Browse by UCL Departments and Centres
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Number of items: 185.
A
Adame-Siles, JA;
Guerrero-Ginel, JE;
Fearn, T;
Garrido-Varo, A;
Pérez-Marín, D;
(2019)
Multistage and adaptive sampling protocols combined with near-infrared spectral sensors for automated monitoring of raw materials in bulk.
Biosystems Engineering
, 188
pp. 82-95.
10.1016/j.biosystemseng.2019.10.008.
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Akhanli, Serhat Emre;
(2019)
Distance construction and clustering of football player performance data.
Doctoral thesis (Ph.D), UCL (University College London).
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Alexopoulos, A;
Dellaportas, P;
Forster, JJ;
(2019)
Bayesian forecasting of mortality rates by using latent Gaussian models.
Journal of the Royal Statistical Society: Series A (Statistics in Society)
, 182
(2)
pp. 689-711.
10.1111/rssa.12422.
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Arakelian, V;
Dellaportas, P;
Savona, R;
Vezzoli, M;
(2019)
Sovereign risk zones in Europe during and after the debt crisis.
Quantitative Finance
, 19
(6)
pp. 961-980.
10.1080/14697688.2018.1562197.
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B
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Banerjee, G;
Chan, E;
Ambler, G;
Wilson, D;
Cipolotti, L;
Shakeshaft, C;
Cohen, H;
... Vahidassr, D; + view all
(2019)
Effect of small-vessel disease on cognitive trajectory after atrial fibrillation-related ischaemic stroke or TIA.
Journal of Neurology
, 266
(5)
pp. 1250-1259.
10.1007/s00415-019-09256-6.
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Barnes, C;
Chandler, R;
Brierley, C;
(2019)
New approaches to postprocessing of multi-model ensemble forecasts.
Quarterly Journal of the Royal Meteorological Society
, 145
(725)
pp. 3479-3498.
10.1002/qj.3632.
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Barp, A;
Briol, F-X;
Duncan, AB;
Girolami, MA;
Mackey, LW;
(2019)
Minimum Stein Discrepancy Estimators.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett., R, (eds.)
Proceedings of Advances in Neural Information Processing Systems 32 (NIPS 2019).
NIPS: Massachusetts, USA.
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Batool, Fatima;
(2019)
Optimum Average Silhouette Width Clustering Methods.
Doctoral thesis (Ph.D), UCL (University College London).
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Belot, A;
Ndiaye, A;
Luque-Fernandez, M-A;
Kipourou, D-K;
Maringe, C;
Rubio, FJ;
Rachet, B;
(2019)
Summarizing and communicating on survival data according to the audience: a tutorial on different measures illustrated with population-based cancer registry data.
Clinical Epidemiology
, 11
pp. 53-65.
10.2147/CLEP.S173523.
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Bisla, J;
Ambler, G;
Frank, B;
Gulati, S;
Hocken, P;
James, M;
Kelly, J;
... Goebel, A; + view all
(2019)
Successful and unsuccessful recruitment and retainment strategies in a UK multicentre drug trial for a rare chronic pain condition which performed above target.
British Journal of Pain
10.1177/2049463719893399.
(In press).
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Bloom, CI;
Ricciardi, F;
Smeeth, L;
Stone, P;
Quint, JK;
(2019)
Predicting COPD 1-year mortality using prognostic predictors routinely measured in primary care.
BMC Medicine
, 17
(1)
, Article 73. 10.1186/s12916-019-1310-0.
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Bohner, Gergő;
(2019)
Unsupervised methods for large-scale, cell-resolution neural data analysis.
Doctoral thesis (Ph.D), UCL (University College London).
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Bottomley, C;
Scott, JAG;
Isham, V;
(2019)
Analysing Interrupted Time Series with a Control.
Epidemiologic Methods
10.1515/em-2018-0010.
(In press).
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Briol, F-X;
Barp, A;
Duncan, AB;
Girolami, M;
(2019)
Statistical Inference for Generative Models with Maximum Mean Discrepancy.
