Imperial College London

ProfessorEmmaMcCoy

Faculty of Natural SciencesDepartment of Mathematics

Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8553e.mccoy Website

 
 
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Assistant

 

Ms Mary Scott +44 (0)20 7594 5477

 
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Location

 

552Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

20 results found

Tzouras S, Anagnostopoulos C, Mccoy E, 2015, Financial time series modeling using the Hurst exponent, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, Vol: 425, Pages: 50-68, ISSN: 0378-4371

Journal article

Graham DJ, McCoy EJ, Stephens DA, 2015, Approximate Bayesian Inference for Doubly Robust Estimation, Bayesian Analysis, Vol: 11, Pages: 47-69, ISSN: 1936-0975

Doubly robust estimators are typically constructed by combining outcomeregression and propensity score models to satisfy moment restrictions thatensure consistent estimation of causal quantities provided at least one of the componentmodels is correctly specified. Standard Bayesian methods are difficult toapply because restricted moment models do not imply fully specified likelihoodfunctions. This paper proposes a Bayesian bootstrap approach to derive approximateposterior predictive distributions that are doubly robust for estimation ofcausal quantities. Simulations show that the approach performs well under varioussources of misspecification of the outcome regression or propensity score models.The estimator is applied in a case study of the effect of area deprivation on theincidence of child pedestrian casualties in British cities.

Journal article

McCoy EJ, Graham DJ, Stephens DA, 2014, Quantifying Causal Effects of Road Network CapacityExpansions on Traffic Volume and Density via a MixedModel Propensity Score Estimator, Journal of the American Statistical Association, Vol: 109, ISSN: 1537-274X

Road network capacity expansions are frequently proposed as solutions to ur-ban traffic congestion but are controversial because it is thought that theycan directly ‘induce’ growth in traffic volumes. This paper quantifies causaleffects of road network capacity expansions on aggregate urban traffic volumeand density in US cities using a mixed model propensity score (PS) estimator.The motivation for this approach is that we seek to estimate a dose-responserelationship between capacity and volume but suspect confounding from bothobserved and unobserved characteristics. Analytical results and simulationsshow that a longitudinal mixed model PS approach can be used to adjust ef-fectively for time-invariant unobserved confounding via random effects. Ourempirical results indicate that network capacity expansions can cause substan-tial increases in aggregate urban traffic volumes such that even major capacityincreases can actually lead to little or no reduction in network traffic densi-ties. This result has important implications for optimal urban transportationstrategies.

Journal article

Martin JS, Jasra A, Singh SS, Whiteley N, Del Moral P, McCoy Eet al., 2014, Approximate bayesian computation for smoothing, Stochastic Analysis and Applications, Vol: 32, Pages: 397-420, ISSN: 0736-2994

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter ε > 0. We provide theoretical results which quantify, in terms of ε, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is, where n is the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. © Taylor & Francis Group, LLC.

Journal article

Martin JS, Jasra A, McCoy E, 2013, Inference for a class of partially observed point process models, Annals of the Institute of Statistical Mathematics, Vol: 65, Pages: 413-437, ISSN: 0020-3157

This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where current methods often fail. We consider various approaches for approximating posterior distributions using SMC. Our approaches, with some theoretical discussion are illustrated on a doubly stochastic point process applied in the context of finance.

Journal article

Jasra A, Singh SS, Martin JS, McCoy Eet al., 2012, Filtering via approximate Bayesian computation, STATISTICS AND COMPUTING, Vol: 22, Pages: 1223-1237, ISSN: 0960-3174

Journal article

Graham DJ, McCoy EJ, Stephens DA, 2012, Quantifying the effect of area deprivation on child pedestrian casualties by using longitudinal mixed models to adjust for confounding, interference and spatial independence, Journal of the Royal Statistical Society Series A-Statistics in Society, Vol: 176, ISSN: 0964-1998

Journal article

Yang Z, Walden AT, McCoy EJ, 2011, Correntropy: Implications of nonGaussianity for the moment expansion and deconvolution, Signal Processing, Vol: 91, Pages: 864-876

The recently introduced correntropy function is an interesting and useful similarity measure between two random variables which has found myriad applications in signal processing. A series expansion for correntropy in terms of higher-order moments of the difference between the two random variables has been used to try to explain its statistical properties for uses such as deconvolution. We examine the existence and form of this expansion, showing that it may be divergent, e.g., when the difference has the Laplace distribution, and give sufficient conditions for its existence for differently characterized sub-Gaussian distributions. The contribution of the higher-order moments can be quite surprising, depending on the size of the Gaussian kernel in the definition of the correntropy. In the blind deconvolution setting we demonstrate that statistical exchangeability explains the existence of sub-optimal minima in the correntropy cost surface and show how the positions of these minima are controlled by the size of the Gaussian kernel.

