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Prof Charles C Taylor

Professor of Statistics
Statistics

Contact details

Room: 8.12
Tel: +44 (0)113 3435168
Email: C.C.Taylor @ leeds.ac.uk

Keywords

Statistical Learning
Spatial statistics
Circular Data
Statistical Smoothing
Statistical Bioinformatics
Data Mining
Multivariate analysis
Kernel Methods

Research interests

Statistical Learning and Data Mining

Density estimation is a key area within nonparametric statistics which is used in exploratory data analysis and the formulation of hypotheses. A critical choice for user interpretation is how much smoothing to use. In this context I have made innovative utilization of three automatic procedures for three methods of estimation: Akaike's Information Criterion to determine the class width of the histogram, cross-validation methods to choose the number of terms in an orthogonal series estimator, and the bootstrap to choose the window width in the kernel estimator. The latter paper, in particular, is highly regarded since analytical calculations can be used to calculate the ootstrap expectations thus avoiding the usual recourse to simulation. My current interests lie in: the application of kernel density estimation to discrimination problems; the effect of enhancing classifiers through boosting and bagging, and adaptation of kernel methods to circular data.

Data Mining (which provides tools for turning large databases into knowledge) is widely seen as increasingly important topic, particularly with the ability to automatically collect, and store large amounts of data. This field is being tackled by both computer scientists and, to a lesser extent, by statisticians. I have played a key role in cross-fertilization of methods, performance indicators, and formulation of further key issues to resolve.

Spatial statistics and image analysis

Over the past twenty years, statistical methods have gained an increasing role in image analysis and the analysis of spatial data. I have made a significant contribution in

- Image summaries, which are important for identifying suitable models and for classification on the basis of the extracted features. These features can be measurable physical quantities of objects in an image (for example the ratio height/width can be useful in identifying the number 1 in the automatic recognition of vehicle number plates) or they can be parameters in a statistical model (for example the variance).

With the advent of increasing amounts of resolution, it has become possible to estimate the fractal dimension of objects (or their outline) in an image. The fractal dimension is another example of an image summary which can be used to describe objects, and to classify future observations. In this context, I have made a contribution to methods of its estimation (Taylor & Taylor, 1991).

Frequently, single number summaries are inadequate, and (univariate) functions are used to describe features of the image. For example, in a point pattern the density of the distance between two randomly chosen pairs of points can be used to describe clustering or inhibitions - for example, for species which are in competition. For point patterns which are highly regular, the usual sequential methods of simulation are prohibitively slow and I (together with Ian Dryden) have developed several innovative methods for a new model. These methods include a Procrustes shape analysis, and an analysis of interpoint distances based on size. In addition, I have developed methods which have been successfully used for discrimination on the basis of morphological summaries.

Useful links

My personal home page
The Statlog book is here

Current postgraduate students

Samira Abushilah (2014)
Wafa Al Mohri (2013)
Amirah Alharthi (2016)
Aziz Aljuaid (2014)
Fatimah Almulhim (2014)
Khaled Alqahtani (2013)
Mohammed Alshahrani (2014)

Publications

Shuweihdi F, Taylor CC, Gusnanto A Classification of form under heterogeneity and non-isotropic errors Journal of Applied Statistics, 44, 1495-1508, 2017
DOI:10.1080/02664763.2016.1214246
View abstract

Di Marzio M, Fensore S, Panzera A, Taylor CC A note on nonparametric estimation of circular conditional densities Journal of Statistical Computation and Simulation, 86, 2573-2582, 2016
DOI:10.1080/00949655.2016.1146279
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Di Marzio M, Panzera A, Taylor CC Nonparametric circular quantile regression Journal of Statistical Planning and Inference, 170, 1-14, 2016
DOI:10.1016/j.jspi.2015.08.004
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Di Marzio M; Fensore S; Panzera A; Taylor CC Practical performance of local likelihood for circular density estimationJOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2560-2572 2016
DOI:10.1080/00949655.2016.1149588

Milan Z, Taylor C, Armstrong D, Davies P, Roberts S, Rupnik B, Suddle A Does preoperative beta-blocker use influence intraoperative hemodynamic profile and post-operative course of liver transplantation? Transplantation Proceedings, 48, 111-115, 2016
DOI:10.1016/j.transproceed.2015.12.027
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Polsen O, Taylor CC Parametric circular-circular regression and diagnostic analysis In Geometry Driven Statistics, 115-128, 2015
DOI:10.1002/9781118866641.ch5
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Gusnanto A, Taylor CC, Nafisah I, Wood HM, Rabbitts P, Berri S Estimating optimal window size for analysis of low-coverage next-generation sequence data. Bioinformatics, 30, 1823-1829, 2014
DOI:10.1093/bioinformatics/btu123
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Di Marzio M, Panzera A, Taylor CC Nonparametric Regression for Spherical Data JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 109, 748-763, 2014
DOI:10.1080/01621459.2013.866567

