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Relevant bibliographies by topics / Tail probabilities

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Academic literature on the topic 'Tail probabilities'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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Journal articles on the topic "Tail probabilities"

1

Gerber, Hans U. "Martingales and Tail Probabilities." ASTIN Bulletin 24, no. 1 (1994): 145–46. http://dx.doi.org/10.2143/ast.24.1.2005086.

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Rukhin, Andrew L. "Estimating normal tail probabilities." Naval Research Logistics Quarterly 33, no. 1 (1986): 91–99. http://dx.doi.org/10.1002/nav.3800330108.

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Cai, Jun, and José Garrido. "A unified approach to the study of tail probabilities of compound distributions." Journal of Applied Probability 36, no. 04 (1999): 1058–73. http://dx.doi.org/10.1017/s0021900200017861.

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We consider the tail probabilities of a class of compound distributions. First, the relations between reliability distribution classes and heavy-tailed distributions are discussed. These relations reveal that many previous results on estimating the tail probabilities are not applicable to heavy-tailed distributions. Then, a generalized Wald's identity and identities for compound geometric distributions are presented in terms of renewal processes. Using these identities, lower and upper bounds for the tail probabilities are derived in a unified way for the class of compound distributions, both

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4

Cai, Jun, and José Garrido. "A unified approach to the study of tail probabilities of compound distributions." Journal of Applied Probability 36, no. 4 (1999): 1058–73. http://dx.doi.org/10.1239/jap/1032374755.

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We consider the tail probabilities of a class of compound distributions. First, the relations between reliability distribution classes and heavy-tailed distributions are discussed. These relations reveal that many previous results on estimating the tail probabilities are not applicable to heavy-tailed distributions.Then, a generalized Wald's identity and identities for compound geometric distributions are presented in terms of renewal processes. Using these identities, lower and upper bounds for the tail probabilities are derived in a unified way for the class of compound distributions, both u

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5

Lasserre, Jean B., and Henk Tijms. "Invariant Probabilities with Geometric Tail." Probability in the Engineering and Informational Sciences 10, no. 2 (1996): 213–21. http://dx.doi.org/10.1017/s0269964800004289.

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We present necessary and suffi2ient Foster-type conditions for a countable state Markov chain to have an invariant probability with at least a geometric tail. These conditions are obtained by using a generalized Farkas Theorem in Linear Algebra. The purpose of this note is also to pose an interesting and important research problem that is still largely open.

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Fudenberg, Drew, and David K. Levine. "Tail probabilities for triangular arrays." Journal of Dynamics & Games 1, no. 1 (2014): 45–56. http://dx.doi.org/10.3934/jdg.2014.1.45.

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LI, QUAN-LIN. "TAIL PROBABILITIES IN QUEUEING PROCESSES." Asia-Pacific Journal of Operational Research 31, no. 02 (2014): 1440007. http://dx.doi.org/10.1142/s0217595914400077.

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In the study of large scale stochastic networks with resource management, differential equations and mean-field limits are two key techniques. Recent research shows that the expected fraction vector (that is, the tail probability vector) plays a key role in setting up mean-field differential equations. To further apply the technique of tail probability vector to deal with resource management of large scale stochastic networks, this paper discusses tail probabilities in some basic queueing processes including QBD processes, Markov chains of GI/M/1 type and of M/G/1 type, and also provides some

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8

FRASER, D. A. S. "Tail probabilities from observed likelihoods." Biometrika 77, no. 1 (1990): 65–76. http://dx.doi.org/10.1093/biomet/77.1.65.

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9

De Schepper, A., and B. Heijnen. "General restrictions on tail probabilities." Journal of Computational and Applied Mathematics 64, no. 1-2 (1995): 177–88. http://dx.doi.org/10.1016/0377-0427(95)00014-3.

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Stachurski, John. "BOUNDING TAIL PROBABILITIES IN DYNAMIC ECONOMIC MODELS." Macroeconomic Dynamics 16, S1 (2011): 117–26. http://dx.doi.org/10.1017/s136510051100054x.

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This paper provides conditions for bounding tail probabilities in stochastic economic models in terms of their transition laws and shock distributions. Particular attention is given to conditions under which the tails of stationary equilibria have exponential decay. By way of illustration, the technique is applied to a threshold autoregression model of exchange rates.

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Dissertations / Theses on the topic "Tail probabilities"

1

Hao, Xuemiao. "Asymptotic tail probabilities of risk processes in insurance and finance." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/296.

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In this thesis we are interested in the impact of economic and financial factors, such as interest rate, tax payment, reinsurance, and investment return, on insurance business. The underlying risk models of insurance business that we consider range from the classical compound Poisson risk model to the newly emerging and more general Lévy risk model. In these risk models, we assume that the claim-size distribution belongs to some distribution classes according to its asymptotic tail behavior. We consider both light-tailed and heavy-tailed cases. Our study is through asymptotic tail probabilitie

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Dzindzalieta, Dainius. "Tight Bernoulli tail probability bounds." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140512_103743-38560.

