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Journal articles on the topic 'Sensitivity Analysis'

To see the other types of publications on this topic, follow the link: Sensitivity Analysis.

Author: Grafiati

Published: 4 June 2021

Last updated: 31 August 2024

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1

Yunanto, Muhamad, and Henny Medyawati. "Fiscal Policy and Monetary Policy: Sensitivity Analysis." International Journal of Trade, Economics and Finance 6, no. 2 (April 2015): 79–84. http://dx.doi.org/10.7763/ijtef.2015.v6.447.

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2

Dragičević, N., B. Karleuša, and N. Ožanić. "Erosion Potential Method (Gavrilović method) sensitivity analysis." Soil and Water Research 12, No. 1 (January 18, 2017): 51–59. http://dx.doi.org/10.17221/27/2016-swr.

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In recent decades, various methods for erosion intensity and sediment production assessment have been developed. The necessity for better model performance has led to the more frequent application of the method sensitivity and uncertainty assessments in order to decrease errors that arise from the model concept and its main assumptions. The analysis presented in this paper refers to the application of the Gavrilović method (Erosion Potential Method), an empirical and semi-quantitative method that can estimate the amount of sediment production and sediment transport as well as the erosion intensity and indicate the areas potentially threatened by erosion. The emphasis in this paper is given upon the method sensitivity analysis that has not previously been conducted for the Gavrilović method. The sensitivity analysis was conducted for fourteen different parameters included in the method, all in relation to different model outputs. Each parameter was perceived and discussed individually in relation to its effect upon the method outputs, and ranked into categories depending on their influence on one or more model outputs. The objective of the analysis was to explore the constraints of the Gavrilović method and the method response to changes deriving from the each individual parameter in an attempt to provide a better understanding of the method, the weight and the contribution of each parameter in the overall method. The parameters that could potentially be used in future research, for method modification and calibration in areas with different catchment characteristics (e.g. climate, geological, etc.) were identified. The most sensitive model parameters resulting from conducted sensitivity analysis for the Gavrilović method are also those considered to be significant in the scientific literature on erosion. The Gavrilović method sensitivity analysis has been done on a case study for the Dubracina catchment area, Croatia.

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3

Plümper, Thomas, and Richard Traunmüller. "The sensitivity of sensitivity analysis." Political Science Research and Methods 8, no. 1 (August 29, 2018): 149–59. http://dx.doi.org/10.1017/psrm.2018.30.

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AbstractThis article evaluates the reliability of sensitivity tests. Using Monte Carlo methods we show that, first, the definition of robustness exerts a large influence on the robustness of variables. Second and more importantly, our results also demonstrate that inferences based on sensitivity tests are most likely to be valid if determinants and confounders are almost uncorrelated and if the variables included in the true model exert a strong influence on outcomes. Third, no definition of robustness reliably avoids both false positives and false negatives. We find that for a wide variety of data-generating processes, rarely used definitions of robustness perform better than the frequently used model averaging rule suggested by Sala-i-Martin. Fourth, our results also suggest that Leamer’s extreme bounds analysis and Bayesian model averaging are extremely unlikely to generate false positives. Thus, if based on these inferential criteria a variable is robust, it is almost certain to belong into the empirical model. Fifth and finally, we also show that researchers should avoid drawing inferences based on lack of robustness.

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4

Qichang An, Qichang An, Jingxu Zhang Jingxu Zhang, Fei Yang Fei Yang, and Hongchao Zhao Hongchao Zhao. "Normalized point source sensitivity analysis in GSSM prototype." Chinese Optics Letters 15, no. 11 (2017): 111202. http://dx.doi.org/10.3788/col201715.111202.

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5

Klimoski, Richard. "Introduction: Sensitivity Analysis." Academy of Management Learning & Education 5, no. 4 (December 2006): 461–62. http://dx.doi.org/10.5465/amle.2006.23473206.

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6

Wagner, Harvey M. "Global Sensitivity Analysis." Operations Research 43, no. 6 (December 1995): 948–69. http://dx.doi.org/10.1287/opre.43.6.948.

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7

Seth, A. K. "Network Sensitivity Analysis." IETE Technical Review 2, no. 12 (December 1985): 399–407. http://dx.doi.org/10.1080/02564602.1985.11437861.

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8

Allaire, Douglas L., and Karen E. Willcox. "Distributional sensitivity analysis." Procedia - Social and Behavioral Sciences 2, no. 6 (2010): 7595–96. http://dx.doi.org/10.1016/j.sbspro.2010.05.134.

