[Pomp-commits] r165 - www/content
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Tue Sep 22 23:05:54 CEST 2009
Author: kingaa
Date: 2009-09-22 23:05:54 +0200 (Tue, 22 Sep 2009)
New Revision: 165
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- add the references page
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+% This file was created with JabRef 2.5.
+% Encoding: ISO8859_1
+
+ at ARTICLE{Arulampalam2002,
+ author = {Arulampalam, M. S. and Maskell, S. and Gordon, N. and Clapp, T.},
+ title = {A Tutorial on Particle Filters for Online Nonlinear, Non-{G}aussian
+ {B}ayesian Tracking},
+ journal = {IEEE Transactions on Signal Processing},
+ year = {2002},
+ volume = {50},
+ pages = {174 -- 188},
+ doi = {10.1109/78.978374},
+ file = {Arulampalam2002.pdf:Desktop/Arulampalam2002.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2007.07.20}
+}
+
+ at ARTICLE{Breto2009,
+ author = {Carles Bret\'{o} and Daihai He and Edward L. Ionides and Aaron A.
+ King},
+ title = {Time series analysis via mechanistic models},
+ journal = {Annals of Applied Statistics},
+ year = {2009},
+ volume = {3},
+ pages = {319--348},
+ number = {1},
+ abstract = {The purpose of time series analysis via mechanistic models is to reconcile
+ the known or hypothesized structure of a dynamical system with observations
+ collected over time. We develop a framework for constructing nonlinear
+ mechanistic models and carrying out inference. Our framework permits
+ the consideration of implicit dynamic models, meaning statistical
+ models for stochastic dynamical systems which are specified by a
+ simulation algorithm to generate sample paths. Inference procedures
+ that operate on implicit models are said to have the plug-and-play
+ property. Our work builds on recently developed plug-and-play inference
+ methodology for partially observed Markov models. We introduce a
+ class of implicitly specified Markov chains with stochastic transition
+ rates, and we demonstrate its applicability to open problems in statistical
+ inference for biological systems. As one example, these models are
+ shown to give a fresh perspective on measles transmission dynamics.
+ As a second example, we present a mechanistic analysis of cholera
+ incidence data, involving interaction between two competing strains
+ of the pathogen Vibrio cholerae.},
+ doi = {10.1214/08-AOAS201},
+ file = {Breto2009.pdf:Breto2009.pdf:PDF;:Breto2009_supp.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2009.09.15}
+}
+
+ at 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},
+ title = {Noise and nonlinearity in measles epidemics: Combining mechanistic
+ and statistical approaches to population modeling},
+ journal = {American Naturalist},
+ year = {1998},
+ volume = {151},
+ pages = {425--440},
+ abstract = {We present and evaluate an approach to analyzing population dynamics
+ data using semimechanistic models. These models incorporate reliable
+ information on population structure and underlying dynamic mechanisms
+ but use nonparametric surface-fitting methods to avoid unsupported
+ assumptions about the precise form of rate equations. Using historical
+ data on measles epidemics as a case study, we show how this approach
+ can lead to better forecasts, better characterizations of the dynamics,
+ and a better understanding of the factors causing complex population
+ dynamics relative to either mechanistic models or purely descriptive
+ statistical time-series models. The semimechanistic models are found
+ to have better forecasting accuracy than either of the model types
+ used in previous analyses when tested on data not used to fit the
+ models. The dynamics are characterized as being both nonlinear and
+ noisy, and the global dynamics are clustered very tightly near the
+ border of stability (dominant Lyapunov exponent lambda approximate
+ to 0). However, locally in state space the dynamics oscillate between
+ strong short-term stability and strong short-term chaos (i.e., between
+ negative and positive local Lyapunov exponents). There is statistically
+ significant evidence for short-term chaos in all data sets examined.