Taylor CC, Di Marzio M, Panzera A Nonparametric regression for circular responses Scandinavian Journal of Statistics: theory and applications, 40, 238-255, 2013

Mardia KV, Petty EM, Taylor CC Matching markers and unlabelled configurations in protein gels Annals of Applied Statistics, 6, 853-869, 2013

Di Marzio M, Panzera A, Taylor CC Smooth estimation of circular cumulative distribution functions and quantiles Journal of Nonparametric Statistics, 24, 935-949, 2012
DOI:10.1080/10485252.2012.721517
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Mardia KV, Kent JT, Zhang Z, Taylor CC, Hamelryck T Mixtures of concentrated multivariate sine distributions with applications to bioinformatics Journal of Applied Statistics, 39, 2475-2492, 2012
DOI:10.1080/02664763.2012.719221
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Di Marzio M, Panzera A, Taylor CC Non-parametric smoothing and prediction for nonlinear circular time series Journal of Time Series Analysis, 33, 620-630, 2012
DOI:10.1111/j.1467-9892.2012.00794.x
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Taylor CC, Mardia KV, Di Marzio M, Panzera A Validating protein structure using kernel density estimates Journal of Applied Statistics, 39, 2379-2388, 2012
DOI:10.1080/02664763.2012.710898
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Di Marzio M, Taylor CC Boosting kernel estimators In Ensemble Machine Learning: Methods and Applications, 87-115, 2012
DOI:10.1007/9781441993267_3
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Di Marzio M, Taylor CC, Ma Y Boosting Kernel Estimators ENSEMBLE MACHINE LEARNING: METHODS AND APPLICATIONS, 87-115, 2012
DOI:10.1007/978-1-4419-9326-7_3

Di Marzio M, Panzera A, Taylor CC A note on density estimation for circular data In Advanced Statistical Methods for the Analysis of Large Data-Sets, 297-304, 2012
DOI:10.1007/978-3-642-21037-2_27
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Milan Z, Taylor C, Duncan B, Kedilaya H, Sylvester D Statistical modeling of hemodynamic changes during orthotopic liver transplantation: Predictive value for outcome and effect of marginal donors Transplantation Proceedings, 43, 1711-1715, 2011
DOI:10.1016/j.transproceed.2011.01.165
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Di Marzio M, Panzera A, Taylor CC Kernel density estimation on the torus Journal of Statistical Planning&Inference, 141, 2156-2173, 2011
DOI:10.1016/j.jspi.2011.01.002
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Iranpanah N, Mohammadzadeh M, Taylor CC A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics Computational Statistics and Data Analysis, 55, 578-587, 2011
DOI:10.1016/j.csda.2010.05.031
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Mardia KV, Kent JT, Hughes G, Taylor CC Maximum likelihood estimation using composite likelihoods for closed exponential families BIOMETRIKA, 96, 975-982, 2009
DOI:10.1093/biomet/asp056

Jankovic Z, Taylor C, Duncan B, Kedilaya H, Sylvester D, Narayanan R Haemodynamic changes during liver transplantation: Predictive value for outcome and effect of marginal donors In Liver Transplantation: Resection. Therapy and Post-Operative Complications, 189-208, 2009
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Di Marzio M, Panzera A, Taylor CC Local polynomial regression for circular predictors Statistics&Probability Letters, 79, 2066-2075, 2009
DOI:10.1016/j.spl.2009.06.014
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Di Marzio M, Taylor CC Using small bias nonparametric density estimators for confidence interval estimation Journal of Nonparametric Statistics, 21, 229-240, 2009
DOI:10.1080/10485250802562607
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Ivings L, Towns KV, Matin MA, Taylor C, Ponchel F, Grainger RJ, Ramesar RS, Mackey DA, Inglehearn CF Evaluation of splicing efficiency in lymphoblastoid cell lines from patients with splicing-factor retinitis pigmentosa Molecular Vision, 14, 2357-2366, 2008
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Bickel P, Buhlmann P, Yao QW, Samworth R, Hall P, Titterington DM, Xue JH, Anagnostopoulos C, Tasoullis DK, Zhang WY, Xia YC, Johnstone IM, Richardson S, Bottolo L, Kent JT, Adragni K, Cook RD, Gather U, Guddat C, Greenshtein E, James GM, Radchenko P, Leng CL, Wang HS, Levina E, Zhu J, Li RZ, Liu YF, Longford NT, Luo WQ, Baxter PD, Taylor CC, Marron JS, Morris JS, Robert CP, Yu KM, Zhang CH, Zhang HH, Zhou HH, Lin XH, Zou H Sure independence screening for ultrahigh dimensional feature space Discussion J ROY STAT SOC B, 70, 883-911, 2008

