45 results found
Noven RC, Veraart AED, Gandy A, 2018, A latent trawl process model for extreme values, JOURNAL OF ENERGY MARKETS, Vol: 11, Pages: 1-24, ISSN: 1756-3607
Gandy A, Veraart LAM, 2018, Adjustable network reconstruction with applications to CDS exposures, Journal of Multivariate Analysis, ISSN: 0047-259X
© 2018 Elsevier Inc. This paper is concerned with reconstructing weighted directed networks from the total in- and out-weight of each node. This problem arises for example in the analysis of systemic risk of partially observed financial networks. Typically a wide range of networks is consistent with this partial information. We develop an empirical Bayesian methodology that can be adjusted such that the resulting networks are consistent with the observations and satisfy certain desired global topological properties such as a given mean density, extending the approach by Gandy and Veraart (2017). Furthermore we propose a new fitness-based model within this framework. We provide a case study based on a data set consisting of 89 fully observed financial networks of credit default swap exposures. We reconstruct those networks based on only partial information using the newly proposed as well as existing methods. To assess the quality of the reconstruction, we use a wide range of criteria, including measures on how well the degree distribution can be captured and higher order measures of systemic risk. We find that the empirical Bayesian approach performs best.
Gandy A, Veraart LAM, 2017, A Bayesian Methodology for Systemic Risk Assessment in Financial Networks, MANAGEMENT SCIENCE, Vol: 63, Pages: 4428-4446, ISSN: 0025-1909
Gandy A, Kvaloy JT, 2017, spcadjust: An R Package for Adjusting for Estimation Error in Control Charts, R JOURNAL, Vol: 9, Pages: 458-476, ISSN: 2073-4859
Lau FDH, Gandy A, 2016, Enhancing football league tables, Significance, Vol: 13, Pages: 8-9, ISSN: 1740-9705
© 2016 The Royal Statistical Society League tables are commonly used to represent the current state of a competition, in football and other sports. But they do not tell the full story. F. Din-Houn Lau and Axel Gandy suggest a few improvements.
Gandy A, Lau F, 2016, The chopthin algorithm for resampling, IEEE Transactions on Signal Processing, Vol: 64, Pages: 4273-4281, ISSN: 1941-0476
Resampling is a standard step in particle filters andmore generally sequential Monte Carlo methods. Wepresent an algorithm, called chopthin, for resamplingweighted particles. In contrast to standard resamplingmethods the algorithm does not produce a set ofequally weighted particles; instead it merely enforcesan upper bound on the ratio between the weights.Simulation studies show that the chopthin algorithmconsistently outperforms standard resampling methods.The algorithms chops up particles with largeweight and thins out particles with low weight, henceits name. It implicitly guarantees a lower bound onthe effective sample size. The algorithm can be implementedefficiently, making it practically useful. Weshow that the expected computational effort is linearin the number of particles. Implementations for C++,R (on CRAN), Python and Matlab are available.
Gandy A, Hahn G, 2016, QuickMMCTest -- quick multiple Monte Carlo testing, Statistics and Computing, Vol: 27, Pages: 823-832, ISSN: 1573-1375
Multiple hypothesis testing is widely used to evaluate scientific studiesinvolving statistical tests. However, for many of these tests, p-values are notavailable and are thus often approximated using Monte Carlo tests such aspermutation tests or bootstrap tests. This article presents a simple algorithmbased on Thompson Sampling to test multiple hypotheses. It works with arbitrarymultiple testing procedures, in particular with step-up and step-downprocedures. Its main feature is to sequentially allocate Monte Carlo effort,generating more Monte Carlo samples for tests whose decisions are so far lesscertain. A simulation study demonstrates that for a low computational effort,the new approach yields a higher power and a higher degree of reproducibilityof its results than previously suggested methods.
Gandy A, Hahn G, 2016, A framework for Monte Carlo based multiple testing, Scandinavian Journal of Statistics, Vol: 43, Pages: 1046-1063, ISSN: 1467-9469
We are concerned with multiple testing in the setting where p-values areunknown and can only be approximated using Monte Carlo simulation. Thisscenario occurs widely in practice. We are interested in obtaining the samerejections and non-rejections as the ones obtained if the p-values for allhypotheses had been available. The present article introduces a framework forthis scenario by providing a generic algorithm for a general multiple testingprocedure. We establish conditions which guarantee that the rejections andnon-rejections obtained through Monte Carlo simulations are identical to theones obtained with the p-values. Our framework is applicable to a general classof step-up and step-down procedures which includes many established multipletesting corrections such as the ones of Bonferroni, Holm, Sidak, Hochberg orBenjamini-Hochberg. Moreover, we show how to use our framework to improvealgorithms available in the literature in such a way as to yield theoreticalguarantees on their results. These modifications can easily be implemented inpractice and lead to a particular way of reporting multiple testing results asthree sets together with an error bound on their correctness, demonstratedexemplarily using a real biological dataset.
