15, no. 4 (October 2001): 481–94. http://dx.doi.org/10.1017/s0269964801154045.

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Variance reduction techniques are often underused in simulation studies. In this article, we indicate how certain ones can be efficiently employed when analyzing queuing models. The first technique considered is that of dynamic stratified sampling; the second is the utilization of multiple control variates; the third concerns the replacement of random variables by their conditional expectations when trying to estimate the expected value of a sum of random variables.
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39

Wu, Zeyun, and Hany S. Abdel-Khalik. "Hybrid biasing approaches for global variance reduction." Applied Radiation and Isotopes 72 (February 2013): 83–88. http://dx.doi.org/10.1016/j.apradiso.2012.09.026.

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40

Tayalı, Halit Alper, and Seda Tolun. "Dimension reduction in mean-variance portfolio optimization." Expert Systems with Applications 92 (February 2018): 161–69. http://dx.doi.org/10.1016/j.eswa.2017.09.009.

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41

Zhao, JunLong, and XingZhong Xu. "Dimension reduction based on weighted variance estimate." Science in China Series A: Mathematics 52, no. 3 (December 5, 2008): 539–60. http://dx.doi.org/10.1007/s11425-008-0130-z.

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42

Shih, Neng-Hui, and Wheyming Tina Song. "Correlation-inducing variance reduction in regenerative simulation." Operations Research Letters 19, no. 1 (July 1996): 17–23. http://dx.doi.org/10.1016/0167-6377(96)00020-x.

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43

Jin, Xiao-Bo, Xu-Yao Zhang, Kaizhu Huang, and Guang-Gang Geng. "Stochastic Conjugate Gradient Algorithm With Variance Reduction." IEEE Transactions on Neural Networks and Learning Systems 30, no. 5 (May 2019): 1360–69. http://dx.doi.org/10.1109/tnnls.2018.2868835.

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44

Berger, Daniel S., Sebastian Henningsen, Florin Ciucu, and Jens B. Schmitt. "Maximizing Cache Hit Ratios by Variance Reduction." ACM SIGMETRICS Performance Evaluation Review 43, no. 2 (September 16, 2015): 57–59. http://dx.doi.org/10.1145/2825236.2825259.

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45

Zou, Gang, and Robert D. Skeel. "Robust Variance Reduction for Random Walk Methods." SIAM Journal on Scientific Computing 25, no. 6 (January 2004): 1964–81. http://dx.doi.org/10.1137/s1064827503424025.

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46

Fu, Gongkang. "Variance reduction by truncated multimodal importance sampling." Structural Safety 13, no. 4 (April 1994): 267–83. http://dx.doi.org/10.1016/0167-4730(94)90033-7.

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47

Ibrahim, Mohamed-Hamza, and Rokia Missaoui. "Approximating concept stability using variance reduction techniques." Discrete Applied Mathematics 273 (February 2020): 117–35. http://dx.doi.org/10.1016/j.dam.2019.03.002.

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48

Belomestny, Denis, Stefan Häfner, Tigran Nagapetyan, and Mikhail Urusov. "Variance reduction for discretised diffusions via regression." Journal of Mathematical Analysis and Applications 458, no. 1 (February 2018): 393–418. http://dx.doi.org/10.1016/j.jmaa.2017.09.002.

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49

Ding, Ying. "Variance Reduction Techniques Based on Binary Option Pricing." Highlights in Science, Engineering and Technology 49 (May 21, 2023): 348–55. http://dx.doi.org/10.54097/hset.v49i.8532.

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Aiming at the binary option pricing model, control variates and antithetic variates methods are focused on comparing the reasons and influencing factors for the difference of variance reduction effect between them. By adjusting the independent variable of the control variates method and the range of binary options indication function, and by sensitivity analysis of four parameters of the pricing model, the study finds a way to improve the variance reduction ability. Through the detailed mathematical derivation of the antithetic variates method to confirm the law of the 4 parameters in the Black-Scholes model and the binary option price found by the former. And construct more accurate theoretical values to provide better direction for setting options prices in the real world.
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50

Boire, François-Michel, R. Mark Reesor, and Lars Stentoft. "Efficient Variance Reduction for American Call Options Using Symmetry Arguments." Journal of Risk and Financial Management 14, no. 11 (October 20, 2021): 504. http://dx.doi.org/10.3390/jrfm14110504.

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Recently it was shown that the estimated American call prices obtained with regression and simulation based methods can be significantly improved on by using put-call symmetry. This paper extends these results and demonstrates that it is also possible to significantly reduce the variance of the estimated call price by applying variance reduction techniques to corresponding symmetric put options. First, by comparing performance for pairs of call and (symmetric) put options for which the solution coincides, our results show that efficiency gains from variance reduction methods are different for calls and symmetric puts. Second, control variates should always be used and is the most efficient method. Furthermore, since control variates is more effective for puts than calls, and since symmetric pricing already offers some variance reduction, we demonstrate that drastic reductions in the standard deviation of the estimated call price is obtained by combining all three variance reduction techniques in a symmetric pricing approach. This reduces the standard deviation by a factor of over 20 for long maturity call options on highly volatile assets. Finally, we show that our findings are not particular to using in-sample pricing but also hold when using an out-of-sample pricing approach.
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