+ Thus the nonlinearity in these systems is characterized by the variance
+ over state space in local measures of chaos versus stability rather
+ than a single summary measure of the overall dynamics as either chaotic
+ or nonchaotic.},
+ file = {Ellner1998.pdf:Ellner1998.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2009.09.22}
+}
+
+ at ARTICLE{He2009,
+ author = {He, Daihai and Ionides, Edward L. and King, Aaron A.},
+ title = {Plug-and-play inference for disease dynamics: measles in large and
+ small populations as a case study},
+ journal = {Journal of The Royal Society Interface},
+ year = {2009},
+ pages = {in press},
+ month = jun,
+ abstract = {Statistical inference for mechanistic models of partially observed
+ dynamic systems is an active area of research. Most existing inference
+ methods place substantial restrictions upon the form of models that
+ can be fitted and hence upon the nature of the scientific hypotheses
+ that can be entertained and the data that can be used to evaluate
+ them. In contrast, the so-called methods require only simulations
+ from a model and are thus free of such restrictions. We show the
+ utility of the plug-and-play approach in the context of an investigation
+ of measles transmission dynamics. Our novel methodology enables us
+ to ask and answer questions that previous analyses have been unable
+ to address. Specifically, we demonstrate that plug-and-play methods
+ permit the development of a modelling and inference framework applicable
+ to data from both large and small populations. We thereby obtain
+ novel insights into the nature of heterogeneity in mixing and comment
+ on the importance of including extra-demographic stochasticity as
+ a means of dealing with environmental stochasticity and model misspecification.
+ Our approach is readily applicable to many other epidemiological
+ and ecological systems.},
+ doi = {10.1098/rsif.2009.0151},
+ file = {He2009.pdf:He2009.pdf:PDF;He2009_Supplement.pdf:He2009_Supplement.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2009.06.26}
+}
+
+ at ARTICLE{Ionides2006,
+ author = {E. L. Ionides and C. Bret{\'o} and Aaron A. King},
+ title = {Inference for nonlinear dynamical systems},
+ journal = {Proceedings of the National Academy of Sciences of the United States
+ of America},
+ year = {2006},
+ volume = {103},
+ pages = {18438--18443},
+ number = {49},
+ abstract = {Nonlinear stochastic dynamical systems are widely used to model systems
+ across the sciences and engineering. Such models are natural to formulate
+ and can be analyzed mathematically and numerically. However, difficulties
+ associated with inference from time-series data about unknown parameters
+ in these models have been a constraint on their application. We present
+ a new method that makes maximum likelihood estimation feasible for
+ partially-observed nonlinear stochastic dynamical systems (also known
+ as state-space models) where this was not previously the case. The
+ method is based on a sequence of filtering operations which are shown
+ to converge to a maximum likelihood parameter estimate. We make use
+ of recent advances in nonlinear filtering in the implementation of
+ the algorithm. We apply the method to the study of cholera in Bangladesh.
+ We construct confidence intervals, perform residual analysis, and
+ apply other diagnostics. Our analysis, based upon a model capturing
+ the intrinsic nonlinear dynamics of the system, reveals some effects
+ overlooked by previous studies.},
+ doi = {10.1073/pnas.0603181103},
+ file = {Ionides2006.pdf:Ionides2006.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2006.10.06}
+}
+
+ at ARTICLE{Kendall1999,
+ author = {B. E. Kendall and C. J. Briggs and W. W. Murdoch and P. Turchin and
+ S. P. Ellner and E. McCauley and R. M. Nisbet and S. N. Wood},
+ title = {Why do populations cycle? A synthesis of statistical and mechanistic
+ modeling approaches},
+ journal = {Ecology},
+ year = {1999},
+ volume = {80},
+ pages = {1789--1805},
+ abstract = {Population cycles have long fascinated ecologists. Even in the most-studied
+ populations, however, scientists continue to dispute the relative
+ importance of various potential causes of the cycles, Over the past
+ three decades, theoretical ecologists have cataloged a large number
+ of mechanisms that are capable of generating cycles in population
+ models. At the same time, statisticians have developed new techniques
+ both for characterizing time series and for fitting population models
+ to time-series data. Both disciplines are now sufficiently advanced
+ that great gains in understanding can be made by synthesizing these
+ complementary, and heretofore mostly independent, quantitative approaches.
+ In this paper we demonstrate how to apply this synthesis to the problem
+ of population cycles, using both long-term population time series
+ and the often-rich observational and experimental data on the ecology
+ of the species in question. We quantify hypotheses by writing mathematical
+ models that embody the interactions and forces that might cause cycles.
+ Some hypotheses can be rejected out of hand, as being unable to generate
+ even qualitatively appropriate dynamics, We finish quantifying the
+ remaining hypotheses by estimating parameters, both from independent
+ experiments and from fitting the models to the time-series data using
+ modern statistical techniques, Finally, we compare simulated time
+ series generated by the models to the observed time series, using
+ a variety of statistical descriptors, which we refer to collectively
+ as "probes." The model most similar to the data, as measured by these
+ probes, is considered to be the most likely candidate to represent
+ the mechanism underlying the population cycles. We illustrate this
+ approach by analyzing one of Nicholson's blowfly populations, in
+ which we know the "true" governing mechanism. Our analysis, which
+ uses only a subset of the information available about the population,
+ uncovers the correct answer, suggesting that this synthetic approach
+ might be successfully applied to field populations as well.},
+ file = {Kendall1999.pdf:Kendall1999.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2009.09.22}
+}
+
+ at ARTICLE{Kendall2005,
+ author = {Kendall, B. E. and Ellner, S. P. and McCauley, E. and Wood, S. N.
