Dr Nicolás Hernández
Lecturer in Statistics
School of Mathematical Sciences
Queen Mary University of London
Research
High Dimensional and Functional Data Analysis, Time Series, Variable Selection, Outlier Detection, Prediction and Classification
Interests
Dr. Hernández is a Lecturer in Statistics within the Data Science, Statistics and Probability Centre at the School of Mathematical Sciences. He joined QMUL after spending 2 years as a Senior Research Fellow within the Institute of Mathematics and Statistical Science at the Department of Statistical Science, UCL. Previously he was appointed as a PDRA at the MRC Biostatistics Unit of the University of Cambridge. Before that he completed his PhD studied about ‘‘Statistical learning methods for functional data with applications to prediction, classification and outlier detection’’ at the Department of Statistics of Universidad Carlos III de Madrid.
His main research is oriented to develop statistical and machine learning methods to tackle inferential problems in high-dimensional and functional data over different fields such as: energy, economics, the environment, demography, business, finance, health and genetics. He has mainly focused on predictive confidence bands for functional time series; domain selection and classification in the Functional Data context; and outlier detection for stochastic processes using Information Theory tools.
Publications
Publications of specific relevance to the Centre for Probability, Statistics and Data Science
2024
Domain Selection for Gaussian Process Data: An application to electrocardiogram signalsHernandez N and Martos G
Biometrical Journal,
Wiley-Vch Verlag 28-11-2024Simultaneous predictive bands for functional time series using minimum entropy setsHernández N, Cugliari J and Jacques J
Communications in Statistics - Simulation and Computation,
Taylor & Francis, 1-25.
23-08-2024 2023
Density kernel depth for outlier detection in functional dataHernández N, Muñoz A and Martos G
International Journal of Data Science and Analytics,
Springer Nature vol. 16 (4), 481-488.
04-08-2023 2021
The flashfm approach for fine-mapping multiple quantitative traitsHernández N, Soenksen J, Newcombe P, Sandhu M, Barroso I, Wallace C and Asimit JL
Nature Communications,
Springer Nature vol. 12 (1)
22-10-2021 2018
Combining Entropy Measures for Anomaly DetectionMuñoz A, Hernández N, Moguerza JM and Martos G
Entropy,
Mdpi vol. 20 (9)
12-09-2018 Entropy Measures for Stochastic Processes with Applications in Functional Anomaly DetectionMartos G, Hernández N, Muñoz A and Moguerza JM
Entropy,
Mdpi vol. 20 (1)
11-01-2018 2016
Kernel Depth Measures for Functional Data with Application to Outlier DetectionHernandez N and Muñoz A
In
Artificial Neural Networks and Machine Learning – Icann 2016 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II,
Springer Editors: Villa AEP, Masulli P and Pons Rivero AJ.
26-08-2016
