Academic literature on the topic 'Non asymptotic bounds'
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Journal articles on the topic "Non asymptotic bounds":
Jiang, Yu Hang, Tong Liu, Zhiya Lou, Jeffrey S. Rosenthal, Shanshan Shangguan, Fei Wang, and Zixuan Wu. "Markov Chain Confidence Intervals and Biases." International Journal of Statistics and Probability 11, no. 1 (December 21, 2021): 29. http://dx.doi.org/10.5539/ijsp.v11n1p29.
Zhou, Lin, and Mehul Motani. "Non-Asymptotic Converse Bounds and Refined Asymptotics for Two Source Coding Problems." IEEE Transactions on Information Theory 65, no. 10 (October 2019): 6414–40. http://dx.doi.org/10.1109/tit.2019.2920893.
Xia, Dong. "Non-asymptotic bounds for percentiles of independent non-identical random variables." Statistics & Probability Letters 152 (September 2019): 111–20. http://dx.doi.org/10.1016/j.spl.2019.04.018.
Menozzi, Stéphane, and Vincent Lemaire. "On Some non Asymptotic Bounds for the Euler Scheme." Electronic Journal of Probability 15 (2010): 1645–81. http://dx.doi.org/10.1214/ejp.v15-814.
Yang, Xiaowei, Lu Pan, Kun Cheng, and Chao Liu. "Optimal Non-Asymptotic Bounds for the Sparse β Model." Mathematics 11, no. 22 (November 17, 2023): 4685. http://dx.doi.org/10.3390/math11224685.
Raj Jhunjhunwala, Prakirt, Daniela Hurtado-Lange, and Siva Theja Maguluri. "Exponential Tail Bounds on Queues: A Confluence of Non- Asymptotic Heavy Traffic and Large Deviations." ACM SIGMETRICS Performance Evaluation Review 51, no. 4 (February 22, 2024): 18–19. http://dx.doi.org/10.1145/3649477.3649488.
Függer, Matthias, Thomas Nowak, and Manfred Schwarz. "Tight Bounds for Asymptotic and Approximate Consensus." Journal of the ACM 68, no. 6 (December 31, 2021): 1–35. http://dx.doi.org/10.1145/3485242.
Gu, Yujie, and Ofer Shayevitz. "On the Non-Adaptive Zero-Error Capacity of the Discrete Memoryless Two-Way Channel." Entropy 23, no. 11 (November 15, 2021): 1518. http://dx.doi.org/10.3390/e23111518.
Lim, Fabian, and Vladimir Stojanovic. "On U-Statistics and Compressed Sensing II: Non-Asymptotic Worst-Case Analysis." Signal Processing, IEEE Transactions on 61, no. 10 (April 2013): 2486–97. http://dx.doi.org/10.1109/tsp.2013.2255041.
Cheng, Xu, Zhipeng Liao, and Ruoyao Shi. "On uniform asymptotic risk of averaging GMM estimators." Quantitative Economics 10, no. 3 (2019): 931–79. http://dx.doi.org/10.3982/qe711.
Dissertations / Theses on the topic "Non asymptotic bounds":
Minsker, Stanislav. "Non-asymptotic bounds for prediction problems and density estimation." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44808.
Schweizer, Nikolaus [Verfasser]. "Non-asymptotic Error Bounds for Sequential MCMC Methods / Nikolaus Schweizer." Bonn : Universitäts- und Landesbibliothek Bonn, 2012. http://d-nb.info/1044081546/34.
Donier-Meroz, Etienne. "Graphon estimation in bipartite networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG010.
Many real-world datasets can be represented as matrices where the entries represent interactions between two entities of different natures. These matrices are commonly known as adjacency matrices of bipartite graphs. In our work, we make the assumption that these interactions are determined by unobservable latent variables.Firstly, our main objective is to estimate the conditional expectation of the data matrix given the unobservable variables under the assumption that matrix entries are i.i.d. This estimation problem can be framed as estimating a bivariate function known as a graphon. In our study, we focus on two cases: piecewise constant graphons and Hölder-continuous graphons.We derive finite sample risk bounds for the least squares estimator. Additionally, we propose an adaptation of Lloyd's algorithm to compute an approximation this estimator and provide results from numerical experiments to evaluate the performance of these methods.Secondly, we address the limitations of the previous framework, which may not be suitable for modeling situations with bounded degrees of vertices, among other scenarios. Therefore, we extend our study to the relaxed independence assumption, where only the rows of the adjacency matrix are assumed to be independent. In this context, we specifically focus on piecewise constant graphons
Bacharach, Lucien. "Caractérisation des limites fondamentales de l'erreur quadratique moyenne pour l'estimation de signaux comportant des points de rupture." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS322/document.