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The purpose of the dissertation is to prove universal tight bounds for deviation from the mean probability inequalities for functions of random variables. Universal bounds shows that they are uniform with respect to some class of distributions and quantity of variables and other parameters. The bounds are called tight, if we can construct a sequence of random variables, such that the upper bounds are achieved. Such inequalities are useful for example in insurance mathematics, for constructing effective algorithms. We extend the results for Lipschitz functions on general probability metric spac

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3

Davis, John C. "Identification of the parameters when the density of the minimum is given." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002113.

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Dzindzalieta, Dainius. "Tiksliosios Bernulio tikimybių nelygybės." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140512_103759-89684.

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Disertacijos darbo tikslas – įrodyti universalias tiksliąsias nelygybes atsitiktinių dydžių funkcijų nukrypimo nuo vidurkio tikimybėms. Universalios nelygybės pažymi, kad jos yra tolygios pagal tam tikras bendras skirstinių klases ir pagal atsitiktinių dydžių kiekį, kartais ir pagal kitus parametrus. Nelygybės vadinamos tiksliosiomis, jeigu pavyksta sukonstruoti atsitiktinių dydžių seką, kuriai nelygybės virsta lygybėmis. Tokios nelygybės labai naudingos, pavyzdžiui, draudimo matematikoje, konstruojant efektyvius algoritmus. Disertaciją sudaro šeši skyriai. Pirmasis skyrius yra įvadas, kuriame

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Dominicy, Yves. "Quantile-based inference and estimation of heavy-tailed distributions." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209311.

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This thesis is divided in four chapters. The two first chapters introduce a parametric quantile-based estimation method of univariate heavy-tailed distributions and elliptical distributions, respectively. If one is interested in estimating the tail index without imposing a parametric form for the entire distribution function, but only on the tail behaviour, we propose a multivariate Hill estimator for elliptical distributions in chapter three. In the first three chapters we assume an independent and identically distributed setting, and so as a first step to a dependent setting, using quantiles

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Hao, Xuemiao Tang Qihe. "Asymptotic tail probabilities of risk processes in insurance and finance." 2009. http://ir.uiowa.edu/etd/296.

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Bonnet, Gilles. "Poisson hyperplane tessellation: Asymptotic probabilities of the zero and typical cells." Doctoral thesis, 2017. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017021715545.

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We consider the distribution of the zero and typical cells of a (homogeneous) Poisson hyperplane tessellation. We give a direct proof adapted to our setting of the well known Complementary Theorem. We provide sharp bounds for the tail distribution of the number of facets. We also improve existing bounds for the tail distribution of size measurements of the cells, such as the volume or the mean width. We improve known results about the generalised D.G. Kendall's problem, which asks about the shape of large cells. We also show that cells with many facets cannot be close to a lower dimensional co

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Books on the topic "Tail probabilities"

1

Yau, Wai Kwok. Saddlepoint approximations to the tail probabilities of general statistics. [s.n.], 1989.

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2

Wong, Augustine Chi-mou. Converting observed likelihood functions to tail probabilities for exponential linear models. [s.n.], 1990.

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3

Fraser, D. A. S. A simple general formula for tail probabilities for frequentist and Bayesian inference: Y D.A.S. Fraser and N. Reid and J. Wu. University of Toronto, 1996.

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4

Heavy-tail phenomena: Probabilistic and statistical modeling. Springer, 2007.

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5

Heads or tails?: Exploring probability through games. PowerKids Press, 2004.

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6

Dancing on the tails of the bell curve: Readings on the joy and power of statistics. Gordian Knot Books, 2013.

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7

Meerschaert, Mark M. Limit Distributions for Sums of Independent Random Vectors: Heavy Tails in Theory and Practice. John Wiley & Sons, 2001.

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8

Yau, Wai-Kwok. Saddlepoint approximations to the tail probabilities of the general statistics. 1989.

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9

Meerschaert, Mark M., and Hans-Peter Scheffler. Limit Distributions for Sums of Independent Random Vectors: Heavy Tails in Theory and Practice (Wiley Series in Probability and Statistics). Wiley-Interscience, 2001.

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Book chapters on the topic "Tail probabilities"

1

Kwok, Yue Kuen, and Wendong Zheng. "Saddlepoint Approximations to Density Functions, Tail Probabilities and Tail Expectations." In SpringerBriefs in Quantitative Finance. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74101-7_2.

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2

Cohen, Aviad, Yuri Rabinovich, Assaf Schuster, and Hadas Shachnai. "Optimal bounds on tail probabilities — a simplified approach." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64359-1_705.

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3

Krauth, J. "Lower Bounds for the Tail Probabilities of the Scan Statistic." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76307-6_9.

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4

Ganesh, A., and V. Anantharam. "Stationary Tail Probabilities in Exponential Server Tandems with Renewal Arrivals." In Stochastic Networks. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2418-9_18.

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5

Cohen, Aviad, Yuri Rabinovich, Assaf Schuster, and Hadas Shachnai. "Optimal Bounds on Tail Probabilities: A Study of an Approach." In Combinatorial Optimization. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3282-4_1.

