Browsing by Author "RodríguezÁlvarez M.X."
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Spatiotemporal adaptive penalized splines with application to Neuroscience
RodríguezÁlvarez M.X.; Durbán M.; Lee D.J.; Eilers P.H.C.; Gonzalez, F (20161230)Data analysed here derive from experiments conducted to study neurons' activity in the visual cortex of behaving monkeys. We consider a spatiotemporal adaptive penalized spline (Pspline) approach for modelling the firing ... 
Spatial Models for Field Trials
RodríguezÁlvarez M.X.; Boer M.P.; Eeuwijk, F.; Eilers, P.H.C. (20160901)An important aim of the analysis of agricultural field trials is to obtain good predictions for genotypic performance, by correcting for spatial effects. In practice these corrections turn out to be complicated, since there ... 
Sample size impact on the categorisation of continuous variables in clinical prediction
Barrio I.; Arostegui I.; RodríguezÁlvarez M.X. (Trends in Mathematics. Research Perspectives CRM Barcelona. Extended Abstracts Fall 2015. Biomedical Big Data; Statistics for Low Dose Radiation Research, 201712)Recent advances in information technologies are generating a growth in the amount of available biomedical data. In this paper, we studied the impact sample size may have on the categorisation of a continuous predictor ... 
On the estimation of variance parameters in nonstandard generalised linear mixed models: Application to penalised smoothing
RodríguezÁlvarez M.X.; Durban M.; Lee D.J.; Eilers P.H.C. (Statistics and Computing, 20180124)We present a novel method for the estimation of variance parameters in generalised linear mixed models. The method has its roots in Harville (1977)'s work, but it is able to deal with models that have a precision matrix ... 
A new approach to categorize continuous variables in prediction models: Proposal and validation
Barrio I.; Arostegui I.; RodríguezÁlvarez M.X.; Quintana J.M. (Statistical Methods in Medical Research, 201712)When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from ... 
Modelling spatial trends in sorghum breeding field trials using a twodimensional Pspline mixed model
Velazco J.G.; RodríguezÁlvarez M.X.; Boer M.P.; Jordan D.R.; Eilers P.H.C.; Malosetti M.; van Eeuwijk F. (Theoretical and Applied Genetics, 20170403)Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multistep modelling process ... 
Fast smoothing parameter separation in multidimensional generalized Psplines: the SAP algorithm
RodríguezÁlvarez M.X.; Lee D.J.; Kneib T.; Durbán M.; Eilers P. (Statistics and Computing, 20141231)A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized linear model with anisotropic penalty is presented. This new proposal is based on the mixed model ... 
Fast estimation of multidimensional adaptive Pspline models
RodríguezÁlvarez M.X.; Durbán M.; Lee D.J.; Eilers P.H.C. (20161021)A fast and stable algorithm for estimating multidimensional adaptive Pspline models is presented. We call it as Separation of Overlapping Penalties (SOP) as it is an extension of the Separation of Anisotropic Penalties ... 
Fast algorithm for smoothing parameter selection in multidimensional Psplines
RodríguezÁlvarez M.X.; Lee D.J.; Kneib T.; Durbán M.; Eilers P.H.C. (Statistics and Computing, 20151231)A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized model with anisotropic penalty is presented. This new proposal is based on the mixed model representation ... 
Correcting for spatial heterogeneity in plant breeding experiments with Psplines
RodríguezÁlvarez M.X.; Boer M.P.; van Eeuwijk F.A.; Eilers P.H.C. (Spatial Statistics, 20171027)An important aim of the analysis of agricultural field experiments is to obtain good predictions for genotypic performance, by correcting for spatial effects. In practice these corrections turn out to be complicated, since ... 
Comparison of two discrimination indexes in the categorisation of continuous predictors in timetoevent studies
Barrio I.; RodríguezÁlvarez M.X.; MeiraMachado L.; Esteban C.; Arostegui I. (SORT (Statistics and Operations Research Transactions), 201704)The Cox proportional hazards model is the most widely used survival prediction model for analysing timetoevent data. To measure the discrimination ability of a survival model the concordance probability index is widely ... 
Bootstrapbased procedures for inference in nonparametric ROC regression analysis
RodríguezÁlvarez M.X.; RocaPardiñas J.; CadarsoSuárez C.; Tahoces P.G. (20160630)Before the use of a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is an essential step. The receiver operating characteristic (ROC) curve is the measure of accuracy most ... 
Bootstrapbased procedures for inference in nonparametric receiveroperating characteristic curve regression analysis
RodríguezÁlvarez M.X.; RocaPardiñas J.; CadarsoSuárez C.; Tahoces P.G. (Statistical Methods in Medical Research, 2017)Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiveroperating characteristic curve is the measure of accuracy most widely used for ... 
Bayesian nonparametric inference for the covariateadjusted ROC curve
Inacio de Carvalho V.; RodríguezÁlvarez M.X. (20180530)Accurate diagnosis of disease is of fundamental importance in clinical practice and medical research. Before a medical diagnostic test is routinely used in practice, its ability to distinguish between diseased and nondiseased ...