Imperial College London
Portrait Picture

Professor David van Dyk

Faculty of Natural Sciences, Department of Mathematics

Chair in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8574d.van-dyk Website

 
 
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Assistant

 

Mr David Whittaker +44 (0)20 7594 8481

 
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Location

 

539Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

128 results found

Jefferys WH, von Hippel T, Scott J, Stein N, Winget DE, DeGennaro S, Dam A, Jeffery E, van Dyk DAet al., 2007, Inverting color-magnitude diagrams to access precise star cluster parameters: A Bayesian approach, 4th Statistical Challenges in Modern Astronomy Conference, Publisher: ASTRONOMICAL SOC PACIFIC, Pages: 435-+, ISSN: 1050-3390

Conference paper

Park T, van Dyk DA, Siemiginowska A, 2007, Fitting narrow spectral lines in high-energy astrophysics using incompatible Gibbs samplers, 4th Statistical Challenges in Modern Astronomy Conference, Publisher: ASTRONOMICAL SOC PACIFIC, Pages: 437-+, ISSN: 1050-3390

Conference paper

Park T, Kashyap VL, Siemiginowska A, Van Dyk DA, Zezas A, Heinke C, Wargelin BJet al., 2006, Bayesian estimation of hardness ratios: Modeling and computations, ASTROPHYSICAL JOURNAL, Vol: 652, Pages: 610-628, ISSN: 0004-637X

Journal article

van Dyk DA, Connors A, Esch DN, Freeman P, Kang H, Karovska M, Kashyap V, Siemiginowska A, Zezas Aet al., 2006, Deconvolution in High-Energy Astrophysics: Science, Instrumentation, and Methods, BAYESIAN ANALYSIS, Vol: 1, Pages: 189-235, ISSN: 1931-6690

Journal article

van Dyk DA, Kang H, 2006, Rejoinder, BAYESIAN ANALYSIS, Vol: 1, Pages: 241-248, ISSN: 1931-6690

Journal article

Imai K, van Dyk DA, 2005, MNP: R package for fitting the multinomial probit model, JOURNAL OF STATISTICAL SOFTWARE, Vol: 14, ISSN: 1548-7660

Journal article

Imai K, van Dyk DA, 2005, A Bayesian analysis of the multinomial probit model using marginal data augmentation, JOURNAL OF ECONOMETRICS, Vol: 124, Pages: 311-334, ISSN: 0304-4076

Journal article

Kang H, van Dyk DA, Kashyap VL, Connors Aet al., 2005, Incorporating atomic data errors in stellar DEM reconstruction, Conference on X-Ray Diagnostics of Astrophysical Plasmas - Theory, Experiment, and Observation, Publisher: AMER INST PHYSICS, Pages: 373-375, ISSN: 0094-243X

Conference paper

Imai K, van Dyk DA, 2004, Causal inference with general treatment regimes: Generalizing the propensity score, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol: 99, Pages: 854-866, ISSN: 0162-1459

Journal article

Esch DN, Connors A, Karovska M, van Dyk DAet al., 2004, An image restoration technique with error estimates, ASTROPHYSICAL JOURNAL, Vol: 610, Pages: 1213-1227, ISSN: 0004-637X

Journal article

van Dyk DA, Kang H, 2004, Highly structured models for spectral analysis in high-energy astrophysics, STATISTICAL SCIENCE, Vol: 19, Pages: 275-293, ISSN: 0883-4237

Journal article

van Dyk DA, 2004, Highly-Structured Statistical Models in High Energy Astrophysics, Menlo Park, CA, Publisher: SLAC Technical Publications, Pages: 114-121

Conference paper

van Dyk DA, Park T, 2004, Efficient EM-Type Algorithms for Fitting Spectral Lines in High-Energy Astrophysics, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: Contributions by Donald Rubin’s Statistical Family \rm (Editors: A. Gelman and X.-L. Meng), New York, Publisher: Wiley & Sons, Pages: 285-296

Book chapter

Javaras KN, Van Dyk DA, 2003, Multiple imputation for incomplete data with semicontinuous variables, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol: 98, Pages: 703-715, ISSN: 0162-1459

Journal article

Van Dyk DA, Tang RX, 2003, The one-step-late PXEM algorithm, STATISTICS AND COMPUTING, Vol: 13, Pages: 137-152, ISSN: 0960-3174

Journal article

Sourlas N, van Dyk DA, Kashyap V, Drake J, Pease Det al., 2003, Bayesian spectral analysis of "MAD" stars, 3rd Statistical Challenges in Modern Astronomy Conference (SCMA III), Publisher: SPRINGER, Pages: 489-490

Conference paper

Hans C, van Dyk DA, 2003, Accounting for absorption lines in high energy spectra, 3rd Statistical Challenges in Modern Astronomy Conference (SCMA III), Publisher: SPRINGER, Pages: 429-430

