Applied Statisticshttp://hdl.handle.net/20.500.11824/102024-03-19T11:25:24Z2024-03-19T11:25:24ZStatistical Modelling for Recurrent Events in Sports Injury Research with Applications to Football Injury DataZumeta, L.http://hdl.handle.net/20.500.11824/17362024-01-15T23:20:03Z2023-01-01T00:00:00ZStatistical Modelling for Recurrent Events in Sports Injury Research with Applications to Football Injury Data
Zumeta, L.
Sports injuries stand as undesirable side effects of athletic participation, carrying serious consequences for athletes' health, their professional careers, and overall team performance. With the growing availability of data, there has been an increasing reliance on statistical models to monitor athletes' health and mitigate the risks of injuries.
In this dissertation, we present advanced statistical modelling approaches and software tools for sports injury data. Our focus is on the time-varying and recurrent nature of injury occurrences, and we pursue three primary objectives: (a) identifying biomechanical risk factors using variable selection methods and shared frailty Cox models, (b) developing a flexible recurrent time-to-event approach to model the effects of training load on subsequent injuries, and (c) creating dedicated statistical tools through the statistical open-source software \textbf{R}. These objectives are driven by interdisciplinary research, conducted in close collaboration with the Medical Services of Athletic Club, and are motivated by real-world applications. Specifically, the work is based on three distinct data sets: functional screening tests data, external training load data, and web-scraped football injury data. The statistical advancements developed contribute to ongoing efforts in sports injury prevention, providing insights, methodologies, and accessible software implementations for sports medicine practitioners.
2023-01-01T00:00:00ZSpatio‑temporal modelling of high‑throughput phenotyping dataPérez, D.M.http://hdl.handle.net/20.500.11824/17002023-10-26T22:19:58Z2023-10-13T00:00:00ZSpatio‑temporal modelling of high‑throughput phenotyping data
Pérez, D.M.
High throughput phenotyping (HTP) platforms and devices are increasingly used to characterise growth and developmental processes for large sets of plant genotypes. This dissertation is motivated by the need to accurately estimate genetic effects over time when analysing data from such HTP experiments. The HTP data we deal with here are characterised by phenotypic traits measured multiple times in the presence of spatial and temporal noise and a hierarchical organisation at three levels (populations, genotypes within populations, and plants within genotypes). The challenge is to balance efficient statistical models and com- putational solutions to deal with the complexity and dimensionality of the experimental data. To that aim, we propose two strategies. The first proposal divides the problem into two stages. The first stage (spatial model) focuses on correcting the phenotypic data for experimental design factors and spatial variation, while the second stage (hierarchical longitudinal model) aims to estimate the evolution over time of the genetic signal. The second proposal is to face the problem simultaneously (one-stage approach). That is, mod- elling the longitudinal evolution of the genetic effect on a given phenotypic trait while accounting for the temporal and spatial effects of environmental and design factors (spatio-temporal hierarchical model). We follow the same modelling philosophy throughout our work and propose multidimensional P-spline-based hierarchical approaches. We provide the user with appealing tools that take advantage of the sparse model matrices structure to reduce computational complexity. All our codes are publicly available on the R-package statgenHTP and https://gitlab.bcamath.org/dperez/htp_one_stage_approach. We illustrate the performance of our methods using spatio-temporal simulated data and data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich. In the plant breeding context, we show how to extract new time-independent phenotypes for genomic selection purposes.
2023-10-13T00:00:00ZDerivative curve estimation in longitudinal studies using P-splinesHernández, M.A.Lee, D.J.Rodríguez-Álvarez, M.X.Durbán, M.http://hdl.handle.net/20.500.11824/16992023-10-20T22:20:04Z2023-09-18T00:00:00ZDerivative curve estimation in longitudinal studies using P-splines
Hernández, M.A.; Lee, D.J.; Rodríguez-Álvarez, M.X.; Durbán, M.
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 allows the description of biological features of the individuals or finding change regions of interest. Although there are several approaches to estimate subject-specific curves and their derivatives, there are still open problems due to the complicated nature of these time course processes. In this article, we illustrate the use of P-spline models to estimate derivatives in the context of longitudinal data. We also propose a new penalty acting at the population and the subject-specific levels to address under-smoothing and boundary problems in derivative estimation. The practical performance of the proposal is evaluated through simulations, and comparisons with an alternative method are reported.
Finally, an application to longitudinal height measurements of 125 football players in a youth professional academy is presented, where the goal is to analyse their growth and maturity patterns over time.
2023-09-18T00:00:00ZA kernel-enriched order-dependent nonparametric spatio-temporal processDas, M.Bhattachrya, S.http://hdl.handle.net/20.500.11824/16962023-10-16T22:20:22Z2023-01-01T00:00:00ZA kernel-enriched order-dependent nonparametric spatio-temporal process
Das, M.; Bhattachrya, S.
Spatio-temporal processes are necessary modeling tools for various environmental, biological, and geographical problems. The underlying model is commonly considered to be parametric and to be a Gaussian process. Additionally, the covariance function is expected to be stationary and separable. This structure need not always be realistic. Moreover, attempts have been made to construct nonparametric processes of neither stationary nor separable covariance functions. Nevertheless, as we elucidate, some desirable and necessary spatio-temporal properties are not guaranteed by the existing approaches, thus, calling for further innovative ideas. In this article, using kernel convolution of order-based dependent Dirichlet process, we construct a novel spatio-temporal model. We show that this satisfies desirable properties and includes the stationary, separable, parametric processes as special cases. Our resultant posterior distribution is variable dimensional, which we attack using Transdimensional Transformation based Markov Chain Monte Carlo, which can update all the variables and change dimensions using deterministic transformations of a random variable drawn from some arbitrary density defined on relevant support. We demonstrate our model’s performance on simulated and real data sets. In all situations, the findings are highly encouraging.
2023-01-01T00:00:00Z