ArXiv: Ithaca, NY, USA.
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Briol, F-X;
DiazDelaO, FA;
Hristov, PO;
(2019)
Contributed Discussion [A Bayesian Conjugate Gradient Method].
Bayesian Analysis
, 14
(3)
pp. 980-984.
10.1214/19-BA1145.
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Briol, FX;
Oates, CJ;
Girolami, M;
Osborne, MA;
Sejdinovic, D;
(2019)
Probabilistic integration: A role in statistical computation?
Statistical Science
, 34
(1)
pp. 1-22.
10.1214/18-STS660.
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Briol, FX;
Oates, CJ;
Girolami, M;
Osborne, MA;
Sejdinovic, D;
(2019)
Rejoinder: Probabilistic integration: A role in statistical computation?
Statistical Science
, 34
(1)
pp. 38-42.
10.1214/18-STS683.
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Buckman, JEJ;
Saunders, R;
Cohen, Z;
Clarke, K;
Ambler, G;
DeRubeis, R;
Gilbody, S;
... Pilling, S; + view all
(2019)
What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database [version 1; peer review: 1 approved, 1 approved with reservations].
Wellcome Open Research
, 4
, Article 69. 10.12688/wellcomeopenres.15225.1.
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Buckman, JEJ;
Saunders, R;
Cohen, ZD;
Clarke, K;
Ambler, G;
DeRubeis, RJ;
Gilbody, S;
... Pilling, S; + view all
(2019)
What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database [version 2; peer review: 2 approved].
Wellcome Open Research
, 4
, Article 69. 10.12688/wellcomeopenres.15225.2.
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Bullement, Ash;
Taylor, Matthew;
McMordie, Sam Thomas;
Waters, Errol;
Hatswell, Anthony James;
(2019)
NICE, in Confidence: An Assessment of Redaction to Obscure Confidential Information in Single Technology Appraisals by the National Institute for Health and Care Excellence.
PharmacoEconomics
, 37
pp. 1383-1390.
10.1007/s40273-019-00818-0.
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Bullement, A;
Nathan, P;
Willis, A;
Amin, A;
Lilley, C;
Stapelkamp, C;
Hatswell, A;
... Bharmal, M; + view all
(2019)
Cost Effectiveness of Avelumab for Metastatic Merkel Cell Carcinoma.
PharmacoEconomics - Open
, 3
pp. 377-390.
10.1007/s41669-018-0115-y.
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C
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Carmo, Rafael Augusto Ferreira do;
(2019)
Models and Algorithms for Episodic Time-Series.
Doctoral thesis (Ph.D), UCL (University College London).
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Chan, MS;
Van Den Hout, A;
Pujades-Rodriguez, M;
Jones, MM;
Matthews, FE;
Jagger, C;
Raine, R;
(2019)
Socioeconomic inequalities in life expectancy of older adults with and without multimorbidity: a record linkage study of 1.1 million people in England.
International Journal of Epidemiology
, 48
(4)
pp. 1340-1351.
10.1093/ije/dyz052.
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Chang, KL;
Guillas, S;
(2019)
Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model.
Journal of the Royal Statistical Society - Applied Statistics: Series C
, 68
(1)
pp. 51-78.
10.1111/rssc.12309.
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Chen, WY;
Barp, A;
Briol, F-X;
Gorham, J;
Girolami, M;
Mackey, L;
Oates, CJ;
(2019)
Stein Point Markov Chain Monte Carlo.
In: Chaudhuri, Kamalika and Salakhutdinov, Ruslan, (eds.)
Proceedings of the 36th International Conference on Machine Learning.
(pp. pp. 1011-1021).
Proceedings of Machine Learning Research: Long Beach, California, USA.
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Chen, Y;
Tanaka, M;
Siddiqui, A;
(2019)
A Leader-Follower Model for Tradable Performance-Based CO2 Emissions Standards.
In:
(pp. pp. 941-946).
