Publications
314 results found
Hand DJ, 2011, The Best Writing on Mathematics 2011, The Best Writing on Mathematics 2011, Editors: Pitici, Publisher: Princeton University Press, Pages: 67-74, ISBN: 9781400839544
This book belongs on the shelf of anyone interested in where math has taken us--and where it is headed.
Hand DJ, 2011, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians by Ronald Christensen, Wesley Johnson, Adam Branscum, Timothy E. Hanson, International Statistical Review, Vol: 79, Pages: 285-286
Hand DJ, 2011, Introduction to Psychometric Theory by Tenko Raykov, George A. Marcoulides, International Statistical Review, Vol: 79, Pages: 298-299
Hand DJ, 2011, Linear Causal Modeling with Structural Equations by Stanley A. Mulaik, International Statistical Review, Vol: 79, Pages: 299-300
Hand DJ, 2011, Measurements and their Uncertainties: A Practical Guide to Modern Error Analysis by Ifan G. Hughes, Thomas P. A. Hase, International Statistical Review, Vol: 79, Pages: 280-280
Hand DJ, 2011, Logistic Regression Models by Joseph M. Hilbe, International Statistical Review, Vol: 79, Pages: 287-288
Hand DJ, 2011, Simultaneous Inference in Regression by Wei Liu, International Statistical Review, Vol: 79, Pages: 288-289
Hand DJ, Kalton G, 2011, Discussion of "Bayesian Models and Methods in Public Policy and Government Settings" by S. E. Fienberg, STATISTICAL SCIENCE, Vol: 26, Pages: 227-234, ISSN: 0883-4237
Henrion M, Mortlock DJ, Hand DJ, et al., 2011, A Bayesian approach to star-galaxy classification, Monthly Notices of the Royal Astronomical Society, Vol: 412, Pages: 2286-2302, ISSN: 0035-8711
Star–galaxy classification is one of the most fundamental data-processing tasks in survey astronomy and a critical starting point for the scientific exploitation of survey data. Star–galaxy classification for bright sources can be done with almost complete reliability, but for the numerous sources close to a survey’s detection limit each image encodes only limited morphological information about the source. In this regime, from which many of the new scientific discoveries are likely to come, it is vital to utilize all the available information about a source, both from multiple measurements and from prior knowledge about the star and galaxy populations. This also makes it clear that it is more useful and realistic to provide classification probabilities than decisive classifications. All these desiderata can be met by adopting a Bayesian approach to star–galaxy classification, and we develop a very general formalism for doing so. An immediate implication of applying Bayes’s theorem to this problem is that it is formally impossible to combine morphological measurements in different bands without using colour information as well; however, we develop several approximations that disregard colour information as much as possible. The resultant scheme is applied to data from the UKIRT Infrared Deep Sky Survey (UKIDSS) and tested by comparing the results to deep Sloan Digital Sky Survey (SDSS) Stripe 82 measurements of the same sources. The Bayesian classification probabilities obtained from the UKIDSS data agree well with the deep SDSS classifications both overall (a mismatch rate of 0.022 compared to 0.044 for the UKIDSS pipeline classifier) and close to the UKIDSS detection limit (a mismatch rate of 0.068 compared to 0.075 for the UKIDSS pipeline classifier). The Bayesian formalism developed here can be applied to improve the reliability of any star–galaxy classification schemes based on the measured values of morphology statistics alo
Hand DJ, 2011, Statistical Inference: An Integrated Bayesian/Likelihood Approach by Murray Aitkin, International Statistical Review, Vol: 79, Pages: 121-121
Hand DJ, 2011, Charming Proofs: A Journey into Elegant Mathematics by Claudi Alsina, Roger B. Nelsen, International Statistical Review, Vol: 79, Pages: 122-122
Hand DJ, 2011, Diagnostic Measurement: Theory, Methods, and Applications by André A. Rupp, Jonathan Templin, Robert A. Henson, International Statistical Review, Vol: 79, Pages: 135-136