Di Marzio M, Taylor CC On boosting kernel regression Journal of Statistical Planning and Inference, 138, 2483-2498, 2008
DOI:10.1016/j.jspi.2007.10.005
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Boomsma W, Mardia KV, Taylor CC, Ferkinghoff-Borg J, Krogh A, Hamelryck T A generative, probabilistic model of local protein structure P NATL ACAD SCI USA, 105, 8932-8937, 2008
DOI:10.1073/pnas.0801715105

Taylor CC Automatic bandwidth selection for circular density estimation COMPUT STAT DATA AN, 52, 3493-3500, 2008
DOI:10.1016/j.csda.2007.11.003

Mardia KV, Hughes G, Taylor CC, Singh H A multivariate von Mises distribution with applications to bioinformatics CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 36, 99-109, 2008

Davies JR, Jackson RM, Mardia KV, Taylor CC The Poisson Index: a new probabilistic model for protein ligand binding site similarity. Bioinformatics, 23, 3001-3008, 2007
DOI:10.1093/bioinformatics/btm470
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Mardia KV; Petty EM; Taylor CC Using EM to Superimpose 2D Images of Polyacrylamide GelsSystems Biology&Statistical Bioinformatics, pp.121 2007

Mardia KV, Taylor CC, Subramaniam GK Protein bioinformatics and mixtures of bivariate von Mises distributions for angular data BIOMETRICS, 63, 505-512, 2007
DOI:10.1111/j.1541-0420.2006.00682.x

Langdon MJ, Taylor CC, West RM Classification of type I-censored bivariate data COMPUT STAT DATA AN, 51, 4562-4576, 2007
DOI:10.1016/j.csda.2006.07.043

Kent JT, Mardia KV, Taylor CC Bioinformatics and the Problem of Matching Unlabelled Configurations Royal Statistical Society. Journal. Series B: Methodological, 2007

Xu C, Dowd PA, Mardia KV, Fowell RJ, Taylor CC Simulating correlated marked point processes. Journal of Applied Statistics, 2007

Davies JR, Jackson RM, Taylor CC, Mardia KV A Statistical Model for Measuring Binding Site Similarity Bioinformatics, 2007

Arato NM, Dryden IL, Taylor CC Hierarchical Bayesian modelling of spatial age-dependent mortality COMPUT STAT DATA AN, 51, 1347-1363, 2006
DOI:10.1016/j.csda.2006.02.007

Dryden IL, Farnoosh R, Taylor CC Image segmentation using Voronoi Polygons and MCMC, with application to muscle fibre images. Journal of Applied Statistics, 33, 609-622, 2006
DOI:10.1080/02664760600679825
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Petty EM, Mardia KV, Taylor CC, Chaudhry Q Matching Pesticides to Proteins to Predict Toxicity In Interdisciplinary Statistics and Bioinformatics, 150-153, 2006

Hughes G, Mardia KV, Taylor CC Modeling Protein Folds with a Trivariate von Mises Distribution In Interdisciplinary Statistics and Bioinformatics, 120-123, 2006

Davies JR, Jackson RM, Mardia KV, Taylor CC Statistical Comparison of Protein Binding Sites In Interdisciplinary Statistics and Bioinformatics, 114-115, 2006

Boomsma W, Kent JT, Mardia KV, Taylor CC, Hamelryck T Graphical Models and Directional Statistics Capture Protein Structure In Interdisciplinary Statistics and Bioinformatics, 91-94, 2006

Kerr KG, Beggs CB, Dean SG, Thornton J, Donnelly J, Todd NJ, Sleigh PA, Qureshi A, Taylor CC Air ionisation and colonisation/infection with methicillin-resistant Staphylococcus aureus and Acinetobacter species in an intensive care unit Intensive Care Medicine, 32, 315-317, 2006
DOI:10.1007/s00134-005-0002-8
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Di Marzio M, Taylor CC Boosted Regression Estimates of Spatial Data: Pointwise Inference Journal of Mathematics and Statistics, 1, 247-257, 2006