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6

Glynn, Peter W., and Marcelo Torres. "Nonparametric Estimation of Tail Probabilities for the Single-Server Queue." In Stochastic Networks. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4062-4_7.

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7

De Muynck, Michiel, Herwig Bruneel, and Sabine Wittevrongel. "Tail Probabilities of the Delay Under a Reservation-Based Scheduling Mechanism." In Queueing Theory and Network Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68520-5_16.

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8

Krauth, J. "Bounds for the Tail Probabilities of the Linear Ratchet Scan Statistic." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46757-8_6.

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9

Glasserman, Paul. "Service Levels and Tail Probabilities in Multistage Capacitated Production-Inventory Systems." In International Series in Operations Research & Management Science. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4949-9_3.

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Kura, Satoshi, Natsuki Urabe, and Ichiro Hasuo. "Tail Probabilities for Randomized Program Runtimes via Martingales for Higher Moments." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17465-1_8.

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Conference papers on the topic "Tail probabilities"

1

KOHATSU-HIGA, ARTURO, and MAKOTO YAMAZATO. "ON TAIL PROBABILITIES OF STORAGE PROCESSES." In Proceedings of the International Conference. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702548_0029.

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Ghosh, Soumyadip, and Sandeep Juneja. "Computing Worst-Case Tail Probabilities Incredit Risk." In 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.323080.

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3

Adler, Robert J., Jose Blanchet, and Jingchen Liu. "Efficient simulation for tail probabilities of Gaussian random fields." In 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736085.

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4

Xu, Gongjun. "Uniformly efficient simulation for tail probabilities of Gaussian random fields." In 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7019918.

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5

Naess, Arvid, Bernt J. Leira, and Olexandr Batsevych. "Efficient Reliability Analysis of Structural Systems With a High Number of Limit States." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-21179.

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In principle, the reliability of complicated structural systems can be accurately predicted by standard Monte Carlo simulation methods, but the computational burden may be prohibitive. A new Monte Carlo based method for estimating system reliability that aims at reducing the computational cost is therefore proposed. It exploits the regularity of tail probabilities to set up an approximation procedure for the prediction of the far tail failure probabilities based on the estimates of the failure probabilities obtained by Monte Carlo simulation at more moderate levels. In the paper the usefulness

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Grant, Morgan R., and Dirk P. Kroese. "Efficient estimation of tail probabilities of the typical distance in preferential attachment models." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822101.

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Hui Yao, Leonardo Rojas-Nandayapa, and Thomas Taimre. "Estimating tail probabilities of random sums of infinite mixtures of phase-type distributions." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822102.

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8

"Estimating tail probabilities of random sums of scale mixture of phase-type random variables." In 24th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2021. http://dx.doi.org/10.36334/modsim.2021.a1.yao.

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Harlow, D. Gary. "Lower Tail Estimation of Fatigue Life." In ASME 2019 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/pvp2019-93104.

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Abstract Uncertainty in the prediction of lower tail fatigue life behavior is a combination of many causes, some of which are aleatoric and some of which are systemic. The error cannot be entirely eliminated or quantified due to microstructural variability, manufacturing processing, approximate scientific modeling, and experimental inconsistencies. The effect of uncertainty is exacerbated for extreme value estimation for fatigue life distributions because by necessity those events are rare. In addition, typically, there is a sparsity of data in the region of smaller stress levels in stress–lif

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Huang, Wenbo, Jiangang Mao, and Zhiyong Zhang. "The Maximum Entropy Estimation of Structural Reliability Based on Monte Carlo Simulation." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-24504.

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Taking the advantage of the high efficiency of Monte Carlo simulation for events of high failure probability, it is adopted to estimate the probabilities of failure of the reduced safe margins of structures. By assuming that the low tail of the probabilistic distribution of the safe margin to follow a Weibull distribution, the failure probabilities simulated are taken as empirical data to extrapolate the Weibull parameters. Among the candidate Weibull distributions, the maximum entropy is used to identify the optimum one which is used to predict the truth probabilities of failure of structures

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Reports on the topic "Tail probabilities"

1

Iyengar, Satish. Importance Sampling for Tail Probabilities. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada232412.

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Wang, Suojin, and H. L. Gray. Approximating Tail Probabilities of Noncentral Distributions. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada228426.

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3

Jekel JR, Charles Fredrick Romero, Vicente J. Conservative Estimation of Tail Probabilities from Limited Sample Data. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1605343.

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4

Nuttall, Albert H. Evaluation of Small Tail Probabilities Directly from the Characteristic Function. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada330541.

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5

Siegmund, David. Approximate Tail Probabilities for the Maxima of Some Random Fields. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada172729.

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6

Glynn, Peter W., and Ward Whitt. Logarithmic Asymptotics for Steady-State Tail Probabilities in a Single- Server Queue. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada266746.

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7

Hsing, Tailen. Extreme Value Theory for Suprema of Random Variables with Regularly Varying Tail Probabilities. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada179126.

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