Noven RC, Veraart AED, Gandy A, 2015, A Levy-driven rainfall model with applications to futures pricing, ASTA-ADVANCES IN STATISTICAL ANALYSIS, Vol: 99, Pages: 403-432, ISSN: 1863-8171
Lau FD-H, Gandy A, 2014, RMCMC: A system for updating Bayesian models, COMPUTATIONAL STATISTICS & DATA ANALYSIS, Vol: 80, Pages: 99-110, ISSN: 0167-9473
Gandy A, Hahn G, 2014, MMCTest-A Safe Algorithm for Implementing Multiple Monte Carlo Tests, SCANDINAVIAN JOURNAL OF STATISTICS, Vol: 41, Pages: 1083-1101, ISSN: 0303-6898
Phinikettos I, Gandy A, 2014, An omnibus CUSUM chart for monitoring time to event data, LIFETIME DATA ANALYSIS, Vol: 20, Pages: 481-494, ISSN: 1380-7870
Gandy A, Kvaloy JT, 2013, Guaranteed Conditional Performance of Control Charts via Bootstrap Methods, SCANDINAVIAN JOURNAL OF STATISTICS, Vol: 40, Pages: 647-668, ISSN: 0303-6898
Lau FD-H, Gandy A, 2013, Optimality of Non-Restarting CUSUM Charts, SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS, Vol: 32, Pages: 458-468, ISSN: 0747-4946
Gandy A, Veraart LAM, 2013, THE EFFECT OF ESTIMATION IN HIGH-DIMENSIONAL PORTFOLIOS, MATHEMATICAL FINANCE, Vol: 23, Pages: 531-559, ISSN: 0960-1627
Gandy A, Lau FD-H, 2013, Non-restarting cumulative sum charts and control of the false discovery rate, BIOMETRIKA, Vol: 100, Pages: 261-268, ISSN: 0006-3444
Gandy A, Rubin-Delanchy P, 2013, AN ALGORITHM TO COMPUTE THE POWER OF MONTE CARLO TESTS WITH GUARANTEED PRECISION, ANNALS OF STATISTICS, Vol: 41, Pages: 125-142, ISSN: 0090-5364
Henrion M, Mortlock DJ, Hand DJ, et al., 2013, Classification and Anomaly Detection for Astronomical Survey Data, Springer Series in Astrostatistics, Pages: 149-184, ISBN: 9781461435075
© Springer Science+Business Media New York 2013. We present two statistical techniques for astronomical problems: a star-galaxy separator for the UKIRT Infrared Deep Sky Survey (UKIDSS) and a novel anomaly detection method for cross-matched astronomical datasets. The star-galaxy separator is a statistical classification method which outputs class membership probabilities rather than class labels and allows the use of prior knowledge about the source populations. Deep Sloan Digital Sky Survey (SDSS) data from the multiply imaged Stripe 82 region are used to check the results from our classifier, which compares favourably with the UKIDSS pipeline classification algorithm. The anomaly detection method addresses the problem posed by objects having different sets of recorded variables in cross-matched datasets. This prevents the use of methods unable to handle missing values and makes direct comparison between objects difficult. For each source, our method computes anomaly scores in subspaces of the observed feature space and combines them to an overall anomaly score. The proposed technique is very general and can easily be used in applications other than astronomy. The properties and performance of our method are investigated using both real and simulated datasets.
Lee MLT, Gail M, Pfeiffer R, et al., 2013, Preface, Pages: V-VI, ISSN: 0930-0325
Gandy A, Trotta R, 2013, Special Issue on Astrostatistics, STATISTICAL ANALYSIS AND DATA MINING, Vol: 6, Pages: 1-+, ISSN: 1932-1864
Henrion M, Hand DJ, Gandy A, et al., 2013, CASOS: a Subspace Method for Anomaly Detection in High Dimensional Astronomical Databases, STATISTICAL ANALYSIS AND DATA MINING, Vol: 6, Pages: 53-72, ISSN: 1932-1864
Gandy A, 2012, Performance monitoring of credit portfolios using survival analysis, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 28, Pages: 139-144, ISSN: 0169-2070
Ashby D, Bird SM, Hunt I, et al., 2012, Discussion on the paper by Spiegelhalter, Sherlaw-Johnson, Bardsley, Blunt, Wood and Grigg, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 175, Pages: 25-47, ISSN: 0964-1998
Phinikettos I, Gandy A, 2011, Fast computation of high-dimensional multivariate normal probabilities, COMPUTATIONAL STATISTICS & DATA ANALYSIS, Vol: 55, Pages: 1521-1529, ISSN: 0167-9473
Gandy A, 2010, R-package simctest
Jen MH, Johnston R, Jones K, et al., 2010, INTERNATIONAL VARIATIONS IN LIFE EXPECTANCY: A SPATIO-TEMPORAL ANALYSIS, TIJDSCHRIFT VOOR ECONOMISCHE EN SOCIALE GEOGRAFIE, Vol: 101, Pages: 73-90, ISSN: 0040-747X
Gandy A, 2009, Sequential Implementation of Monte Carlo Tests With Uniformly Bounded Resampling Risk, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol: 104, Pages: 1504-1511, ISSN: 0162-1459
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