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Tytuł artykułu

Optimality of the auxiliary particle filter

Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
In this article we study asymptotic properties of weighted samples produced by the auxiliary particle filter (APF) proposed by Pitt and Shephard [17]. Besides establishing a central limit theorem (CLT) for smoothed particle estimates, we also derive bounds on the Lp error and bias of the same for a finite particle sample size. By examining the recursive formula for the asymptotic variance of the CLT we identify first-stage importance weights for which the increase of asymptotic variance at a single iteration of the algorithm is minimal. In the light of these findings, we discuss and demonstrate on several examples how the APF algorithm can be improved.
Rocznik
Strony
1--28
Opis fizyczny
Bibliogr. 18 poz., wykr.
Twórcy
autor
  • Département CITI, Télécom SudParis, 9 Rue Charles Fourier, 91011 Evry Cedex, France
autor
  • Département TSI, Institut des Télécoms, Télécom ParisTech, 46 Rue Barrault, 75634 Paris Cedex 13, France
autor
  • Center of Mathematical Sciences, Lund University, Box 118, SE-22100, Lund, Sweden
Bibliografia
  • [1] T. Bollerslev, R. F. Engle and D. B. Nelson, ARCH models, in: The Handbook of Econometrics, Vol. 4, R. F. Engle and D. McFadden (Eds.), North-Holland, Amsterdam 1994, pp. 2959-3038.
  • [2] O. Cappé, É. Moulines and T. Rydén, Inference in Hidden Markov Models, Springer, New York 2005.
  • [3] N. Chopin, Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference, Ann. Statist. 32 (2004), pp. 2385-2411.
  • [4] P. Del Moral, Feyman-Kac Formulae. Genealogical and Interacting Particle Systems with Applications, Springer, New York 2004.
  • [5] R. Douc and É. Moulines, Limit theorems for weighted samples with applications to sequential Monte Carlo methods, Ann. Statist. 36 (2008), pp. 2344-2376.
  • [6] A. Doucet, N. de Freitas and N. Gordon, Sequential Monte Carlo Methods in Practice, Springer, New York 2001.
  • [7] A. Doucet and A. Johansen, A note on auxiliary particle filters, Statist. Probab. Lett. 78 (2008), pp. 1498-1504.
  • [8] P. Fearnhead, Sequential Monte Carlo Methods in Filter Theory, Ph. D. thesis, University of Oxford, 1998.
  • [9] N. J. Gordon, D. J. Salmond and A. F. M. Smith, Novel approach to non-linear/non-Gaussian Bayesian state estimation, IEEE Proc. Comm. Radar Signal Proc. 140 (1993), pp. 107-113.
  • [10] J. Hull and A. White, The pricing of options on assets with stochastic volatilities, J. Finance 42 (1987), pp. 281-300.
  • [11] M. Hürzeler and H. R. Künsch, Monte Carlo approximations for general state space models, J. Comput. Graph. Statist. 7 (1998), pp. 175-193.
  • [12] H. R. Künsch, Recursive Monte Carlo filters: algorithms and theoretical analysis, Ann. Statist. 33 (2005), pp. 1983-2021.
  • [13] J. Liu, Monte Carlo Strategies in Scientific Computing, Springer, New York 2001.
  • [14] J. Olsson, O. Cappé, R. Douc and É. Moulines, Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models, Bernoulli 14 (2008), pp. 155-179.
  • [15] J. Olsson, R. Douc and É. Moulines, Improving the two-stage sampling algorithm: a statistical perspective, in: On Bounds and Asymptotics of Sequential Monte Carlo Methods for Filtering, Smoothing, and Maximum Likelihood Estimation in State Space Models, Ph. D. thesis, Lund University, 2006, pp. 143-181.
  • [16] V. V. Petrov, Limit Theorems of Probability Theory, Springer, New York 1995.
  • [17] M. K. Pitt and N. Shephard, Filtering via simulation: Auxiliary particle filters, J. Amer. Statist. Assoc. 87 (1999), pp. 493-499.
  • [18] M. K. Pitt and N. Shephard, Time varying covariances: A factor stochastic volatility approach (with discussion), in: Bayesian Statistics, Vol. 6, J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (Eds.), Oxford University Press, Oxford 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-88b5c4f2-dd11-414d-8483-a9247112be16
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