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Journal articles on the topic 'Sensitivity estimation'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Hahn, Andreas, Ulrike Loderstädt, Hagen Frickmann, and Norbert G. Schwarz. "Sparing the control arm using well-characterized diagnostic approaches – the Gart and Buck prevalence estimator for efficacy estimation in single-arm trials." Journal of Laboratory Medicine 43, no. 5 (2019): 279–81. http://dx.doi.org/10.1515/labmed-2019-0091.

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Abstract Efficacy estimation of medical interventions in clinical trials requires diagnostics to assess the study endpoint. The imperfect nature of diagnostics often leads to biased efficacy estimations, usually underestimation, that could result in failure of clinical trials. Adjustment methods can be used if sensitivity and specificity are known, but they are not regularly applied. Double-arm clinical trials are the standard for demonstrating the superiority of a medical intervention. However, sometimes single-arm trials are the only option: for example, if a parallel group trial design with

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2

Howard, B. J., P. Strand, P. Assimakopoulos, et al. "Estimation of radioecological sensitivity." Radioprotection 37, no. C1 (2002): C1–1167—C1–1173. http://dx.doi.org/10.1051/radiopro/2002142.

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3

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

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4

Murat, Baykal *1 Kenan Gençol 2. "PERFORMANCE EVALUATION OF SINGLE-TONE FREQUENCY ESTIMATORS UNDER NOISY AND IMPERFECT SIGNAL CONDITIONS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 12 (2017): 37–42. https://doi.org/10.5281/zenodo.1084976.

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It is well known that estimation of the parameters, in particular frequency a complex sinusoid contaminated with noise is one of the crucial problems in the literature as the frequency estimation has been applied in many areas such as communications, instrumentation and radar. There are three significant issues which are accuracy, estimator variance and sensitivity to bias that must be taken into consideration while finding the frequency of a sinusoid. In this study, performances of various estimators in estimating the frequency of single-tone signals are evaluated. Estimators are tested again

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5

Park, Soojin, and Gregory J. Palardy. "Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions." Journal of Educational and Behavioral Statistics 45, no. 4 (2020): 475–506. http://dx.doi.org/10.3102/1076998620908599.

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Estimating the effects of randomized experiments and, by extension, their mediating mechanisms, is often complicated by treatment noncompliance. Two estimation methods for causal mediation in the presence of noncompliance have recently been proposed, the instrumental variable method (IV-mediate) and maximum likelihood method (ML-mediate). However, little research has examined their performance when certain assumptions are violated and under varying data conditions. This article addresses that gap in the research and compares the performance of the two methods. The results show that the distrib

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6

Da Veiga, Sébastien, and Fabrice Gamboa. "Efficient estimation of sensitivity indices." Journal of Nonparametric Statistics 25, no. 3 (2013): 573–95. http://dx.doi.org/10.1080/10485252.2013.784762.

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7

Seth, J. "Estimation of sensitivity of immunoassays." Clinical Chemistry 36, no. 1 (1990): 178. http://dx.doi.org/10.1093/clinchem/36.1.178a.

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8

East, P. W. "Microwave intercept receiver sensitivity estimation." IEE Proceedings - Radar, Sonar and Navigation 144, no. 4 (1997): 186. http://dx.doi.org/10.1049/ip-rsn:19971211.

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9

Heidergott, Bernd, Felisa J. Vázquez-Abad, and Warren Volk-Makarewicz. "Sensitivity estimation for Gaussian systems." European Journal of Operational Research 187, no. 1 (2008): 193–207. http://dx.doi.org/10.1016/j.ejor.2007.04.004.

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10

Mi, Lu, Hao Wang, Yonglong Tian, Hao He, and Nir N. Shavit. "Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10042–50. http://dx.doi.org/10.1609/aaai.v36i9.21243.

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Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it. Most previous methods are not able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it. To address this gap, we perform a systematic exploration into training-free uncertai

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11

Rachid, Ahmed, and Aiman Mazhar Qureshi. "Sensitivity Analysis of Heat Stress Indices." Climate 11, no. 9 (2023): 181. http://dx.doi.org/10.3390/cli11090181.