This thesis deals with the study of estimators' performance in signal processing. The focus is the analysis of the lower bounds on the Mean Square Error (MSE) for abrupt change-point estimation. Such tools will help to characterize performance of maximum likelihood estimator in the frequentist context but also maximum a posteriori and conditional mean estimators in the Bayesian context. The main difficulty comes from the fact that, when dealing with sampled signals, the parameters of interest (i.e., the change points) lie on a discrete space. Consequently, the classical large sample theory results (e.g., asymptotic normality of the maximum likelihood estimator) or the Cramér-Rao bound do not apply. Some results concerning the asymptotic distribution of the maximum likelihood only are available in the mathematics literature but are currently of limited interest for practical signal processing problems. When the MSE of estimators is chosen as performance criterion, an important amount of work has been provided concerning lower bounds on the MSE in the last years. Then, several studies have proposed new inequalities leading to tighter lower bounds in comparison with the Cramér-Rao bound. These new lower bounds have less regularity conditions and are able to handle estimators’ MSE behavior in both asymptotic and non-asymptotic areas. The goal of this thesis is to complete previous results on lower bounds in the asymptotic area (i.e. when the number of samples and/or the signal-to-noise ratio is high) for change-point estimation but, also, to provide an analysis in the non-asymptotic region. The tools used here will be the lower bounds of the Weiss-Weinstein family which are already known in signal processing to outperform the Cramér-Rao bound for applications such as spectral analysis or array processing. A closed-form expression of this family is provided for a single and multiple change points and some extensions are given when the parameters of the distributions on each segment are unknown. An analysis in terms of robustness with respect to the prior influence on our models is also provided. Finally, we apply our results to specific problems such as: Gaussian data, Poisson data and exponentially distributed data
Brunnbauer, Michael. "Topological properties of asymptotic invariants and universal volume bounds." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-87504.
El, Korso Mohammed Nabil. "Analyse de performances en traitement d'antenne : bornes inférieures de l'erreur quadratique moyenne et seuil de résolution limite." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112074/document.
This manuscript concerns the performance analysis in array signal processing. It can bedivided into two parts :- First, we present the study of some lower bounds on the mean square error related to the source localization in the near eld context. Using the Cramér-Rao bound, we investigate the mean square error of the maximum likelihood estimator w.r.t. the direction of arrivals in the so-called asymptotic area (i.e., for a high signal to noise ratio with a nite number of observations.) Then, using other bounds than the Cramér-Rao bound, we predict the threshold phenomena.- Secondly, we focus on the concept of the statistical resolution limit (i.e., the minimum distance between two closely spaced signals embedded in an additive noise that allows a correct resolvability/parameter estimation.) We de ne and derive the statistical resolution limit using the Cramér-Rao bound and the hypothesis test approaches for the mono-dimensional case. Then, we extend this concept to the multidimensional case. Finally, a generalized likelihood ratio test based framework for the multidimensional statistical resolution limit is given to assess the validity of the proposed extension
Pinto, Manuel. "Des inegalites fonctionnelles et leurs applications." Université Louis Pasteur (Strasbourg) (1971-2008), 1988. http://www.theses.fr/1988STR13097.
Hadiji, Hédi. "On some adaptivity questions in stochastic multi-armed bandits." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM021.
The main topics adressed in this thesis lie in the general domain of sequential learning, and in particular stochastic multi-armed bandits. The thesis is divided into four chapters and an introduction. In the first part of the main body of the thesis, we design a new algorithm achieving, simultaneously, distribution-dependent and distribution-free optimal guarantees. The next two chapters are devoted to adaptivity questions. First, in the context of continuum-armed bandits, we present a new algorithm which, for the first time, does not require the knowledge of the regularity of the bandit problem it is facing. Then, we study the issue of adapting to the unknown support of the payoffs in bounded K-armed bandits. We provide a procedure that (almost) obtains the same guarantees as if it was given the support in advance. In the final chapter, we study a slightly different bandit setting, designed to enforce diversity-preserving conditions on the strategies. We show that the optimal regert in this setting at a speed that is quite different from the traditional bandit setting. In particular, we observe that bounded regret is possible under some specific hypotheses
Hussain, Zahir M. "Adaptive instantaneous frequency estimation: Techniques and algorithms." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36137/7/36137_Digitised%20Thesis.pdf.