IEEE: Miyazaki, Japan.
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Chisholm, S;
Stein, AB;
Jordan, NR;
Hubel, TM;
Shawe-Taylor, J;
Fearn, T;
McNutt, JW;
... Hailes, S; + view all
(2019)
Parsimonious test of dynamic interaction.
Ecology and Evolution
, 9
(4)
pp. 1654-1664.
10.1002/ece3.4805.
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Choi, D;
Pavlou, M;
Omar, R;
Arts, M;
Balabaud, L;
Buchowski, JM;
Bunger, C;
... Crockard, HA; + view all
(2019)
A novel risk calculator to predict outcome after surgery for symptomatic spinal metastases; use of a large prospective patient database to personalise surgical management.
European Journal Cancer
, 107
pp. 28-36.
10.1016/j.ejca.2018.11.011.
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Coats, CJ;
Pavlou, M;
Watkinson, OT;
Protonotarios, A;
Moss, L;
Hyland, R;
Rantell, K;
... Elliott, PM; + view all
(2019)
Effect of Trimetazidine Dihydrochloride Therapy on Exercise Capacity in Patients With Nonobstructive Hypertrophic Cardiomyopathy: A Randomized Clinical Trial.
JAMA Cardiology
, 4
(3)
pp. 230-235.
10.1001/jamacardio.2018.4847.
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Cotar, C;
Petrache, M;
(2019)
Next-order asymptotic expansion for N-marginal optimal transport with Coulomb and Riesz costs.
Advances in Mathematics
, 344
pp. 137-233.
10.1016/j.aim.2018.12.008.
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Coullon, Jeremie;
(2019)
MCMC for a hyperbolic Bayesian inverse problem in motorway traffic flow.
Doctoral thesis (Ph.D), UCL (University College London).
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Crawley, J;
Biddulph, P;
Northrop, PJ;
Wingfield, J;
Oreszczyn, T;
Elwell, C;
(2019)
Quantifying the measurement error on England and Wales EPC ratings.
Energies
, 12
(18)
, Article 3523. 10.3390/en12183523.
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Cunningham, N;
Griffin, J;
Wild, D;
Lee, A;
(2019)
particleMDI: a Julia Package for the Integrative Cluster Analysis of Multiple Datasets.
In:
Proceedings of International Conference on Bayesian Statistics in Action BAYSM 2018: Bayesian Statistics and New Generations.
(pp. pp. 65-74).
Springer, Cham
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D
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D’Antona, L;
McHugh, JA;
Ricciardi, F;
Thorne, LW;
Matharu, MS;
Watkins, LD;
Toma, AK;
(2019)
Association of Intracranial Pressure and Spontaneous Retinal Venous Pulsation.
JAMA Neurology
10.1001/jamaneurol.2019.2935.
(In press).
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De Mijolla, D;
Viti, S;
Holdship, J;
Manolopoulou, I;
Yates, J;
(2019)
Incorporating astrochemistry into molecular line modelling via emulation.
Astronomy and Astrophysics
, 630
, Article A117. 10.1051/0004-6361/201935973.
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Debia, S;
Pineau, P-O;
Siddiqui, AS;
(2019)
Strategic use of storage: The impact of carbon policy, resource availability, and technology efficiency on a renewable-thermal power system.
Energy Economics
, 80
pp. 100-122.
10.1016/j.eneco.2018.12.006.
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Dellaportas, P;
Titsias, M;
(2019)
Gradient-based Adaptive Markov Chain Monte Carlo.
In:
Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
33rd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada.
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Dellaportas, P;
Tsionas, MG;
(2019)
Importance sampling from posterior distributions using copula-base approximations.
Journal of Econometrics
, 210
(1)
pp. 45-57.
10.1016/j.jeconom.2018.11.004.
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Di Tanna, GL;
Porter, JK;
Lipton, RB;
Brennan, A;
Palmer, S;
Hatswell, AJ;
Sapra, S;
(2019)
Migraine day frequency in migraine prevention: longitudinal modelling approaches.