+ and Briggs, C. J. and Murdoch, W. M. and Turchin, P.},
+ title = {Population cycles in the pine looper moth: Dynamical tests of mechanistic
+ hypotheses},
+ journal = {Ecological Monographs},
+ year = {2005},
+ volume = {75},
+ pages = {259--276},
+ number = {2},
+ abstract = {The forest insect pest Bupalus piniarius (pine looper moth) is a classic
+ example of a natural population cycle. As is typical for Populations
+ that exhibit regular oscillations in density, there are several biological
+ mechanisms that are hypothesized to be responsible for the cycles;
+ but despite several decades of detailed study there has been no definite
+ conclusion as to which mechanism is most important. We evaluated
+ three hypotheses for which there was direct experimental evidence:
+ (1) food quality (nutritional value of pine needles affected by defoliation);
+ (2) parasitoids (trophic interactions with specialist parasitoids),
+ and (3) maternal effects (maternal body size affects the performance
+ of offspring). We reviewed the empirical evidence for each of these
+ hypotheses and expressed each hypothesis in the form of a mechanistic
+ dynamic model. We used a nonlinear forecasting approach to fit each
+ model to three long-term Population time series in Britain that exhibit
+ some degree of regular cycling, and we used parametric bootstrap
+ to evaluate the significance of differences between models in their
+ goodness of fit to the data. The results differed among the three
+ forests: at Culbin, the parasitoid and maternal effects models fit
+ equally well; at Roseisle, the food quality and maternal effects
+ models fit equally well; and at Tentsmuir, the parasitoid model fit
+ best. However, the best-fit parasitism models required that the parasitism
+ rate vary between nearly 0 and nearly 1 during a cycle, greatly exceeding
+ the range of parasitism rates that have been observed in the field.
+ In contrast, the required variation in the observable maternal quality
+ variable (pupal mass) was within the range of empirical observations.
+ Under mild constraints on the parasitism rate (though allowing a
+ much wider range than has been measured in B. piniarius at any location),
+ the fit of the parasitism model fell off dramatically. The maternal
+ effects model then had uniformly strong support, outperforming the
+ constrained parasitism model at all three sites and the food quality
+ model at two; it performed slightly better than the food quality
+ model at the remaining site. This represents the first system in
+ which the maternal effects hypothesis for population cycles has been
+ supported by both strong biological and dynamical evidence.},
+ file = {Kendall2005.pdf:Kendall2005.pdf:PDF},
+ owner = {kingaa},
+ timestamp = {2009.09.22}
+}
+
+ at ARTICLE{King2008,
+ author = {King, Aaron A. and Ionides, Edward L. and Pascual, Mercedes and Bouma,
+ Menno J.},
+ title = {Inapparent infections and cholera dynamics},
+ journal = {Nature},
+ year = {2008},
+ volume = {454},
+ pages = {877--880},
+ number = {7206},
+ month = aug,
+ abstract = {In many infectious diseases, an unknown fraction of infections produce
+ symptoms mild enough to go unrecorded, a fact that can seriously
+ compromise the interpretation of epidemiological records. This is
+ true for cholera, a pandemic bacterial disease, where estimates of
+ the ratio of asymptomatic to symptomatic infections have ranged from
+ 3 to 100. In the absence of direct evidence, understanding of fundamental
+ aspects of cholera transmission, immunology and control has been
+ based on assumptions about this ratio and about the immunological
+ consequences of inapparent infections. Here we show that a model
+ incorporating high asymptomatic ratio and rapidly waning immunity,
+ with infection both from human and environmental sources, explains
+ 50 yr of mortality data from 26 districts of Bengal, the pathogen's
+ endemic home. We find that the asymptomatic ratio in cholera is far
