arXiv subjects

Maximilian Maier

Publications and source records attributed to Maximilian Maier.

5 recordsLinked to original sources

arXiv2026

Why the unrestricted weighted least squares should be routinely reported in medical meta-analyses

The unrestricted weighted least squares (UWLS) meta-analysis estimator of mean effect is an alternative to the conventional random-effects model (RE). It is a weighted least squares regression estimator that can be represented as a multiplicative random-effects model. UWLS has been shown to fit medical research better than RE as measured by AIC/BIC widely across Cochrane Database of Systematic Reviews (CDSR). The independence of UWLS's mean and heterogeneity estimators provide small-sample advantages that RE does not possess. Large small-sample biases and uncertainty in RE's heterogeneity variance estimates explain most of RE's relatively poor fit along with RE's boundary problem, where RE's heterogeneity variance is estimated to be zero. We prove that UWLS almost always has superior fit at RE's boundary with uncommon exceptions. UWLS has also been found to have generally superior statistical properties: bias, MSE, and coverage relative to RE across 1,665 simulation designs compiled from four published studies authored by different teams of researchers. A recent study in this journal replicated UWLS's superior goodness of fit widely across both the CDSR and a new set of simulations. Due to reporting and interpretation errors, this recent study calls for the continued use of RE as the default meta-analysis estimator with limited applications of UWLS. We address this recent study's concerns and show why UWLS should be routinely reported in medical meta-analyses.

stat.ME↗

arXiv2026

Post-training makes large language models less human-like

Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, we introduce Psych-201, a novel dataset that enables us to measure behavioral alignment at scale. We find that post-training -- the stage that turns base models into useful assistants -- consistently reduces alignment with human behavior across model families, sizes, and objectives. Moreover, this misalignment widens in newer model generations even as base models continue to improve. Finally, we find that persona-induction -- a popular technique for eliciting human-like behavior by conditioning models on participant-specific information -- does not improve predictions at the level of individuals. Taken together, our results suggest that the very processes that are currently employed to turn LLMs into useful assistants also make them less accurate models of human behavior.

cs.CL↗

arXiv2023

Footprint of publication selection bias on meta-analyses in medicine, environmental sciences, psychology, and economics

Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in environmental sciences and psychology, whereas meta-analyses in medicine are contaminated the least. After adjusting for publication selection bias, the median probability of the presence of an effect decreased from 99.9% to 29.7% in economics, from 98.9% to 55.7% in psychology, from 99.8% to 70.7% in environmental sciences, and from 38.0% to 29.7% in medicine. The median absolute effect sizes (in terms of standardized mean differences) decreased from d = 0.20 to d = 0.07 in economics, from d = 0.37 to d = 0.26 in psychology, from d = 0.62 to d = 0.43 in environmental sciences, and from d = 0.24 to d = 0.13 in medicine.

stat.AP↗

arXiv2022

Utilizing the sensitization effect for direct laser writing in a novel photoresist based on the chitin monomer N-acetyl-D-glucosamine

The great flexibility of direct laser writing arises from the possibility to fabricate precise three-dimensional structures on very small scales as well as the broad range of applicable materials. However, there is still a vast number of promising materials which are currently inaccessible requiring the continuous development of novel photoresists. Here, a new bio-sourced resist is reported which relies on the monomeric unit of chitin, N-acetyl-D-glucosamine, expanding the existing plant-based biopolymer resists by a bio-based monomer from the animal kingdom. In addition it is shown that combined use of two photoinitiators is advantageous over the use of a single one. In our approach, the first photoinitator is a good two-photon absorber at the applied wavelength, while the second photoinitiator exhibits poor two-photon absorbtion abilities, but is better suited for crosslinking of the monomer. The first photoinitiator absorbs the light acting as a sensitizer and transfers the energy to the second initiator, which subsequently forms a radical and initializes the polymerization. This sensitization effect enables a new route to utilize reactive photointiators with a small two-photon absorption cross-section for direct laser writing without changing their chemical structure.

physics.optics↗

arXiv2022

Initiator-free photo-crosslinkable cellulose-based resists for fabricating submicron patterns via direct laser writing

Novel bifunctional cellulose diacetate derivatives were synthesized in order to achieve bio-based photoresists, which can be structured by two-photon absorption via direct laser writing (DLW) without the need to use a photoinitiator. Therefore, cellulose diacetate is functionalized with thiol moieties and olefinic or methacrylic side groups enabling thiol-conjugated crosslinking. These cellulose derivatives are also photo-crosslinkable via UV irradiation ($λ$ = 254 nm and 365 nm) without using an initiator.

physics.optics↗