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9

Irving, A. D. "Stochastic sensitivity analysis." Applied Mathematical Modelling 16, no. 1 (January 1992): 3–15. http://dx.doi.org/10.1016/0307-904x(92)90110-o.

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10

Somayajula, Gopichand, and James E. Bernard. "Grid sensitivity analysis." Finite Elements in Analysis and Design 7, no. 4 (February 1991): 307–15. http://dx.doi.org/10.1016/0168-874x(91)90046-2.

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11

Novotny, A. A., R. A. Feijóo, E. Taroco, and C. Padra. "Topological sensitivity analysis." Computer Methods in Applied Mechanics and Engineering 192, no. 7-8 (February 2003): 803–29. http://dx.doi.org/10.1016/s0045-7825(02)00599-6.

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12

Eschenbach, Ted G., and Robert J. Gimpel. "Stochastic Sensitivity Analysis." Engineering Economist 35, no. 4 (January 1990): 305–21. http://dx.doi.org/10.1080/00137919008903024.

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13

Molenberghs, Geert. "Incomplete Data in Clinical Studies: Analysis, Sensitivity, and Sensitivity Analysis." Drug Information Journal 43, no. 4 (July 2009): 409–29. http://dx.doi.org/10.1177/009286150904300404.

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14

Kavka, M., M. Mimra, and F. Kumhála. "Sensitivity analysis of key operating parameters of combine harvesters." Research in Agricultural Engineering 62, No. 3 (August 30, 2016): 113–21. http://dx.doi.org/10.17221/48/2015-rae.

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The sensitivity analysis of key operating parameters on the average annual sub-profit in a group of three combine harvesters operating in companies providing agricultural services were analysed. Based on the results of the cost analysis, the following key operating parameters with the greatest influence on the costs were identified: the purchase price of the machine, the price of fuel, maintenance costs, personnel costs and annual performance. These parameters were used in the sensitivity analysis to investigate their effect on unit costs. Changing the above-mentioned parameters is calculated within ± 30% from their mean value. To perform a sensitivity analysis of the average annual sub-profit of combine harvesters, the unit price of mechanized work was additionally used. The results showed that greatest impact on both the average annual earnings of combines operation and on the changes in unit cost was those of the annual performance of the combine harvester, combine harvester purchase price and the cost of fuel. On the other hand, maintenance and personnel costs had a smaller influence concerning these changes of parameters.

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15

Liu, Zhe, Fenghe Tao, and Changzhi Jia. "Reliability Sensitivity Analysis of Vehicle Components and Architecture Optimization." International Journal of Materials, Mechanics and Manufacturing 5, no. 2 (May 2017): 106–12. http://dx.doi.org/10.18178/ijmmm.2017.5.2.299.

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16

Zhou, Peiyuan, and Jinling Wang. "Stochastic Ionosphere Models for Precise GNSS Positioning: Sensitivity Analysis." Journal of Global Positioning Systems 12, no. 1 (June 30, 2013): 53–60. http://dx.doi.org/10.5081/jgps.12.1.53.

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17

Doyle, James D., Clark Amerault, and Carolyn A. Reynolds. "Sensitivity analysis of mountain waves using an adjoint model." Meteorologische Zeitschrift 16, no. 6 (December 17, 2007): 607–20. http://dx.doi.org/10.1127/0941-2948/2007/0236.

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18

Alam, Rafikul, and Sk Safique Ahmad. "Sensitivity Analysis of Nonlinear Eigenproblems." SIAM Journal on Matrix Analysis and Applications 40, no. 2 (January 2019): 672–95. http://dx.doi.org/10.1137/17m1153236.

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19

Huang, Wen, Danylo Khilko, Sergiĭ Kolyada, and Guohua Zhang. "Dynamical compactness and sensitivity." Journal of Differential Equations 260, no. 9 (May 2016): 6800–6827. http://dx.doi.org/10.1016/j.jde.2016.01.011.

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20

Ibrahim, Joseph G. "Comment: Incomplete Data in Clinical Studies: Analysis, Sensitivity, and Sensitivity Analysis." Drug Information Journal 43, no. 4 (July 2009): 431–32. http://dx.doi.org/10.1177/009286150904300405.

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21

Molenberghs, Geert. "Incomplete Data in Clinical Studies: Analysis, Sensitivity, and Sensitivity Analysis—Rejoinder." Drug Information Journal 43, no. 4 (July 2009): 447–48. http://dx.doi.org/10.1177/009286150904300409.

