[Pomp-commits] r775 - www/content
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Aug 21 18:25:29 CEST 2012
Author: kingaa
Date: 2012-08-21 18:25:29 +0200 (Tue, 21 Aug 2012)
New Revision: 775
Modified:
www/content/pomp.bib
Log:
- update bibliography database
Modified: www/content/pomp.bib
===================================================================
--- www/content/pomp.bib 2012-08-14 14:24:21 UTC (rev 774)
+++ www/content/pomp.bib 2012-08-21 16:25:29 UTC (rev 775)
@@ -1,5 +1,5 @@
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@ARTICLE{Andrieu2010,
author = {Andrieu, Christophe and Doucet, Arnaud and Holenstein, Roman},
@@ -89,6 +89,47 @@
timestamp = {2009.09.15}
}
+ at ARTICLE{Breto2011,
+ author = {Bret{\'o}, Carles and Ionides, Edward L.},
+ title = {Compound Markov counting processes and their applications to modeling
+ infinitesimally over-dispersed systems},
+ journal = {Stochastic Processes and their Applications},
+ year = {2011},
+ volume = {121},
+ pages = {2571--2591},
+ number = {11},
+ month = nov,
+ __markedentry = {[kingaa:]},
+ abstract = {We propose an infinitesimal dispersion index for Markov counting processes.
+ We show that, under standard moment existence conditions, a process
+ is infinitesimally (over-)equi-dispersed if, and only if, it is simple
+ (compound), i.e. it increases in jumps of one (or more) unit(s),
+ even though infinitesimally equi-dispersed processes might be under-,
+ equi- or over-dispersed using previously studied indices. Compound
+ processes arise, for example, when introducing continuous-time white
+ noise to the rates of simple processes resulting in L{\'e}vy-driven
+ SDEs. We construct multivariate infinitesimally over-dispersed compartment
+ models and queuing networks, suitable for applications where moment
+ constraints inherent to simple processes do not hold.},
+ doi = {10.1016/j.spa.2011.07.005},
+ file = {Breto2011.pdf:Breto2011.pdf:PDF},
+ issn = {0304-4149},
+ keywords = {Continuous time, Counting Markov process, Birth–death process, Environmental
+ stochasticity, Infinitesimal over-dispersion, Simultaneous events},
+ owner = {kingaa},
+ timestamp = {2012.01.17}
+}
+
+ at BOOK{Davison1997,
+ title = {{B}ootstrap {M}ethods and their {A}pplication},
+ publisher = {Cambridge University Press},
+ year = {1997},
+ author = {A.C. Davison and D.V. Hinkley},
+ address = {New York},
+ owner = {kingaa},
+ timestamp = {2011.05.26}
+}
+
@ARTICLE{Ellner1998,
author = {S. P. Ellner and B. A. Bailey and G. V. Bobashev and A. R. Gallant
and B. T. Grenfell and D. W. Nychka},
@@ -138,6 +179,15 @@
timestamp = {2007.03.13}
}
+ at BOOK{Gourieroux1996,
+ title = {{S}imulation-based {E}conometric {M}ethods},
+ publisher = {Oxford University Press},
+ year = {1996},
+ author = {C. Gouri\'{e}roux and A. Monfort},
+ owner = {kingaa},
+ timestamp = {2011.05.26}
+}
+
@ARTICLE{He2010,
author = {He, Daihai and Ionides, Edward L. and King, Aaron A.},
title = {{P}lug-and-play inference for disease dynamics: measles in large
@@ -171,6 +221,35 @@
timestamp = {2009.06.26}
}
+ at ARTICLE{Ionides2011,
+ author = {E. L. Ionides and A. Bhadra and Y. Atchad{\'e} and A. A. King},
+ title = {Iterated filtering},
+ journal = {Annals of Statistics},
+ year = {2011},
+ volume = {39},
+ pages = {1776--1802},
+ number = {3},
+ abstract = {Inference for partially observed Markov process models has been a
+ longstanding methodological challenge with many scientific and engineering
+ applications. Iterated filtering algorithms maximize the likelihood
+ function for partially observed Markov process models by solving
+ a recursive sequence of filtering problems. We present new theoretical
+ results pertaining to the convergence of iterated filtering algorithms
+ implemented via sequential Monte Carlo filters. This theory complements
+ the growing body of empirical evidence that iterated filtering algorithms
+ provide an effective inference strategy for scientific models of
+ nonlinear dynamic systems. The first step in our theory involves
+ studying a new recursive approach for maximizing the likelihood function
+ of a latent variable model, when this likelihood is evaluated via
+ importance sampling. This leads to the consideration of an iterated
+ importance sampling algorithm which serves as a simple special case
+ of iterated filtering, and may have applicability in its own right.},
+ doi = {10.1214/11-AOS886},
+ file = {Ionides2011.pdf:Ionides2011.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2011.03.07}
+}
+
@ARTICLE{Ionides2006,
author = {E. L. Ionides and C. Bret{\'o} and Aaron A. King},
title = {{I}nference for nonlinear dynamical systems},
@@ -338,6 +417,46 @@
timestamp = {2008.08.13}
}
+ at ARTICLE{Knape2011,
+ author = {Knape, Jonas and de Valpine, Perry},
+ title = {Fitting complex population models by combining particle filters with
+ Markov chain Monte Carlo},
+ journal = {Ecology},
+ year = {2011},
+ volume = {93},
+ pages = {256--263},
+ number = {2},
+ month = oct,
+ __markedentry = {[kingaa:]},
+ abstract = {We show how a recent framework combining Markov chain Monte Carlo
+ (MCMC) with particle filters (PFMCMC) may be used to estimate population
+ state-space models. With the purpose of utilizing the strengths of
+ each method, PFMCMC explores hidden states by particle filters, while
+ process and observation parameters are estimated using an MCMC algorithm.