BMC Medical Research Methodology
, 19
, Article 20. 10.1186/s12874-019-0664-5.
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Di Tanna, GL;
Porter, JK;
Lipton, RB;
Hatswell, AJ;
Sapra, S;
Villa, G;
(2019)
Longitudinal assessment of utilities in patients with migraine: an analysis of erenumab randomized controlled trials.
Health Qual Life Outcomes
, 17
(1)
, Article 171. 10.1186/s12955-019-1242-6.
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Diana, A;
Griffin, J;
Matechou, E;
(2019)
A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population.
In: Argiento, Raffaele and Durante, Daniele and Wade, Sara, (eds.)
Proceedings of Bayesian Young Statisticians Meeting 2018 - BAYSM2018.
(pp. pp. 3-11).
Springer
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Dias, FS;
Peters, GW;
(2019)
A Non-parametric Test and Predictive Model for Signed Path Dependence.
Computational Economics
10.1007/s10614-019-09934-7.
(In press).
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Dixon, WG;
Beukenhorst, AL;
Yimer, BB;
Cook, L;
Gasparrini, A;
El-Hay, T;
Hellman, B;
... McBeth, J; + view all
(2019)
How the weather affects the pain of citizen scientists using a smartphone app.
npj Digital Medicine
, 2
, Article 105. 10.1038/s41746-019-0180-3.
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Dong, M;
Wang, Y;
Yang, X;
Xue, J-H;
(2019)
Learning Local Metrics and Influential Regions for Classification.
IEEE Transactions on Pattern Analysis and Machine Intelligence
10.1109/tpami.2019.2914899.
(In press).
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Dong, Minghzi;
(2019)
Metric Learning with Lipschitz Continuous Functions.
Doctoral thesis (Ph.D), UCL (University College London).
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Döring, L;
Watson, AR;
Weissmann, P;
(2019)
Lévy processes with finite variance conditioned to avoid an interval.
Electronic Journal of Probability
, 24
(55)
pp. 1-32.
10.1214/19-EJP306.
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Du, C;
Lu, Z;
Xue, JH;
Liao, Q;
(2019)
A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection.
In:
Proceedings of the Eleventh International Conference on Digital Image Processing (ICDIP 2019).
SPIE
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Duran-Casablancas, C;
Grau-Bové, J;
Fearn, T;
Strlič, M;
(2019)
Accumulation of wear and tear in archival and library collections. Part II: an epidemiological study.
Heritage Science
, 7
(1)
, Article 11. 10.1186/s40494-019-0253-2.
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E
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Ehrhardt, B;
Wolfe, PJ;
(2019)
Network Modularity in the Presence of Covariates.
SIAM Review
, 61
(2)
pp. 261-276.
10.1137/17M1111528.
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Espasandín-Domínguez, J;
Cadarso-Suárez, C;
Kneib, T;
Marra, G;
Klein, N;
Radice, R;
Lado-Baleato, O;
... Gude, F; + view all
(2019)
Assessing the relationship between markers of glycemic control through flexible copula regression models.
Statistics in Medicine
, 38
(27)
pp. 5161-5181.
10.1002/sim.8358.
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F
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Fearn, T;
Pérez Marín, D;
Garrido Varo, A;
Guerrero Ginel, JE;
(2019)
Classifying with confidence using Bayes rule and kernel density estimation.
Chemometrics and Intelligent Laboratory Systems
, 189
pp. 81-87.
10.1016/j.chemolab.2019.04.0004.
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Filippou, P;
Kneib, T;
Marra, G;
Radice, R;
(2019)
A Trivariate Additive Regression Model with Arbitrary Link Functions and Varying Correlation Matrix.
Journal of Statistical Planning and Inference
, 119
pp. 236-248.
10.1016/j.jspi.2018.07.002.
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Finke, A;
King, R;
Beskos, A;
Dellaportas, P;
(2019)
Efficient Sequential Monte Carlo Algorithms for Integrated Population Models.