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More than 40 heat indices are being used across the world to quantify outdoor thermal comfort. The selection of an Outdoor Heat Stress Index (OHSI) depends on several parameters, including clothing, age, awareness, local environment, food consumption, human activities, and resources. This study investigates various indicators of heat stress, including (i) OHSIs officially used to quantify heat stress worldwide, (ii) the estimation methods of these indices, and (iii) the sensitivity analysis of indices, namely, Corrected Effective Temperature (CET), Heat Index (HI), Wet Bulb Globe Temperature (

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12

Tikkiwal, G. C., та Piyush Kant Rai. "A COMPOSITE ESTIMATOR FOR SMALL DOMAINS AND ITS SENSITIVITY INTERVAL FOR WEIGHTS α?" Statistics in Transition new series 10, № 2 (2009): 269–75. http://dx.doi.org/10.59170/stattrans-2009-020.

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The composite estimation methods, suggested in the literature, have major problems to deal with the estimation of optimum weights to combine synthetic and direct estimators. To take care of the absence of optimum weights, we have obtained the sensible interval of involved weights in the form of better performance interval of the weights with a view to retaining superiority for the different composite estimators i.e. for the direct ratio vs. synthetic ratio composite estimator and simple direct vs. synthetic ratio composite estimator.

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13

Li, Zhengzheng, Yan Zhang, and Scott E. Giangrande. "Rainfall-Rate Estimation Using Gaussian Mixture Parameter Estimator: Training and Validation." Journal of Atmospheric and Oceanic Technology 29, no. 5 (2012): 731–44. http://dx.doi.org/10.1175/jtech-d-11-00122.1.

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Abstract This study develops a Gaussian mixture rainfall-rate estimator (GMRE) for polarimetric radar-based rainfall-rate estimation, following a general framework based on the Gaussian mixture model and Bayes least squares estimation for weather radar–based parameter estimations. The advantages of GMRE are 1) it is a minimum variance unbiased estimator; 2) it is a general estimator applicable to different rain regimes in different regions; and 3) it is flexible and may incorporate/exclude different polarimetric radar variables as inputs. This paper also discusses training the GMRE and the sen

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14

Altman, Eitan, Izza Mounir, Fatim-Zahra Najid, and Samir M. Perlaza. "On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation." International Journal of Environmental Research and Public Health 17, no. 15 (2020): 5328. http://dx.doi.org/10.3390/ijerph17155328.

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In this paper, a formula for estimating the prevalence ratio of a disease in a population that is tested with imperfect tests is given. The formula is in terms of the fraction of positive test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true negatives (specificity). The motivation of this work arises in the context of the COVID-19 pandemic in which estimating the number of infected individuals depends on the sensitivity and specificity of the tests. In this context, it is shown that approximating the prevalence ratio by the ratio betwee

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15

Elie, Romuald. "Double Kernel Estimation of Sensitivities." Journal of Applied Probability 46, no. 3 (2009): 791–811. http://dx.doi.org/10.1239/jap/1253279852.

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In this paper we address the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution. In finance, for example, the sensitivities of the price of a contingent claim are called the Greeks. A new way of estimating the Greeks has recently been introduced in Elie, Fermanian and Touzi (2007) through a randomization of the parameter of interest combined with nonparametric estimation techniques. In this paper we study another type of estimator that turns out to be closely related to the score function, which is well k

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16

Elie, Romuald. "Double Kernel Estimation of Sensitivities." Journal of Applied Probability 46, no. 03 (2009): 791–811. http://dx.doi.org/10.1017/s002190020000588x.

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In this paper we address the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution. In finance, for example, the sensitivities of the price of a contingent claim are called the Greeks. A new way of estimating the Greeks has recently been introduced in Elie, Fermanian and Touzi (2007) through a randomization of the parameter of interest combined with nonparametric estimation techniques. In this paper we study another type of estimator that turns out to be closely related to the score function, which is well k

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17

Otis, Richard, Brandon Bocklund, and Zi‐Kui Liu. "Sensitivity estimation for calculated phase equilibria." Journal of Materials Research 36, no. 1 (2021): 140–50. http://dx.doi.org/10.1557/s43578-020-00073-6.

