In the functional response model (FRM), where a functional response is explained by scalar predictors, inference becomes challenging when the design matrix is not full-rank, leading to an ill-conditioned model (ICFRM). Widely used methods for this problem, such as $L^2$-norm-based tests (Zhang, 2013), suffer from critical flaws such as poor control of the type I error rate, which can...
Over the past two decades, the problem of selecting relevant variables in high-dimensional data analysis has gained particular importance in both statistics and machine learning. Despite substantial advances in modeling techniques and numerous algorithmic proposals, most existing approaches overlook the issue of missing observations — a phenomenon ubiquitous in real-world datasets, especially...
In modern data analysis, technological advancements frequently result in the collection of Functional Data (FD), where observations are naturally represented as smooth functions, curves, or surfaces over a continuum (e.g., time or space). Examples include daily stock prices, continuous temperature recordings, or spectroscopic measurements. Functional Data Analysis (FDA) offers a powerful...
In clinical development it is essential to identify subgroups of patients who exhibit a beneficial treatment effect, ideally before moving to confirmatory trials. Such subgroups are often defined by predictive biomarkers with corresponding cut-off values. However, data-driven selection of biomarkers or cut-offs introduces selection bias, i.e. the treatment effect within the selected subgroup...
Non-experimental data, such as electronic medical records, are often used in causal inference to estimate the effect of an exposure on an outcome of interest. However, this type of data can be affected by potential sources of bias in causal analyses. For example, these data do not come from a study design that ensures a balance of patient characteristics between exposure groups, a problem...
Meta-analyses synthesise the results of multiple independent studies to obtain more comprehensive knowledge about a research topic. When study outcomes vary, meta-regression can be used to identify potential sources of heterogeneity across studies. One complication is the typically small number of studies available. Due to this, interaction terms are often omitted in meta-regression models,...