Journal article

Au-Yeung SWM, Harder U, Mccoy EJ, Knottenbelt WJet al., 2009, Predicting patient arrivals to an accident and emergency department, EMERGENCY MEDICINE JOURNAL, Vol: 26, Pages: 241-244, ISSN: 1472-0205

Journal article

McCoy EJ, Stephens DA, 2004, Bayesian time series analysis of periodic behaviour and spectral structure, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 20, Pages: 713-730, ISSN: 0169-2070

Journal article

Olhede SC, McCoy EJ, Stephens DA, 2004, Large-sample properties of the periodogram estimator of seasonally persistent processes, Biometrika, Vol: 91, Pages: 613-628, ISSN: 0006-3444

Journal article

Hodges SD, Roberts G, Papaspiliopoulos O, Sentana E, Bingham NH, Cox DR, Nicolato E, Venardos E, Critchley F, Davis MHA, Tompkins R, Benth FE, Karlsen KH, Reikvam K, Brockwell PJ, Davis RA, Christensen BJ, Dellaportas P, McCoy EJ, Stephens DA, Diebold FX, Fruhwirth-Schnatter S, Genon-Catalot V, Laredo C, Grange CWJ, Griffin JE, Steel MFJ, Hobson D, Jensen JL, Jones MC, Lawrance AJ, Ledford AW, Leonenko NN, Levendorskii S, Mandelbrot BB, Meddahi N, Pitt MK, Priestley MB, Renault E, Rosinski J, Sato K, Taylor SJ, Tong H, Yang H, Veretennikov AY, Walker SG, Werker BJM, Wood Aet al., 2001, Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics - Discussion, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol: 63, Pages: 208-241, ISSN: 1369-7412

Journal article

McCoy EJ, 2001, Wavelet-based modelling of persistent periodicities, 3rd European Congress of Mathematics (3ecm), Publisher: BIRKHAUSER VERLAG AG, Pages: 601-608

Conference paper

Walden AT, Percival DB, McCoy EJ, 1998, Spectrum estimation by wavelet thresholding of multitaper estimators, IEEE Transactions on Signal Processing, Vol: 46, Pages: 3153-3165, ISSN: 1053-587X

Journal article

McCoy EJ, Walden AT, Percival DB, 1998, Multitaper spectral estimation of power law processes, IEEE Transactions on Signal Processing, Vol: 46, Pages: 655-668, ISSN: 1053-587X

Journal article

McCoy EJ, Walden AT, Percival DB, 1998, Multitaper spectral estimation of power law processes, IEEE Transactions on Signal Processing, Vol: 46, Pages: 655-668, ISSN: 1053-587X

Journal article

Walden AT, Percival DB, McCoy EJ, 1998, Spectrum estimation by wavelet thresholding of multitaper estimators, IEEE Transactions on Signal Processing, Vol: 46, Pages: 3153-3165, ISSN: 1053-587X

Journal article

McCoy EJ, Walden AT, 1996, Wavelet analysis and synthesis of stationary long-memory processes, Journal of Computational and Graphical Statistics, Vol: 5, Pages: 26-56, ISSN: 1061-8600

Journal article

WALDEN AT, MCCOY EJ, PERCIVAL DB, 1995, THE EFFECTIVE BANDWIDTH OF A MULTITAPER SPECTRAL ESTIMATOR, BIOMETRIKA, Vol: 82, Pages: 201-214, ISSN: 0006-3444

Journal article

WALDEN AT, MCCOY E, PERCIVAL DB, 1994, THE VARIANCE OF MULTITAPER SPECTRUM ESTIMATES FOR REAL GAUSSIAN-PROCESSES, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 42, Pages: 479-482, ISSN: 1053-587X

Journal article

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