Literatura científica selecionada sobre o tema "Misspecified bounds"
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Artigos de revistas sobre o assunto "Misspecified bounds"
Liu, Changyu, Yuling Jiao, Junhui Wang e Jian Huang. "Nonasymptotic Bounds for Adversarial Excess Risk under Misspecified Models". SIAM Journal on Mathematics of Data Science 6, n.º 4 (1 de outubro de 2024): 847–68. http://dx.doi.org/10.1137/23m1598210.
Texto completo da fonteTeichner, Ron, e Ron Meir. "Kalman smoother error bounds in the presence of misspecified measurements". IFAC-PapersOnLine 56, n.º 2 (2023): 10252–57. http://dx.doi.org/10.1016/j.ifacol.2023.10.907.
Texto completo da fonteFudenberg, Drew, Giacomo Lanzani e Philipp Strack. "Pathwise concentration bounds for Bayesian beliefs". Theoretical Economics 18, n.º 4 (2023): 1585–622. http://dx.doi.org/10.3982/te5206.
Texto completo da fonteLu, Shuai, Peter Mathé e Sergiy Pereverzyev. "Analysis of regularized Nyström subsampling for regression functions of low smoothness". Analysis and Applications 17, n.º 06 (23 de setembro de 2019): 931–46. http://dx.doi.org/10.1142/s0219530519500039.
Texto completo da fonteWang, Ke, e Hong Yue. "Sampling Time Design with Misspecified Cramer-Rao Bounds under Input Uncertainty". IFAC-PapersOnLine 58, n.º 14 (2024): 622–27. http://dx.doi.org/10.1016/j.ifacol.2024.08.406.
Texto completo da fonteFortunati, Stefano, Fulvio Gini, Maria S. Greco e Christ D. Richmond. "Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental Findings and Applications". IEEE Signal Processing Magazine 34, n.º 6 (novembro de 2017): 142–57. http://dx.doi.org/10.1109/msp.2017.2738017.
Texto completo da fonteSalmon, Mark. "EDITOR'S INTRODUCTION". Macroeconomic Dynamics 6, n.º 1 (fevereiro de 2002): 1–4. http://dx.doi.org/10.1017/s1365100502027013.
Texto completo da fonteCheng, Xu, Zhipeng Liao e Ruoyao Shi. "On uniform asymptotic risk of averaging GMM estimators". Quantitative Economics 10, n.º 3 (2019): 931–79. http://dx.doi.org/10.3982/qe711.
Texto completo da fonteBanerjee, Imon, Vinayak A. Rao e Harsha Honnappa. "PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models". Entropy 23, n.º 3 (6 de março de 2021): 313. http://dx.doi.org/10.3390/e23030313.
Texto completo da fonteOrtega, Lorenzo, Corentin Lubeigt, Jordi Vilà-Valls, e Eric Chaumette. "On GNSS Synchronization Performance Degradation under Interference Scenarios: Bias and Misspecified Cramér-Rao Bounds". NAVIGATION: Journal of the Institute of Navigation 70, n.º 4 (2023): navi.606. http://dx.doi.org/10.33012/navi.606.
Texto completo da fonteTeses / dissertações sobre o assunto "Misspecified bounds"
McPhee, Hamish. "Algorithme d'échelle de temps autonome et robuste pour un essaim de nanosatellites". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP094.