Journal of Agricultural, Biological and Environmental Statistics
, 24
(2)
pp. 204-224.
10.1007/s13253-018-00349-9.
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Francisco, EDR;
Kugler, JL;
Kang, SM;
Silva, R;
Whigham, PA;
(2019)
Beyond Technology: Management Challenges in the Big Data Era.
Revista de Administração de Empresas
, 59
(6)
pp. 375-378.
10.1590/S0034-759020190603.
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Froghi, F;
Soggiu, F;
Ricciardi, F;
Gurusamy, K;
Martin, DS;
Singh, J;
Siddique, S;
... Davidson, BR; + view all
(2019)
Ward-based Goal-Directed Fluid Therapy (GDFT) in Acute Pancreatitis (GAP) trial: study protocol for a feasibility randomised controlled trial.
BMJ Open
, 9
(10)
, Article e028783. 10.1136/bmjopen-2018-028783.
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G
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Gabrio, A;
Baio, G;
Manca, A;
(2019)
Bayesian Statistical Economic Evaluation Methods for Health Technology Assessment.
In: Hamilton, JH, (ed.)
Economic Theory and Mathematical Models.
Oxford Research Encyclopedia of Economics and Finance: Oxford, UK.
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Gabrio, A;
Mason, A;
Baio, G;
(2019)
A full Bayesian model to handle structural ones and missingness in economic evaluations from individual-level data.
Statistics in Medicine
, 38
(8)
pp. 1399-1420.
10.1002/sim.8045.
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Gabrio, Andrea;
(2019)
Full Bayesian Methods to Handle Missing Data in Health Economic Evaluation.
Doctoral thesis (Ph.D), UCL (University College London).
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Galbraith, AB;
(2019)
The Mayor of Ngaruawahia.
Rex Galbraith: London, UK.
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Garg, A;
Vickerstaff, V;
Nathwani, N;
Garway-Heath, D;
Konstantakopoulou, E;
Ambler, G;
Bunce, C;
... Gazzard, G; + view all
(2019)
Efficacy of Repeat Selective Laser Trabeculoplasty in Medication-Naïve Open Angle Glaucoma and Ocular Hypertension during the LiGHT Trial.
Ophthalmology
10.1016/j.ophtha.2019.10.023.
(In press).
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Garg, A;
Vickerstaff, V;
Nathwani, N;
Garway-Heath, D;
Konstantakopoulou, E;
Ambler, G;
Bunce, C;
... LiGHT Trial Study Group; + view all
(2019)
Primary Selective Laser Trabeculoplasty for Open Angle Glaucoma and Ocular Hypertension: Clinical Outcomes, Predictors of Success and Safety from the Laser in Glaucoma and Ocular Hypertension (LiGHT) Trial.
Ophthalmology
10.1016/j.ophtha.2019.04.012.
(In press).
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Garrido-Varo, A;
Garcia-Olmo, J;
Fearn, T;
(2019)
A note on Mahalanobis and related distance measures in WinISI and The Unscrambler.
Journal of Near Infrared Spectroscopy
, 27
(4)
253 -258.
10.1177/0967033519848296.
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Garrido-Varo, A;
Riccioli, C;
Fearn, T;
De Pedro, E;
Perez-Marin, D;
(2019)
Multivariate predictive models for the prediction of fatty acids in the EU high added-value "acorn Iberian pig ham" using a miniature near-infrared spectroscopy instrument.
In: Kim, MS and Chin, BA and Cho, BK, (eds.)
SENSING FOR AGRICULTURE AND FOOD QUALITY AND SAFETY XI.
SPIE: Baltimore, MD, United States.
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Gazzard, G;
Konstantakopoulou, E;
Garway-Heath, D;
Garg, A;
Vickerstaff, V;
Hunter, R;
Ambler, G;
... LiGHT Trial Study Group; + view all
(2019)
Selective laser trabeculoplasty versus eye drops for first-line treatment of ocular hypertension and glaucoma (LiGHT): a multicentre randomised controlled trial.
