Research output: Contribution to Journal/Magazine › Comment/debate › peer-review
Research output: Contribution to Journal/Magazine › Comment/debate › peer-review
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TY - JOUR
T1 - Discussion on the paper of 'Particle Markov chain Monte Carlo methods' by Christophe Andrieu, Arnaud Doucet, and Roman Holenstein
AU - Gonzalez Belmonte, Miguel Angel
AU - Papaspiliopoulos, Omiros
N1 - The paper was read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, October 14th, 2009.
PY - 2010
Y1 - 2010
N2 - We congratulate the authors for a remarkable paper, which addresses a problem of fundamental practical importance: parameter estimation in state space models by using sequential Monte Carlo (SMC) algorithms. In Belmonte et al. (2008) we fit duration state space models to high frequency transaction data and we require a computational methodology that can handle efficiently time series of length T =O.104–105/. We have experimented with particle Markov chain Monte Carlo (PMCMC) methods and with the smooth particle filter (SPF) of Pitt (2002). The latter is also based on the use of SMC algorithms to derive maximum likelihood parameter estimates; it is, however, limited to scalar signals. Therefore, in the context of duration modelling this limitation rules out multifactor or multi-dimensional models, and we believe that PMCMC methods can be very useful in such cases.
AB - We congratulate the authors for a remarkable paper, which addresses a problem of fundamental practical importance: parameter estimation in state space models by using sequential Monte Carlo (SMC) algorithms. In Belmonte et al. (2008) we fit duration state space models to high frequency transaction data and we require a computational methodology that can handle efficiently time series of length T =O.104–105/. We have experimented with particle Markov chain Monte Carlo (PMCMC) methods and with the smooth particle filter (SPF) of Pitt (2002). The latter is also based on the use of SMC algorithms to derive maximum likelihood parameter estimates; it is, however, limited to scalar signals. Therefore, in the context of duration modelling this limitation rules out multifactor or multi-dimensional models, and we believe that PMCMC methods can be very useful in such cases.
KW - Bayesian inference
KW - Markov chain Monte Carlo methods
KW - Sequential Monte Carlo methods
KW - State space models
U2 - 10.1111/j.1467-9868.2009.00736.x
DO - 10.1111/j.1467-9868.2009.00736.x
M3 - Comment/debate
VL - 72
SP - 308
EP - 310
JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)
JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)
SN - 1369-7412
IS - 3
ER -