Conference paper

van Dyk DA, 2003, Hierarchical models, data augmentation, and Markov chain Monte Carlo, Editors: Feigelson, Babu, Publisher: SPRINGER, Pages: 41-56, ISBN: 0-387-95546-1

Book chapter

Kang H, van Dyk DA, Yu Y, Siemiginowska A, Connors A, Kashyap Vet al., 2003, New MCMC Methods to Address Pile-up in the Chandra X-ray Observatory, New York, Publisher: Springer–Verlag, Pages: 449-450

Conference paper

Protassov R, van Dyk DA, Connors A, Kashyap VL, Siemiginowska Aet al., 2002, Statistics, handle with care: Detecting multiple model components with the likelihood ratio test, ASTROPHYSICAL JOURNAL, Vol: 571, Pages: 545-559, ISSN: 0004-637X

Journal article

Zaslavsky AM, Van Dyk DA, 2002, !Chen, Z., and !Kuo, L. (2001), "A note on the estimation of the multinomial logit model with random effects," <i>The American Statistician</i>, 55,89-95:: Comment by Zaslavsky and !Van Dyk, AMERICAN STATISTICIAN, Vol: 56, Pages: 80-81, ISSN: 0003-1305

Journal article

van Dyk DA, 2002, Discussion of Setting Confidence Intervals for Bounded Parameters by M. Mandelkern, Statistical Science, Vol: 17, Pages: 164-168

Journal article

van Dyk DA, Hans CM, 2002, Accounting for Absorption Lines in Images Obtained with the Chandra X-ray Observatory, Spatial Cluster Modelling \rm (Editors: D. Denison and A. Lawson), London, Publisher: CRC Press, Pages: 175-198

Book chapter

van Dyk DA, Meng XL, 2001, The art of data augmentation, JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol: 10, Pages: 1-50, ISSN: 1061-8600

Journal article

van Dyk DA, Meng XL, 2001, Untitled - Rejoinder, JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol: 10, Pages: 98-111, ISSN: 1061-8600

Journal article

Van Dyk DA, Connors A, Kashyap VL, Siemiginowska Aet al., 2001, Analysis of energy spectra with low photon counts via Bayesian posterior simulation, ASTROPHYSICAL JOURNAL, Vol: 548, Pages: 224-243, ISSN: 0004-637X

Journal article

Foulley JL, van Dyk DA, 2000, The PX-EM algorithm for fast stable fitting of Henderson's mixed model, GENETICS SELECTION EVOLUTION, Vol: 32, Pages: 143-163, ISSN: 0999-193X

Journal article

van Dyk DA, 2000, Fitting mixed-effects models using efficient EM-type algorithms, JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol: 9, Pages: 78-98, ISSN: 1061-8600

Journal article

Van Dyk DA, 2000, Fitting Mixed-Effects Models Using Efficient EM-Type Algorithms, Journal of Computational and Graphical Statistics, Vol: 9, Pages: 78-98, ISSN: 1061-8600

In recent years numerous advances in EM methodology have led to algorithms which can be very efficient when compared with both their EM predecessors and other numerical methods (e.g., algorithms based on Newton—Raphson). This article combines several of these new methods to develop a set of mode-finding algorithms for the popular mixed-effects model which are both fast and more reliable than such standard algorithms as proc mixed in SAS. We present efficient algorithms for maximum likelihood (ML), restricted maximum likelihood (REML), and computing posterior modes with conjugate proper and improper priors. These algorithms are not only useful in their own right, but also illustrate how parameter expansion, conditional data augmentation, and the ECME algorithm can be used in conjunction to form efficient algorithms. In particular, we illustrate a difficulty in using the typically very efficient PXEM (parameter-expanded EM) for posterior calculations, but show how algorithms based on conditional data augmentation can be used. Finally, we present a result that extends Hobert and Casella’s result on the propriety of the posterior for the mixed-effects model under an improper prior, an important concern in Bayesian analysis involving these models that when not properly understood has lead to difficulties in several applications. © 2000 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Journal article

Foulley JL, Van Dyk DA, 2000, The PX-EM algorithm for fast stable fitting of Henderson's mixed model, Genetics Selection Evolution, Vol: 32, Pages: 143-163, ISSN: 0999-193X

This paper presents procedures for implementing the PX-EM algorithm of Liu, Rubin and Wu to compute REML estimates of variance covariance components in Henderson's linear mixed models. The class of models considered encompasses several correlated random factors having the same vector length e.g., as in random regression models for longitudinal data analysis and in sire-maternal grandsire models for genetic evaluation. Numerical examples are presented to illustrate the procedures. Much better results in terms of convergence characteristics (number of iterations and time required for convergence) are obtained for PX-EM relative to the basic EM algorithm in the random regression.

Journal article

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