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AbstractThe development of a consistent framework for Calphad model sensitivity is necessary for the rational reduction of uncertainty via new models and experiments. In the present work, a sensitivity theory for Calphad was developed, and a closed‐form expression for the log‐likelihood gradient and Hessian of a multi‐phase equilibrium measurement was presented. The inherent locality of the defined sensitivity metric was mitigated through the use of Monte Carlo averaging. A case study of the Cr–Ni system was used to demonstrate visualizations and analyses enabled by the developed theory. Crite

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18

Castillo, Enrique, Carmen Castillo, Ali S. Hadi, and Jose Maria Sarabia. "Local Sensitivity Analysis in Estimation Problems." Journal of Computational and Graphical Statistics 17, no. 3 (2008): 703–25. http://dx.doi.org/10.1198/106186008x345062.

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19

Janon, Alexandre, Maëlle Nodet, and Clémentine Prieur. "Certified metamodels for sensitivity indices estimation." ESAIM: Proceedings 35 (March 2012): 234–38. http://dx.doi.org/10.1051/proc/201235020.

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20

Zhang, Yanjun, Lu Wang, and Jun Zhang. "Sensitivity evolution in quantum Hamiltonian estimation." International Journal of Systems, Control and Communications 9, no. 3 (2018): 255. http://dx.doi.org/10.1504/ijscc.2018.093402.

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21

Zhang, Jun, Lu Wang, and Yanjun Zhang. "Sensitivity evolution in quantum Hamiltonian estimation." International Journal of Systems, Control and Communications 9, no. 3 (2018): 255. http://dx.doi.org/10.1504/ijscc.2018.10013515.

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22

Collins, M. T., C. A. Speer, and B. R. Thompson. "Proper Estimation of Sensitivity and Specificity." Clinical and Vaccine Immunology 13, no. 12 (2006): 1373–74. http://dx.doi.org/10.1128/cvi.00283-06.

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23

Maume-Deschamps, Véronique, and Ibrahima Niang. "Estimation of quantile oriented sensitivity indices." Statistics & Probability Letters 134 (March 2018): 122–27. http://dx.doi.org/10.1016/j.spl.2017.10.019.

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24

Antoniano-Villalobos, Isadora, Emanuele Borgonovo, and Xuefei Lu. "Nonparametric estimation of probabilistic sensitivity measures." Statistics and Computing 30, no. 2 (2019): 447–67. http://dx.doi.org/10.1007/s11222-019-09887-9.

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25

Jiang, Guangxin, and Michael C. Fu. "Quantile sensitivity estimation for dependent sequences." Journal of Applied Probability 53, no. 3 (2016): 715–32. http://dx.doi.org/10.1017/jpr.2016.36.

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AbstractIn this paper we estimate quantile sensitivities for dependent sequences via infinitesimal perturbation analysis, and prove asymptotic unbiasedness, weak consistency, and a central limit theorem for the estimators under some mild conditions. Two common cases, the regenerative setting and ϕ-mixing, are analyzed further, and a new batched estimator is constructed based on regenerative cycles for regenerative processes. Two numerical examples, the G/G/1 queue and the Ornstein–Uhlenbeck process, are given to show the effectiveness of the estimator.

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26

Long, Kerrie J., Sue Ellen Haupt, and George S. Young. "Assessing sensitivity of source term estimation." Atmospheric Environment 44, no. 12 (2010): 1558–67. http://dx.doi.org/10.1016/j.atmosenv.2010.01.003.

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27

Finlayson, Graham, Maryam Mohammadzadeh Darrodi, and Michal Mackiewicz. "Rank-based camera spectral sensitivity estimation." Journal of the Optical Society of America A 33, no. 4 (2016): 589. http://dx.doi.org/10.1364/josaa.33.000589.

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28

Logofătu, Petre C. "Sensitivity analysis of grating parameter estimation." Applied Optics 41, no. 34 (2002): 7179. http://dx.doi.org/10.1364/ao.41.007179.

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29

Kang, M., U. Yang, and K. Sohn. "Spectral sensitivity estimation for EMCCD camera." Electronics Letters 47, no. 25 (2011): 1369–70. http://dx.doi.org/10.1049/el.2011.2501.

