J. Romo

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Lasso variable selection in functional regression

J. Romo, R. Lillo, N. Mingotti, 2013 .

On the Concept of Depth for Functional Data

J. Romo, S. López-Pintado, 2009 .

Stability under contamination of robust regression estimators based on differences of residuals

J. Romo, J. R. Berrendero, 1996 .

Effects of parameter estimation on prediction densities: a bootstrap approach

Juan Romo, Lorenzo Pascual, Esther Ruiz, 1999 .

Forecasting time series with sieve bootstrap

Juan Romo, Daniel Peña, J. Romo, 2002 .

Bootstrap tests for unit roots based on LAD estimation

J. Romo, Marta Moreno, 2000 .

Extremality measures and a rank test for functional data

R. E. Lillo, J. Romo, A. M. Franco-Pereira, 2014 .

Classification of functional data: a weighted distance approach

J. Romo, A. Alonso, David Casado, 2009 .

On sieve bootstrap prediction intervals

Juan Romo, Andrés M. Alonso, Daniel Peña, 2003 .

Resampling time series using missing values techniques

J. Romo, D. Peña, A. Alonso, 2003 .

A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms.

M. C. Aguilera-Morillo, J. Romo, R. Lillo, 2018, Blood advances.

- 0101 Forecasting time series with sieve bootstrap

J. Romo, D. Peña, M. Alonso, 2022 .

Variable selection with P‐splines in functional linear regression: Application in graft‐versus‐host disease

M. C. Aguilera-Morillo, J. Romo, R. Lillo, 2020, Biometrical journal. Biometrische Zeitschrift.

A Kendall correlation coefficient between functional data

J. Romo, R. Lillo, Dalia Valencia, 2019, Advances in Data Analysis and Classification.

Discussion of “Multivariate functional outlier detection”

J. Romo, Ana Arribas-Gil, 2015, Statistical Methods & Applications.

Comparing quantile residual life functions by confidence bands

A. M. Franco-Pereira, J. Romo, R. Lillo, 2011, Lifetime Data Analysis.

Differentiable Functionals and Smoothed Bootstrap

J. Romo, A. Cuevas, 1997 .

Initializing k-means Clustering by Bootstrap and Data Depth

Aurora Torrente, Juan Romo, J. Romo, 2020, Journal of Classification.

J. Romo, A. Lopez, 2010 .

Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market

Juan Romo, Esther Ruiz, J. Romo, 2009 .

The effect of liquidity on the price discovery process in credit derivatives markets in times of financial distress

J. Romo, J. I. Peña, S. Mayordomo, 2011 .

On the type hypothesis for the strong law of large numbers

J. Romo, Víctor Hernández, 1987 .

Constant coefficient tests for random coefficient regression

J. Romo, P. Delicado, 1998 .

Random coefficient regressions: parametric goodness-of-fit tests

J. Romo, P. Delicado, Pedro Delicado, 2004 .

Goodness of Fit Tests in Random Coefficient Regression Models

J. Romo, P. Delicado, 1999 .

The central limit theorem for empirical processes on V-Č classes: A majorizing measure approach

J. Romo, 1994 .

Stable limits for empirical processes on Vapnik-Ccervonenkis classes of functions

J. Romo, 1993 .

DepthTools: an R package for a robust analysis of gene expression data

Aurora Torrente, Sara López-Pintado, Juan Romo, 2013, BMC Bioinformatics.

Robust depth-based tools for the analysis of gene expression data.

Aurora Torrente, Sara López-Pintado, Juan Romo, 2010, Biostatistics.

roahd Package: Robust Analysis of High Dimensional Data

Francesca Ieva, Anna Maria Paganoni, Juan Romo, 2019, R J..

Discussion of “ Multivariate Functional Outlier Detection ”

Juan Romo, Ana Arribas-Gil, J. A. Cuesta-Albertos, 2015 .

Shape outlier detection and visualization for functional data: the outliergram.

Juan Romo, Ana Arribas-Gil, J. Romo, 2013, Biostatistics.

Comparing quantile residual life functions by confidence bands

Juan Romo, Rosa E. Lillo, Alba M. Franco-Pereira, 2012, Lifetime data analysis.

Homogeneity test for functional data

Juan Romo, Rosa Lillo, Ram'on Flores, 2015, 1507.01835.

A half-region depth for functional data

Juan Romo, Sara López-Pintado, J. Romo, 2011, Comput. Stat. Data Anal..

Extremality for Functional Data

Juan Romo, Rosa E. Lillo, Alba M. Franco-Pereira, 2011 .

