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Derivative curve estimation in longitudinal studies using P-splines
(2023-09-18)
The estimation of curve derivatives is of interest in many disciplines. It allows the extraction of important characteristics to gain insight about the underlying process. In the context of longitudinal data, the derivative ...
A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data
(2022-02-24)
High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical ...
ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates
(2021-01-01)
This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric ...
Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models
(2021-01-01)
A package is introduced that provides the weighted smooth backfitting estimator for a large family of popular semiparametric regression models. This family is known as generalized structured models, comprising, for example, ...
Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points
(2021)
Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput eld phenotyping (HTFP) hold ...
Bayesian hierarchical modelling of growth curve derivatives via sequences of quotient differences
(2020-01)
Growth curve studies are typically conducted to evaluate differences between group or treatment-specific curves. Most analyses focus solely on the growth curves, but it has been argued that the derivative of growth curves ...
Multidimensional Adaptive Penalised Splines with Application to Neurons' Activity Studies
(2020)
P-spline models have achieved great popularity both in statistical and in applied research.
A possible drawback of P-spline is that they assume a smooth transition of the covariate effect
across its whole domain. In some ...
Bayesian nonparametric inference for the covariate-adjusted ROC curve
(2018-05-30)
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 ...
On the estimation of variance parameters in non-standard generalised linear mixed models: Application to penalised smoothing
(2018-01-24)
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 ...
Sample size impact on the categorisation of continuous variables in clinical prediction
(2017-12)
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 ...