Brentnall AR, Crowder MJ, Hand DJ, 2011, Approximate repeated-measures shrinkage, COMPUTATIONAL STATISTICS & DATA ANALYSIS, Vol: 55, Pages: 1150-1159, ISSN: 0167-9473
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- Citations: 1
Pavlidis NG, Tasoulis DK, Adams NM, et al., 2011, λ-Perceptron: An adaptive classifier for data streams, PATTERN RECOGNITION, Vol: 44, Pages: 78-96, ISSN: 0031-3203
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- Citations: 30
Krzanowski WJ, Hand DJ, 2011, Testing the difference between two Kolmogorov-Smirnov values in the context of receiver operating characteristic curves, JOURNAL OF APPLIED STATISTICS, Vol: 38, Pages: 437-450, ISSN: 0266-4763
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- Citations: 9
Hand DJ, 2010, Estimation and tests in distribution mixtures and change‐points models by Odile Pons, International Statistical Review, Vol: 78, Pages: 475-476
Hand DJ, 2010, Modeling and Analysis of Stochastic Systems, Second Edition by Vidyadhar G. Kulkarni, International Statistical Review, Vol: 78, Pages: 467-467
Hand DJ, 2010, Theory of Stochastic Processes: With Applications to Financial Mathematics and Risk Theory by Dmytro Gusak, Alexander Kukush, Alexey Kulik, Yuliya Mishura, Andrey Pilipenko, International Statistical Review, Vol: 78, Pages: 461-461
Hand DJ, 2010, Econophysics and Companies: Statistical Life and Death in Complex Business Networks by Hideaki Aoyama, Yoshi Fujiwara, Yuichi Ikeda, Hiroshi Iyetomi, Wataru Souma, International Statistical Review, Vol: 78, Pages: 445-445
Hand DJ, 2010, The Oxford Handbook of Applied Bayesian Analysis edited by Anthony O’Hagan, Mike West, International Statistical Review, Vol: 78, Pages: 474-475
Brentnall AR, Crowder MJ, Hand DJ, 2010, Predictive-sequential forecasting system development for cash machine stocking, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 26, Pages: 764-776, ISSN: 0169-2070
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- Citations: 6
Hand DJ, Zhou F, 2010, Evaluating models for classifying customers in retail banking collections, JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, Vol: 61, Pages: 1540-1547, ISSN: 0160-5682
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- Citations: 7
Hand DJ, 2010, Introduction to Social Statistics: The Logic of Statistical Reasoning by Thomas Dietz, Linda Kalof, International Statistical Review, Vol: 78, Pages: 326-327
Hand DJ, 2010, Machine Learning: An Algorithmic Perspective by Stephen Marsland, International Statistical Review, Vol: 78, Pages: 325-325
Brentnall AR, Crowder MJ, Hand DJ, 2010, Predicting the amount individuals withdraw at cash machines using a random effects multinomial model, STATISTICAL MODELLING, Vol: 10, Pages: 197-214, ISSN: 1471-082X
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- Citations: 7
Hand DJ, 2010, Evaluating diagnostic tests: The area under the ROC curve and the balance of errors, STATISTICS IN MEDICINE, Vol: 29, Pages: 1502-1510, ISSN: 0277-6715
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- Citations: 84
Heard NA, Weston DJ, Platanioti K, et al., 2010, BAYESIAN ANOMALY DETECTION METHODS FOR SOCIAL NETWORKS, ANNALS OF APPLIED STATISTICS, Vol: 4, Pages: 645-662, ISSN: 1932-6157
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- Citations: 96
Tasoulis DK, Adams NM, Hand DJ, 2010, Selective fusion of out-of-sequence measurements, INFORMATION FUSION, Vol: 11, Pages: 183-191, ISSN: 1566-2535
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- Citations: 14
Hand DJ, 2010, Text Mining: Classification, Clustering, and Applications edited by Ashok Srivastava, Mehran Sahami, International Statistical Review, Vol: 78, Pages: 134-135
Hand DJ, 2010, Statistical Analysis of Network Data: Methods and Models by Eric D. Kolaczyk, International Statistical Review, Vol: 78, Pages: 135-135
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