Book chapters on the topic "Non asymptotic bounds":
Fujikoshi, Yasunori, and Vladimir V. Ulyanov. "Non-Asymptotic Bounds." In Non-Asymptotic Analysis of Approximations for Multivariate Statistics, 1–4. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2616-5_1.
Fujikoshi, Yasunori, and Vladimir V. Ulyanov. "General Approach to Constructing Non-Asymptotic Bounds." In Non-Asymptotic Analysis of Approximations for Multivariate Statistics, 117–30. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2616-5_11.
van de Geer, Sara A. "On non-asymptotic bounds for estimation in generalized linear models with highly correlated design." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 121–34. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000319.
Kahn, David M., and Jan Hoffmann. "Exponential Automatic Amortized Resource Analysis." In Lecture Notes in Computer Science, 359–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_19.
Sjöstrand, Johannes. "Proof I: Upper Bounds." In Non-Self-Adjoint Differential Operators, Spectral Asymptotics and Random Perturbations, 329–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10819-9_16.
Sjöstrand, Johannes. "Proof II: Lower Bounds." In Non-Self-Adjoint Differential Operators, Spectral Asymptotics and Random Perturbations, 363–407. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10819-9_17.
Sjöstrand, Johannes. "From Resolvent Estimates to Semigroup Bounds." In Non-Self-Adjoint Differential Operators, Spectral Asymptotics and Random Perturbations, 211–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10819-9_11.
Akahira, Masafumi, and Kei Takeuchi. "Supplement The Bound for the Asymptotic Distribution of Estimators when the Maximum Order of Consistency Depends on the Parameter." In Non-Regular Statistical Estimation, 166–73. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2554-6_8.
Nguyen, TrungTin, Dung Ngoc Nguyen, Hien Duy Nguyen, and Faicel Chamroukhi. "A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection." In Lecture Notes in Computer Science, 234–45. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8391-9_19.
FUJIKOSHI, Y. "Non-uniform Error Bounds for Asymptotic Expansions of Scale Mixtures of Distributions." In Multivariate Statistics and Probability, 194–205. Elsevier, 1989. http://dx.doi.org/10.1016/b978-0-12-580205-5.50020-3.
Conference papers on the topic "Non asymptotic bounds":
Matsuta, Tetsunao, and Tomohiko Uyematsu. "Non-asymptotic bounds for fixed-length lossy compression." In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282768.
Verdu, Sergio. "Non-asymptotic achievability bounds in multiuser information theory." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483192.
Zhang, Jianqiu. "Non-Asymptotic Capacity Lower Bounds for Non-coherent SISO Channels." In 2006 40th Annual Conference on Information Sciences and Systems. IEEE, 2006. http://dx.doi.org/10.1109/ciss.2006.286409.
Wei, Jiahui, Elsa Dupraz, and Philippe Mary. "Asymptotic and Non-Asymptotic Rate-Loss Bounds for Linear Regression with Side Information." In 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023. http://dx.doi.org/10.23919/eusipco58844.2023.10289952.
Fort, G., and E. Moulines. "The Perturbed Prox-Preconditioned Spider Algorithm: Non-Asymptotic Convergence Bounds." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513846.
Liebeherr, Jorg, Almut Burchard, and Florin Ciucu. "Non-asymptotic Delay Bounds for Networks with Heavy-Tailed Traffic." In IEEE INFOCOM 2010 - IEEE Conference on Computer Communications. IEEE, 2010. http://dx.doi.org/10.1109/infcom.2010.5461913.
Hayashi, Masahito, and Shun Watanabe. "Non-asymptotic bounds on fixed length source coding for Markov chains." In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013. http://dx.doi.org/10.1109/allerton.2013.6736617.
Heimann, Ron, Amir Leshem, Ephraim Zehavi, and Anthony J. Weiss. "Non-asymptotic performance bounds of eigenvalue based detection of signals in non-Gaussian noise." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472215.
Watanabe, Shun, Shigeaki Kuzuoka, and Vincent Y. F. Tan. "Non-asymptotic and second-order achievability bounds for source coding with side-information." In 2013 IEEE International Symposium on Information Theory (ISIT). IEEE, 2013. http://dx.doi.org/10.1109/isit.2013.6620787.
Alaeddini, Atiye, Siavash Alemzadeh, Afshin Mesbahi, and Mehran Mesbahi. "Linear Model Regression on Time-series Data: Non-asymptotic Error Bounds and Applications." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619074.
Reports on the topic "Non asymptotic bounds":
Petrova, Katerina. On the Validity of Classical and Bayesian DSGE-Based Inference. Federal Reserve Bank of New York, January 2024. http://dx.doi.org/10.59576/sr.1084.