On the explosion breakdown rate of the maximum bias function of some scale and location estimates

J. Romo, R. Zamar, J. R. Berrendero, 1995 .

Robust Functional Supervised Classification for Time Series

J. Romo, S. López-Pintado, A. Alonso, 2014, J. Classif..

Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer

M. C. Aguilera-Morillo, Rosa Elvira Lillo Rodríguez, Miguel Martín, 2021, Mathematics.

Testing for statistical arbitrage in credit derivatives markets

J. Romo, J. I. Peña, S. Mayordomo, 2014 .

Resampling time series using missing values techniques

Juan Romo, Andrés M. Alonso, Daniel Peña, 2003 .

Bootstrap prediction for returns and volatilities in GARCH models

Juan Romo, Lorenzo Pascual, Esther Ruiz, 2006, Comput. Stat. Data Anal..

A Kendall correlation coefficient between functional data

Juan Romo, Rosa E. Lillo, Dalia Valencia, 2019, Adv. Data Anal. Classif..

Supervised classification for functional data: A weighted distance approach

Andrés M. Alonso, David Casado, Juan Romo, 2012, Comput. Stat. Data Anal..

Depth-based classification for functional data

Juan Romo, Sara López-Pintado, J. Romo, 2005, Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications.

A New Test of Statistical Arbitrage with Applications to Credit Derivatives Markets

J. Romo, J. I. Peña, S. Mayordomo, 2011 .

Are There Arbitrage Opportunities in Credit Derivatives Markets? A New Test and an Application to the Case of CDS and ASPs

J. Romo, Juan Ignacio Peña Sánchez de Rivera, S. Mayordomo, 2009 .

On robustness properties of bootstrap approximations

Juan Romo, Antonio Cuevas, J. Romo, 1993 .

Bootstrap predictive inference for ARIMA processes

Juan Romo, Lorenzo Pascual, Esther Ruiz, 2004 .

Introducing model uncertainty by moving blocks bootstrap

Juan Romo, Andrés M. Alonso, Daniel Peña, 2006 .

Introducing model uncertainty in time series bootstrap

Juan Romo, Andrés M. Alonso, Daniel Peña, 2001 .

Censored functional data for incomplete follow‐up studies

J. Romo, E. Strzalkowska-Kominiak, 2021, Statistics in medicine.

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks

Luis F. Chiroque, Antonio Fernandez Anta, A Azcorra, 2018, Scientific Reports.

Depthgram: Visualizing outliers in high‐dimensional functional data with application to fMRI data exploration

J. Romo, M. Desco, Antonio Elías, 2022, Statistics in medicine.

Discussion of “Multivariate functional outlier detection”

J. Romo, Ana Arribas-Gil, 2015, Statistical Methods & Applications.

A rate of convergence in clustering analysis

J. Romo, 1992 .

Bootstrap prediction intervals for power-transformed time series

Juan Romo, Lorenzo Pascual, Esther Ruiz, 2005 .

Interpretable support vector machines for functional data

Juan Romo, Belen Martin-Barragan, Rosa Lillo, 2014, Eur. J. Oper. Res..

Band depth based initialization of K-means for functional data clustering

Aurora Torrente, Juan Romo, Javier Albert-Smet, 2021, Advances in Data Analysis and Classification.

DepthTools: an R package for a robust analysis of gene expression data

J. Romo, S. López-Pintado, Aurora Torrente, 2013, BMC Bioinformatics.

The bootstrap -A review

Wenceslao González-Manteiga, José Manuel Prada Sánchez, Juan Romo, 1992 .

Unit root bootstrap tests for AR(1) models

Juan Romo, Nélida E. Ferretti, J. Romo, 1996 .

Bootstrap tests for unit root AR(1) models

Nélida E. Ferretti, J. Romo, 1993 .

Unit root bootstrap tests under infinite variance

J. Romo, Marta Moreno, 2012 .

A functional data based method for time series classification

Juan Romo, Sara López Pintado, David Casado, 2008 .

On the estimation of the influence curve

J. Romo, A. Cuevas, 1995 .

The percentile residual life up to time t0: ordering and aging properties

A. M. Franco-Pereira, J. Romo, R. Lillo, 2011 .

Percentile residual life orders

Moshe Shaked, Juan Romo, Rosa E. Lillo, 2011 .

Characterization of bathtub distributions via percentile residual life functions

Rosa Elvira Lillo Rodríguez, Juan Romo Urroz, Alba María Franco Pereira, 2010 .

TILE RESIDUAL LIFE UP TO TIME t 0 : ORDERI G A D AGI G PROPERTIES

A. M. Franco-Pereira, J. Romo, R. Lillo, 2022 .

Forecasting returns and volatilities in GARCH processes using the bootstrap

J. Romo, E. Ruiz, L. Pascual, 2000 .

Depth-based inference for functional data

Juan Romo, Sara López-Pintado, J. Romo, 2007, Comput. Stat. Data Anal..