Texto completo da fonteA new robust time scale algorithm, the Autonomous Time scale using the Student's T-distribution (ATST), has been proposed and validated using simulated clock data. Designed for use in a nanosatellite swarm, ATST addresses phase jumps, frequency jumps, anomalous measurement noise, and missing data by making a weighted average of the residuals contained in the Basic Time Scale Equation (BTSE). The weights come from an estimator that assumes the BTSE residuals are modeled by a Student's t-distribution.Despite not detecting anomalies explicitly, the ATST algorithm performs similarly to a version of the AT1 time scale that detects anomalies perfectly in simulated data. However, ATST is best for homogeneous clock types, requires a high number of clocks, adds computational complexity, and cannot necessarily differentiate anomaly types. Despite these identified limitations the robustness achieved is a promising contribution to the field of time scale algorithms.The implementation of ATST includes a method that maintains phase and frequency continuity when clocks are removed or reintroduced into the ensemble by resetting appropriate clock weights to zero. A Least Squares (LS) estimator is also presented to pre-process inter-satellite measurements, reducing noise and estimating missing data. The LS estimator is also compatible with anomaly detection which removes anomalous inter-satellite measurements because it can replace the removed measurements with their estimates.The thesis also explores optimal estimation of parameters of two heavy-tailed distributions: the Student's t and Bimodal Gaussian mixture. The Misspecified Cramér Rao Bound (MCRB) confirms that assuming heavy-tailed distributions handles outliers better compared to assuming a Gaussian distribution. We also observe that at least 25 clocks are required for asymptotic efficiency when estimating the mean of the clock residuals. The methodology also aids in analyzing other anomaly types fitting different distributions.Future research proposals include addressing ATST's limitations with diverse clock types, mitigating performance loss with fewer clocks, and exploring robust time scale generation using machine learning to weight BTSE residuals. Transient anomalies can be targeted using machine learning or even a similar method of robust estimation of clock frequencies over a window of past data. This is interesting to research and compare to the ATST algorithm that is instead proposed for instantaneous anomalies
"Bayesian Framework for Sparse Vector Recovery and Parameter Bounds with Application to Compressive Sensing". Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55639.
Texto completo da fonteDissertation/Thesis
Masters Thesis Computer Engineering 2019
Capítulos de livros sobre o assunto "Misspecified bounds"
Fortunati, Stefano, Fulvio Gini e Maria S. Greco. "Parameter bounds under misspecified models for adaptive radar detection". In Academic Press Library in Signal Processing, Volume 7, 197–252. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-811887-0.00004-3.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Misspecified bounds"
McPhee, Hamish, Jean-Yves Tourneret, David Valat, Jérôme Delporte, Yoan Grégoire e Philippe Paimblanc. "Misspecified Cramér-Rao Bounds for Anomalous Clock Data in Satellite Constellations". In 2024 32nd European Signal Processing Conference (EUSIPCO), 1222–26. IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715422.
Texto completo da fonteRichmond, Christ D., e Larry L. Horowitz. "Parameter bounds under misspecified models". In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810254.
Texto completo da fonteDiong, M. L., E. Chaumette e F. Vincent. "Generalized Barankin-type lower bounds for misspecified models". In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953001.
Texto completo da fonteRichmond, Christ D., e Abdulhakim Alhowaish. "On Misspecified Parameter Bounds with Application to Sparse Bayesian Learning". In 2020 54th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf51394.2020.9443550.
Texto completo da fonteFortunati, Stefano. "Misspecified Cramér-rao bounds for complex unconstrained and constrained parameters". In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081488.
Texto completo da fonteParker, Peter A., e Christ D. Richmond. "Methods and bounds for waveform parameter estimation with a misspecified model". In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421439.
Texto completo da fonteRichmond, Christ D., e Prabahan Basu. "Bayesian framework and radar: On misspecified bounds and radar-communication cooperation". In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551792.
Texto completo da fonteTeichner, Ron, e Ron Meir. "Discrete-Time Kalman Filter Error Bounds in the Presence of Misspecified Measurements". In 2023 European Control Conference (ECC). IEEE, 2023. http://dx.doi.org/10.23919/ecc57647.2023.10178341.
Texto completo da fonteHabi, Hai Victor, Hagit Messer e Yoram Bresler. "Learned Generative Misspecified Lower Bound". In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095336.
Texto completo da fonteRosentha, Nadav E., e Joseph Tabrikian. "Asymptotically Tight Misspecified Bayesian Cramér-Rao Bound". In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10448099.
Texto completo da fonte