+ higher than had been previously supposed and that the immunity derived
+ from mild infections wanes much more rapidly than earlier analyses
+ have indicated. We find, too, that the environmental reservoir (free-living
+ pathogen) is directly responsible for relatively few infections but
+ that it may be critical to the disease's endemicity. Our results
+ demonstrate that inapparent infections can hold the key to interpreting
+ the patterns of disease outbreaks. New statistical methods, which
+ allow rigorous maximum likelihood inference based on dynamical models
+ incorporating multiple sources and outcomes of infection, seasonality,
+ process noise, hidden variables and measurement error, make it possible
+ to test more precise hypotheses and obtain unexpected results. Our
+ experience suggests that the confrontation of time-series data with
+ mechanistic models is likely to revise our understanding of the ecology
+ of many infectious diseases.},
+ doi = {10.1038/nature07084},
+ file = {King2008.pdf:King2008.pdf:PDF;King2008_Supplement.pdf:King2008_Supplement.pdf:PDF},
+ owner = {kingaa},
+ publisher = {Macmillan Publishers Limited. All rights reserved},
+ timestamp = {2008.08.13}
+}
+
+ at comment{jabref-meta: selector_publisher:}
+
+ at comment{jabref-meta: selector_author:}
+
+ at comment{jabref-meta: selector_journal:}
+
+ at comment{jabref-meta: selector_keywords:}
+
Added: www/content/refs.htm
===================================================================
--- www/content/refs.htm (rev 0)
+++ www/content/refs.htm 2009-09-22 21:05:54 UTC (rev 165)
@@ -0,0 +1,50 @@
+<h2><a name="SECTIONREF">Bibliography</a></h2>
+
+<dl compact>
+ <dt><a name="Arulampalam2002">M. S. Arulampalam, S. Maskell, N. Gordon, & T. Clapp (2002).</a></dt>
+
+ <dd>`A tutorial on particle filters for online nonlinear, non-Gaussian Bayesian tracking.'<br>
+ <i>IEEE Transactions on Signal Processing</i> <b>50</b>:174 - 188.<br></dd>
+
+ <dt><a name="Breto2009">C. Bretó, D. He, E. L. Ionides, & A. A. King (2009).</a></dt>
+
+ <dd>`Time series analysis via mechanistic models.'<br>
+ <i>Annals of Applied Statistics</i> <b>3</b>(1):319-348.<br></dd>
+
+ <dt><a name="Ellner1998">S. P. Ellner, B. A. Bailey, G. V. Bobashev, A. R. Gallant, B. T. Grenfell, & D. W. Nychka (1998).</a></dt>
+
+ <dd>`Noise and nonlinearity in measles epidemics: Combining mechanistic and statistical approaches to population modeling.'<br>
+ <i>American Naturalist</i> <b>151</b>:425-440.<br></dd>
+
+ <dt><a name="He2009">D. He, E. L. Ionides, & A. A. King (2009).</a></dt>
+
+ <dd>`Plug-and-play inference for disease dynamics: measles in large and small populations as a case study.'<br>
+ <i>Journal of The Royal Society Interface</i> p. in press.<br></dd>
+
+ <dt><a name="Ionides2006">E. L. Ionides, C. Bretó, & A. A. King (2006).</a></dt>
+
+ <dd>`Inference for nonlinear dynamical systems.'<br>
+ <i>Proceedings of the National Academy of Sciences of the United States of America</i> <b>103</b>(49):18438-18443.<br></dd>
+
+ <dt><a name="Kendall1999">B. E. Kendall, C. J. Briggs, W. W. Murdoch, P. Turchin, S. P. Ellner, E. McCauley, R. M. Nisbet, & S. N. Wood (1999).</a></dt>
+
+ <dd>`Why do populations cycle? a synthesis of statistical and mechanistic modeling approaches.'<br>
+ <i>Ecology</i> <b>80</b>:1789-1805.<br></dd>
+
+ <dt><a name="Kendall2005">B. E. Kendall, S. P. Ellner, E. McCauley, S. N. Wood, C. J. Briggs, W. M. Murdoch, & P. Turchin (2005).</a></dt>
+
+ <dd>`Population cycles in the pine looper moth: Dynamical tests of mechanistic hypotheses.'<br>
+ <i>Ecological Monographs</i> <b>75</b>(2):259-276.<br></dd>
+
+ <dt><a name="King2008">A. A. King, E. L. Ionides, M. Pascual, & M. J. Bouma (2008).</a></dt>
+
+ <dd>`Inapparent infections and cholera dynamics.'<br>
+ <i>Nature</i> <b>454</b>(7206):877-880.<br></dd>
+</dl>
+
+<p><br></p>
+<hr>
+
+<address>
+ Aaron A. King 2009-09-22
+</address>
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