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22

Puy, Arnald, Samuele Lo Piano, and Andrea Saltelli. "A sensitivity analysis of the PAWN sensitivity index." Environmental Modelling & Software 127 (May 2020): 104679. http://dx.doi.org/10.1016/j.envsoft.2020.104679.

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23

Tanaka, Yutaka, and Tomoyuki Tarumi. "SENSITIVITY ANALYSIS IN CANONICAL FACTOR ANALYSIS." Journal of the Japanese Society of Computational Statistics 2, no. 1 (1989): 9–20. http://dx.doi.org/10.5183/jjscs1988.2.9.

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24

Mateo, Zenaida F., Yoshimasa Odaka, and Yutaka Tanaka. "SENSITIVITY ANALYSIS IN FACTOR ANALYSIS REGRESSION." Journal of the Japanese Society of Computational Statistics 6, no. 1 (1993): 11–24. http://dx.doi.org/10.5183/jjscs1988.6.11.

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25

F. Mateo, Zenaida, Yoshimasa Odaka, and Yutaka Tanaka. "Sensitivity Analysis in Alpha Factor Analysis." Japanese Journal of Biometrics 14, no. 1/2 (1993): 1–15. http://dx.doi.org/10.5691/jjb.14.1.

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26

Agarwal, Shivi, Shiv Prasad Yadav, and S. P. Singh. "Sensitivity analysis in data envelopment analysis." International Journal of Operational Research 19, no. 2 (2014): 174. http://dx.doi.org/10.1504/ijor.2014.058948.

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27

Kabala, Z. J., and P. C. D. Milly. "Sensitivity analysis of partial differential equations: A case for functional sensitivity." Numerical Methods for Partial Differential Equations 7, no. 2 (1991): 101–12. http://dx.doi.org/10.1002/num.1690070202.

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28

Liu, Yongsheng. "Sensitivity analysis and proportioning design of rock burst similar materials." Functional materials 24, no. 3 (September 29, 2017): 005–500. http://dx.doi.org/10.15407/fm24.03.496.

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29

Dzantiev, B. B. "MODERN TECHNIQUES OF IMMUNOCHEMICAL ANALYSIS: INTEGRATION OF SENSITIVITY AND RAPIDITY." Biotechnologia Acta 6, no. 4 (2013): 94–104. http://dx.doi.org/10.15407/biotech6.04.094.

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30

Koushik, Priyanka. "Data-Driven Simulation: Integrating Sensitivity Analysis into Supply Chain Optimization." International Journal of Science and Research (IJSR) 13, no. 5 (May 5, 2024): 875–84. http://dx.doi.org/10.21275/sr24511175813.

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31

Debrunner, Victor E., and A. A. (Louis) Beex. "Sensitivity Analysis of Digital Filter Structures." SIAM Journal on Matrix Analysis and Applications 9, no. 1 (January 1988): 106–25. http://dx.doi.org/10.1137/0609009.

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32

Gandelman, Dan Abensur, Angela Oliveira Da Costa, Rafael Barros Araujo, Kriseida C. P. G. Alekseev, and Marina Damião Besteti Ribeiro. "CBIO price sensitivity analysis." Rio Oil and Gas Expo and Conference 20, no. 2020 (December 1, 2020): 263–64. http://dx.doi.org/10.48072/2525-7579.rog.2020.263.

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33

Lee, Young-Jae. "Transit Network Sensitivity Analysis." Journal of Public Transportation 9, no. 1 (February 2006): 21–52. http://dx.doi.org/10.5038/2375-0901.9.1.2.

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34

Link, William A., and Paul F. Doherty. "SCALING IN SENSITIVITY ANALYSIS." Ecology 83, no. 12 (December 2002): 3299–305. http://dx.doi.org/10.1890/0012-9658(2002)083[3299:sisa]2.0.co;2.

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35

Jun-Qiang Sun and Xiaoxiong Xiong. "MODIS Polarization-Sensitivity Analysis." IEEE Transactions on Geoscience and Remote Sensing 45, no. 9 (September 2007): 2875–85. http://dx.doi.org/10.1109/tgrs.2007.900672.

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36

Torn, Ryan D., and Gregory J. Hakim. "Ensemble-Based Sensitivity Analysis." Monthly Weather Review 136, no. 2 (February 1, 2008): 663–77. http://dx.doi.org/10.1175/2007mwr2132.1.