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30

Büttner, Carsten, Klaus Bobey, and Bernd Schlichting1. "Spectral Sensitivity Estimation of Digital Cameras." Conference on Colour in Graphics, Imaging, and Vision 3, no. 1 (2006): 70–74. http://dx.doi.org/10.2352/cgiv.2006.3.1.art00014.

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31

Jung, Youngsun, Ming Xue, and Guifu Zhang. "Simultaneous Estimation of Microphysical Parameters and the Atmospheric State Using Simulated Polarimetric Radar Data and an Ensemble Kalman Filter in the Presence of an Observation Operator Error." Monthly Weather Review 138, no. 2 (2010): 539–62. http://dx.doi.org/10.1175/2009mwr2748.1.

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Abstract The impacts of polarimetric radar data on the estimation of uncertain microphysical parameters are investigated through observing system simulation experiments when the effects of uncertain parameters on the observation operators are also considered. Five fundamental microphysical parameters (i.e., the intercept parameters of rain, snow, and hail and the bulk densities of snow and hail) are estimated individually or collectively using the ensemble square root Kalman filter. The differential reflectivity ZDR, specific differential phase KDP, and radar reflectivity at horizontal polariz

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32

Mohebbi, Farzad. "Function Estimation in Inverse Heat Transfer Problems Based on Parameter Estimation Approach." Energies 13, no. 17 (2020): 4410. http://dx.doi.org/10.3390/en13174410.

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A new sensitivity analysis scheme is presented based on explicit expressions for sensitivity coefficients to estimate timewise varying heat flux in heat conduction problems over irregular geometries using the transient readings of a single sensor. There is no prior information available on the functional form of the unknown heat flux; hence, the inverse problem is regarded as a function estimation problem and sensitivity and adjoint problems are involved in the solution of the inverse problem to recover the unknown heat flux. However, using the proposed sensitivity analysis scheme, one can com

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33

Flynn, Thomas. "Forward sensitivity analysis for contracting stochastic systems." Advances in Applied Probability 50, no. 01 (2018): 102–30. http://dx.doi.org/10.1017/apr.2018.6.

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Abstract In this paper we investigate gradient estimation for a class of contracting stochastic systems on a continuous state space. We find conditions on the one-step transitions, namely differentiability and contraction in a Wasserstein distance, that guarantee differentiability of stationary costs. Then we show how to estimate the derivatives, deriving an estimator that can be seen as a generalization of the forward sensitivity analysis method used in deterministic systems. We apply the results to examples, including a neural network model.

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34

Dupuy, Bastien, Stéphane Garambois, and Jean Virieux. "Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis." GEOPHYSICS 81, no. 3 (2016): M35—M53. http://dx.doi.org/10.1190/geo2015-0239.1.

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The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity

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35

Tominaga, Shoji, Shogo Nishi, and Ryo Ohtera. "Measurement and Estimation of Spectral Sensitivity Functions for Mobile Phone Cameras." Sensors 21, no. 15 (2021): 4985. http://dx.doi.org/10.3390/s21154985.

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Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions for mobile phone cameras are developed. In the direct measurement method, the spectral sensitivity at each wavelength is measured using monochromatic light. Although accurate, this method is time-consuming and expensive. The indirect estimation method is based on color samples, in which the

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36

Zhang, Meifan, Xin Liu, and Lihua Yin. "Sensitivity Estimation for Differentially Private Query Processing." Applied Sciences 15, no. 14 (2025): 7667. https://doi.org/10.3390/app15147667.

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Differential privacy is a robust framework for private data analysis and query processing, which achieves privacy preservation by introducing controlled noise to query results in a centralized setting. The sensitivity of a query, defined as the maximum change in query output resulting from the addition or removal of a single data record, directly influences the magnitude of noise to be introduced. Computing sensitivity for simple queries, such as count queries, is straightforward, but it becomes significantly more challenging for complex queries involving join operations. In such cases, the gl

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37

Vlasevskiy, V. N., A. E. Belkin, and I. I. Ivanov. "Bearings stiffness parameter estimation by sensitivity method." IOP Conference Series: Materials Science and Engineering 1129, no. 1 (2021): 012044. http://dx.doi.org/10.1088/1757-899x/1129/1/012044.