Hughes G, Mardia KV, Taylor CC Circular time series with application to protein conformations In Quantitative Biology, Shape Analysis, and Wavelets, 146-149, 2005

Xu C, Dowd PA, Mardia KV, Fowell RJ, Taylor CC Inference for marked point processes In Quantitative Biology, Shape Analysis and Wavelets, 172-174, 2005

Di Marzio M, Taylor CC Kernel density classification and boosting: an L-2 analysis STAT COMPUT, 15, 113-123, 2005
DOI:10.1007/s11222-005-6203-8

Abdulhussain Boosted Regression Estimates of Spatial Data: Pointwise Inference Journal of Mathematics and Statistics, 1, 257-266, 2005
DOI:10.3844/jmssp.2005.257.266

Dryden IL, Markus L, Taylor CC, Kovacs J Non-stationary spatio-temporal analysis of karst water levels Royal Statistical Society. Journal. Series C: Applied Statistics, 54, 673-690, 2005

Di Marzio M, Taylor CC On boosting kernel density methods for multivariate data: density estimation and classification Statistical Methods and Applications, 14, 163-178, 2005
DOI:10.1007/s10260-005-0110-1

Matin MA, Mardia KV, Taylor CC Support vector machines-type procedure through kriging In Bioinformatics, Images, and Wavelets, 138-140, 2004

Mardia KV, Taylor CC, Subramaniam M EM algorithm for bivariate circular distribution In Bioinformatics, Images, and Wavelets, 146-154, 2004

Kent JT, Mardia KV, Taylor CC Matching problems for unlabelled configurations In Bioinformatics, Images, and Wavelets, 33-36, 2004

Taylor CC, Zempleni A Chain plot: a tool for exploiting bivariate temporal structures COMPUT STAT DATA AN, 46, 141-153, 2004
DOI:10.1016/S0167-9473(03)00120-8

Di Marzio M, Taylor CC Boosting kernel density estimates: A bias reduction technique? BIOMETRIKA, 91, 226-233, 2004

Mardia KV, Taylor CC, Subramaniam M Applications of circular distributions to conformational angles in proteins In Stochastic geometry, biological structure and images, 149-152, 2003

Kent JT, Mardia KV, Taylor CC Matching unlabelled configurations using the EM algorithm In Proceedings in Stochastic Geometry, Biological Structure and Images, 19-21, 2003

Mardia KV, Taylor CC, Westhead DR Structural bioinformatics revisited In Stochastic geometry, biological structure and images, 11-18, 2003

Dryden IL, Scarr MR, Taylor CC Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods J ROY STAT SOC C-APP, 52, 31-50, 2003

Mardia KV, Taylor CC, Subramaniam M Speech recognition and cepstral coefficients In Statistics of large datasets - functional and image data, bioinformatics and data mining, 77-84, 2002

Neely K, Taylor CC, Prosser O, Hamlyn PF Assessment of cooked alpaca and llama meats from the statistical analysis of data collected using an 'electronic nose' Meat Science, 58, 53-58, 2001
DOI:10.1016/S0309-1740(00)00130-3

Taylor CC, Dryden IL, Farnoosh R The K-function for nearly regular point processes. Biometrics, 57, 224-231, 2001
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Southworth R, Mardia KV, Taylor CC Transformation- and label-invariant neural network for the classification of landmark data Journal of Applied Statistics, 27, 205-211, 2000
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Mardia KV, Southworth HR, Taylor CC On bias in maximum likelihood estimators Journal of Statistical Planning and Inference, 76, 31-39, 1999
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Faghihi MR, Taylor CC, Dryden IL Procrustes shape analysis of triangulations of a two-coloured point pattern Statistics and Computing, 9, 43-53, 1999
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Dryden IL, Taylor CC, Faghihi MR Size analysis of nearly regular Delaunay triangulations Methodology and Computing in Applied Probability, 1, 97-117, 1999
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Morris RJ; Mardia KV; Taylor CC; Burrows JD Knowledge based geometric object recognitionIEE Conference Publication, 47-50 1997
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Taylor C; Nakhaeizadeh G Learning in dynamically changing domains: Theory revision and context dependence issuesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 353-360 1997
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Dryden IL, Faghihi MR, Taylor CC Procrustes shape analysis of planar point subsets Royal Statistical Society. Journal. Series B: Methodological, 59, 353-374, 1997
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Nakhaeizadeh G; Taylor CC Machine Learning&Statistics : the interface. John Wiley&Sons, Inc 1997
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Taylor CC Classification and kernel density estimation. 41, 411-417 1997