The Lancet
10.1016/S0140-6736(18)32213-X.
(In press).
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Gazzard, G;
Konstantakopoulou, E;
Garway-Heath, D;
Garg, A;
Vickerstaff, V;
Hunter, R;
Ambler, G;
... Buszewicz, M; + view all
(2019)
Selective laser trabeculoplasty versus drops for newly diagnosed ocular hypertension and glaucoma: the LiGHT RCT.
Health Technology Assessment
, 23
(31)
pp. 1-102.
10.3310/hta23310.
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Geneletti, S;
Ricciardi, F;
O'Keeffe, AG;
Baio, G;
(2019)
Bayesian modelling for binary outcomes in the regression discontinuity design.
Journal of the Royal Statistical Society: Series A (Statistics in Society)
, 182
(3)
pp. 983-1002.
10.1111/rssa.12440.
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Gessler, S;
King, M;
Lemma, A;
Barber, J;
Jones, L;
Dunning, S;
Madden, V;
... Lanceley, A; + view all
(2019)
Stepped approach to improving sexual function after gynaecological cancer: the SAFFRON feasibility RCT.
Health Technology Assessment
, 23
(6)
pp. 1-92.
10.3310/hta23060.
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Griffin, J;
(2019)
Two part envelopes for rejection sampling of some completely random measures.
Statistics and Probability Letters
, 151
pp. 36-41.
10.1016/j.spl.2019.03.004.
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H
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Hajmohammadi, H;
Marra, G;
Heydecker, B;
(2019)
Data-driven models for microscopic vehicle emissions.
Transportation Research Part D: Transport and Environment
, 76
pp. 138-154.
10.1016/j.trd.2019.09.013.
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Han, Q;
Wang, T;
Chatterjee, S;
Samworth, RJ;
(2019)
Isotonic regression in general dimensions.
Annals of Statistics
, 47
(5)
pp. 2440-2471.
10.1214/18-AOS1753.
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Han, X;
Yu, J;
Xue, JH;
Sun, W;
(2019)
Spectral Super-resolution for RGB Images using Class-based BP Neural Networks.
In:
(Proceedings) 2018 Digital Image Computing: Techniques and Applications (DICTA).
IEEE
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Hatswell, AJ;
Burns, D;
Baio, G;
Wadelin, F;
(2019)
Frequentist and Bayesian meta‐regression of health state utilities for multiple myeloma incorporating systematic review and analysis of individual patient data.
Health Economics
, 28
(5)
pp. 653-665.
10.1002/hec.3871.
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Hatswell, AJ;
Porter, JK;
(2019)
Reducing Drug Wastage in Pharmaceuticals Dosed by Weight or Body Surface Areas by Optimising Vial Sizes.
Applied Health Economics and Health Policy
, 17
(3)
pp. 391-397.
10.1007/s40258-018-0444-0.
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Hatswell, AJ;
Sullivan, WG;
(2019)
Creating historical controls using data from a previous line of treatment – Two non-standard approaches.
Statistical Methods in Medical Research
10.1177/0962280219826609.
(In press).
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Heath, A;
Manolopoulou, I;
Baio, G;
(2019)
Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression.
Medical Decision Making
10.1177/0272989X19837983.
(In press).
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Hennig, C;
Viroli, C;
Anderlucci, L;
(2019)
Quantile-based clustering.
Electronic Journal of Statistics
, 13
(2)
pp. 4849-4883.
10.1214/19-ejs1640.
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Hennig, CM;
Sauerbrei, W;
(2019)
Exploration of the variability of variable selection based on distances between bootstrap sample results.
Advances in Data Analysis and Classification
10.1007/s11634-018-00351-6.
(In press).
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Hirt, M;
Dellaportas, P;
(2019)
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers.
In: Chaudhuri, K and Sugiyama, M, (eds.)
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics.
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