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38

E. Constante-Flores, Gonzalo, Antonio J. Conejo, and Jiankang Wang. "Sensitivity-based Vulnerability Assessment of State Estimation." Journal of Modern Power Systems and Clean Energy 9, no. 4 (2021): 886–96. http://dx.doi.org/10.35833/mpce.2020.000658.

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39

Gong, Xiaorong, Shudong Chen, and Shuang Zhang. "JOM-4S Overhauser Magnetometer and Sensitivity Estimation." Sensors 21, no. 22 (2021): 7698. http://dx.doi.org/10.3390/s21227698.

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The Overhauser magnetometer is a scalar quantum magnetometer based on the dynamic nuclear polarization (DNP) effect in the Earth’s magnetic field. Sensitivity is a key technical specification reflecting the ability of instruments to sense small variations of the Earth’s magnetic field and is closely related to the signal-to-noise ratio (SNR) of the free induction decay (FID) signal. In this study, deuterated 15N TEMPONE radical is used in our sensor to obtain high DNP enhancement. The measured SNR of the FID signal is approximately 63/1, and the transverse relaxation time T2 is 2.68 s. The dir

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40

YEH, FANG-BO, and THONG-SHING HWANG. "Optimal sensitivity bound estimation and controller design." International Journal of Control 47, no. 4 (1988): 979–84. http://dx.doi.org/10.1080/00207178808906070.

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41

Poe, Steven. "Sensitivity of Phylogeny Estimation to Taxonomic Sampling." Systematic Biology 47, no. 1 (1998): 18–31. http://dx.doi.org/10.1080/106351598261003.

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42

Owen, Art B. "Better estimation of small sobol' sensitivity indices." ACM Transactions on Modeling and Computer Simulation 23, no. 2 (2013): 1–17. http://dx.doi.org/10.1145/2457459.2457460.

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43

du Plessis, Hannes, and Paul van Rensburg. "Risk-based portfolio sensitivity to covariance estimation." Investment Analysts Journal 49, no. 3 (2020): 243–68. http://dx.doi.org/10.1080/10293523.2020.1806467.

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44

Lu, G. N., J. M. Galvan, C. Jeloyan, G. Goumy, and V. Marcoux. "Sensitivity estimation of CMOS optical BDJ detector." Materials Science and Engineering: C 21, no. 1-2 (2002): 203–10. http://dx.doi.org/10.1016/s0928-4931(02)00087-5.

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45

Buscaglia, G. C., R. A. Feijóo, and C. Padra. "A posteriori error estimation in sensitivity analysis." Structural Optimization 9, no. 3-4 (1995): 194–99. http://dx.doi.org/10.1007/bf01743969.

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46

Witten, Matthew, and William Satzer. "Gompertz survival model parameters: Estimation and sensitivity." Applied Mathematics Letters 5, no. 1 (1992): 7–12. http://dx.doi.org/10.1016/0893-9659(92)90125-s.

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47

Papaioannou, Iason, Karl Breitung, and Daniel Straub. "Reliability sensitivity estimation with sequential importance sampling." Structural Safety 75 (November 2018): 24–34. http://dx.doi.org/10.1016/j.strusafe.2018.05.003.

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48

Upadhyay, Anurag, Y. K. Prajapati, Vivek Singh, and J. P. Saini. "Sensitivity estimation of metamaterial loaded planar waveguide." Optical and Quantum Electronics 47, no. 7 (2014): 2277–87. http://dx.doi.org/10.1007/s11082-014-0104-z.

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49

Kazakov, V. G., S. N. Afanas'ev, V. A. Kukushkin, V. A. Ryzhov, and V. V. Suprunyuk. "Sensitivity estimation for a membrane-capacitance manometer." Measurement Techniques 29, no. 1 (1986): 7–9. http://dx.doi.org/10.1007/bf00862466.

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50

Chung, Chang-Bock. "Sensitivity behavior analysis in distributed parameter estimation." Korean Journal of Chemical Engineering 8, no. 4 (1991): 240–47. http://dx.doi.org/10.1007/bf02707190.

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