+ PFMCMC is exemplified by analyzing time series data on a red kangaroo
+ (Macropus rufus) population in New South Wales, Australia, using
+ MCMC over model parameters based on an adaptive Metropolis-Hastings
+ algorithm. We fit three population models to these data; a density-dependent
+ logistic diffusion model with environmental variance, an unregulated
+ stochastic exponential growth model, and a random-walk model. Bayes
+ factors and posterior model probabilities show that there is little
+ support for density dependence and that the random-walk model is
+ the most parsimonious model. The particle filter Metropolis-Hastings
+ algorithm is a brute-force method that may be used to fit a range
+ of complex population models. Implementation is straightforward and
+ less involved than standard MCMC for many models, and marginal densities
+ for model selection can be obtained with little additional effort.
+ The cost is mainly computational, resulting in long running times
+ that may be improved by parallelizing the algorithm.},
+ booktitle = {Ecology},
+ doi = {10.1890/11-0797.1},
+ file = {:Knape2011.pdf:PDF},
+ issn = {0012-9658},
+ owner = {kingaa},
+ publisher = {Ecological Society of America},
+ timestamp = {2012.06.01}
+}
+
@INCOLLECTION{Liu2001b,
author = {Liu, J and West, M.},
title = {{C}ombining {P}arameter and {S}tate {E}stimation in {S}imulation-{B}ased
@@ -363,6 +482,55 @@
url = {http://dx.doi.org/10.1073/pnas.0608571103}
}
+ at ARTICLE{Smith1993,
+ author = {A. A. Smith},
+ title = {{E}stimating nonlinear time-series models using simulated vector
+ autoregression},
+ journal = {Journal of Applied Econometrics},
+ year = {1993},
+ volume = {8},
+ pages = {S63--S84},
+ owner = {kingaa},
+ timestamp = {2011.05.26}
+}
+
+ at ARTICLE{Tidd1993,
+ author = {Tidd, C. W. and Olsen, L. F. and Schaffer, W. M.},
+ title = {{T}he {C}ase for {C}haos in {C}hildhood {E}pidemics. {II}. {P}redicting
+ {H}istorical {E}pidemics from {M}athematical {M}odels},
+ journal = {Proceedings of the Royal Society of London, Series B},
+ year = {1993},
+ volume = {254},
+ pages = {257--273},
+ number = {1341},
+ month = {Dec.},
+ abstract = {The case for chaos in childhood epidemics rests on two observations.
+ The first is that historical epidemics show various `fieldmarks'
+ of chaos, such as positive Lyapunov exponents. Second, phase portraits
+ reconstructed from real-world epidemiological time series bear a
+ striking resemblance to chaotic solutions obtained from certain epidemiological
+ models. Both lines of evidence are subject to dispute: the algorithms
+ used to look for the fieldmarks can be fooled by short, noisy time
+ series, and the same fieldmarks can be generated by stochastic models
+ in which there is demonstrably no chaos at all. In the present paper,
+ we compare the predictive abilities of stochastic models with those
+ of mechanistic scenarios that admit to chaotic solutions. The main
+ results are as follows: (i) the mechanistic models outperform their
+ stochastic counterparts; (ii) forecasting efficacy of the deterministic
+ models is maximized by positing parameter values that induce chaotic
+ behaviour; (iii) simple mechanistic models are equal if not superior
+ to more detailed schemes that include age structure; and (iv) prediction
+ accuracy for monthly notifications declines rapidly with time, so
+ that, from a practical standpoint, the results are of little value.
+ By way of contrast, next amplitude maps can successfully forecast
+ successive changes in maximum incidence one or more years into the
+ future.},
+ file = {Tidd1993.pdf:Tidd1993.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2009.09.22},
+ url = {http://links.jstor.org/sici?sici=0962-8452%2819931222%29254%3A1341%3C257%3ATCFCIC%3E2.0.CO%3B2-G}
+}
+
@ARTICLE{Wearing2009,
author = {Helen J Wearing and Pejman Rohani},
title = {{E}stimating the duration of pertussis immunity using epidemiological
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