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Abstract The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudo-operational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance. For a single observation, expressions for these changes involve a product of scalar quantities, which can be rapidly evaluated for large numbers of observations. This technique is applied to determining climatological forecast sensitivity and predicting the impact of observations on sea level pressure and precipitation forecast metrics. The climatological 24-h forecast sensitivity of the average pressure over western Washington State shows a region of maximum sensitivity to the west of the region, which tilts gently westward with height. The accuracy of ensemble sensitivity predictions is tested by withholding a single buoy pressure observation from this region and comparing this perturbed forecast with the control case where the buoy is assimilated. For 30 cases, there is excellent agreement between these forecast differences and the ensemble predictions, as measured by the forecast metric. This agreement decreases for increasing numbers of observations. Nevertheless, by using statistical confidence tests to address sampling error, the impact of thousands of observations on forecast-metric variance is shown to be well estimated by a subset of the O(100) most significant observations.

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37

De Camillis, Luca, Giulio Antonini, and Vikram Jandhyala. "Power-Bus Sensitivity Analysis." IEEE Transactions on Advanced Packaging 33, no. 2 (May 2010): 447–56. http://dx.doi.org/10.1109/tadvp.2009.2022322.

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38

Vytyaz, Igor, David C. Lee, Pavan Kumar Hanumolu, Un-Ku Moon, and Kartikeya Mayaram. "Sensitivity Analysis for Oscillators." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 27, no. 9 (September 2008): 1521–34. http://dx.doi.org/10.1109/tcad.2008.927731.

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39

Cheng, A. Y., and D. S. Yeung. "Sensitivity analysis of neocognitron." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 29, no. 2 (May 1999): 238–49. http://dx.doi.org/10.1109/5326.760568.

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40

Ding, Peng, and Tyler J. VanderWeele. "Sensitivity Analysis Without Assumptions." Epidemiology 27, no. 3 (May 2016): 368–77. http://dx.doi.org/10.1097/ede.0000000000000457.

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41

Ponce, Victor Miguel, and Diane Windingland. "Kinematic Shock: Sensitivity Analysis." Journal of Hydraulic Engineering 111, no. 4 (April 1985): 600–611. http://dx.doi.org/10.1061/(asce)0733-9429(1985)111:4(600).

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42

Tune, Paul, and Matthew Roughan. "Network-design sensitivity analysis." ACM SIGMETRICS Performance Evaluation Review 42, no. 1 (June 20, 2014): 449–61. http://dx.doi.org/10.1145/2637364.2591979.

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43

Minguez, Roberto, and Antonio J. Conejo. "State Estimation Sensitivity Analysis." IEEE Transactions on Power Systems 22, no. 3 (August 2007): 1080–91. http://dx.doi.org/10.1109/tpwrs.2007.901488.

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44

Flaig, John J. "PROCESS CAPABILITY SENSITIVITY ANALYSIS." Quality Engineering 11, no. 4 (July 1999): 587–92. http://dx.doi.org/10.1080/08982119908919279.

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45

Baucells, Manel, and Emanuele Borgonovo. "Invariant Probabilistic Sensitivity Analysis." Management Science 59, no. 11 (November 2013): 2536–49. http://dx.doi.org/10.1287/mnsc.2013.1719.

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46

Hughes, Christopher, and Katharine Kostbade Hughes. "N-Way Sensitivity Analysis." Medical Decision Making 10, no. 1 (February 1990): 68–74. http://dx.doi.org/10.1177/0272989x9001000110.

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47

Borgonovo, Emanuele, and Stefano Tarantola. "Advances in sensitivity analysis." Reliability Engineering & System Safety 107 (November 2012): 1–2. http://dx.doi.org/10.1016/j.ress.2012.09.001.

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48

Colonna, G., S. Longo, F. Esposito, and M. Capitelli. "Fourier-transform sensitivity analysis." Applied Physics B Laser and Optics 59, no. 1 (July 1994): 61–72. http://dx.doi.org/10.1007/bf01081729.

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49

Rezaiee-Pajand, M., and M. R. Salary. "Two-dimensional sensitivity analysis." Computers & Structures 61, no. 3 (November 1996): 563–71. http://dx.doi.org/10.1016/0045-7949(96)00059-4.

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50

Nguyen, Duc T. "Multilevel substructuring sensitivity analysis." Computers & Structures 25, no. 2 (January 1987): 191–202. http://dx.doi.org/10.1016/0045-7949(87)90142-8.

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