Taylor CC; Faghihi MR; Dryden IL An understanding of muscle fibre imagesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 223-228 1995
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Burrows JD; Little JA; Mardia KV; Taylor CC; Walder AN Statistical approaches to image restorationIEE Conference Publication, 59-63 1995
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Taylor CC Simulation methods to estimate smoothing parameters in image reconstruction Journal of Statistical Computation and Simulation, 49, 161-177, 1994
DOI:10.1080/00949659408811569
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Lexell J, Sjöström M, Nordlund A, Taylor CC Growth and development of human muscle: A quantitative morphological study of whole vastus lateralis from childhood to adult age Muscle& Nerve, 15, 404-409, 1992
DOI:10.1002/mus.880150323
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Venema HW, Lexell J, Taylor C, Stewart JD, Phillips LH, Ho SL, Shah M, Williams AC, Patock T, Nandedkar SD, Sanders DB, Yuki N, Sato S, Fujimoto S, Yamada S, Tsujino Y, Kinoshita A, Itoh T, Barnes WS, Hasson SM, Shoji S Letters to the editor Muscle& Nerve, 15, 964-972, 1992
DOI:10.1002/mus.880150816

Lexell J, Taylor C Fiber density: A fast and accurate way to estimate human muscle fiber areas [2] Muscle and Nerve, 14, 476-477, 1991

Sjöström M, Lexell J, Eriksson A, Taylor CC Evidence of fibre hyperplasia in human skeletal muscles from healthy young men? - A left-right comparison of the fibre number in whole anterior tibialis muscles European Journal of Applied Physiology and Occupational Physiology, 62, 301-304, 1991
DOI:10.1007/BF00634963
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Lexell J, Taylor CC Variability in muscle fibre areas in whole human quadriceps muscle: Effects of increasing age Journal of Anatomy, 174, 239-249, 1991
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Lexell J, Taylor CC A morphometrical comparison of right and left whole human vastus lateralis muscle: how to reduce sampling errors in biopsy techniques Clinical Physiology, 11, 271-276, 1991
DOI:10.1111/j.1475-097X.1991.tb00458.x
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Lexell J, Taylor C “Smoothed histograms”: A visual aid for the analysis of distributions of muscle fiber areas Muscle& Nerve, 14, 826-828, 1991
DOI:10.1002/mus.880140905
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Sohn YH, Sunwoo IN, Chi JG, Lexell J, Taylor C, Priori A, Berardelli A, Inghilleri M, Cruccu G, Zaccagnini M, Manfredi M, Palmowski A, Reichmann H, Toyka KV, Letz R, Gerr FE, Simpson DM, Rivner MH, Swift TR, Crout BO, Rhodes KP Letters to the editor Muscle& Nerve, 14, 474-480, 1991
DOI:10.1002/mus.880140515

Taylor CC Orthogonal series estimators and cross-validation Journal of Statistical Computation and Simulation, 37, 151-158, 1990
DOI:10.1080/00949659008811301

Taylor CC Bootstrap choice of the smoothing parameter in Kernel density estimation Biometrika, 76, 705-712, 1989
DOI:10.1093/biomet/76.4.705
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Lexell J, Taylor CC Variability in muscle fibre areas in whole human quadriceps muscle: how to reduce sampling errors in biopsy techniques Clinical Physiology, 9, 333-343, 1989
DOI:10.1111/j.1475-097X.1989.tb00987.x
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Lexell J, Taylor CC Variability in muscle fibre areas in whole human quadriceps muscle. How much and why? Acta Physiologica Scandinavica, 136, 561-568, 1989
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Lexell J, Taylor CC, Sjöström M What is the cause of the ageing atrophy?. Total number, size and proportion of different fiber types studied in whole vastus lateralis muscle from 15- to 83-year-old men Journal of the Neurological Sciences, 84, 275-294, 1988
DOI:10.1016/0022-510X(88)90132-3
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Taylor CC Akaike's information criterion and the histogram Biometrika, 74, 636-639, 1987
DOI:10.1093/biomet/74.3.636
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Taylor CC, Burrough PA Multiscale sources of spatial variation in soil. III. Improved methods for fitting the nested model to one-dimensional semivariograms Mathematical Geology, 18, 811-821, 1986
DOI:10.1007/BF00899745
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Lexell J, Taylor C, Sjostrom M Analysis of sampling errors in biopsy techniques using data from whole muscle cross sections Journal of Applied Physiology, 59, 1228-1235, 1985
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Taylor CC A new method for unfolding sphere size distributions Journal of Microscopy, 132, 57-66, 1983
DOI:10.1111/j.1365-2818.1983.tb04708.x