Kelter, Riko
3 results
Now showing 1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , bayest: an R-package for effect-size targeted Bayesian two-sample t-tests(2020)Typical situations in research include the comparison of two groups regarding a metric variable, in which case usually the two-sample t-test is applied. While common frequentist two-sample t-tests focus on the difference of means of both groups via a p-value, the quantity of interest in applied research most often is the effect size. Existing Bayesian alternatives of the two-sample t-test replace frequentist significance thresholds like the p-value with the Bayes factor, taking the same testing stance. The R package bayest implements a Markov-Chain-Monte-Carlo algorithm to conduct a Bayesian two-sample t-test which estimates the effect size between two groups, while also providing detailed visualization and analysis of all parameters of interest. Because of its focus on the ease of use and interpretability, clinicians and other users can run this t-test within a few lines of code and find out if differences between two groups are scientifically meaningful, instead of significant.Source Type:Article534 181 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , The Bayesian simulation study (BASIS) framework for simulation studies in statistical and methodological research(2023)Statistical simulation studies are becoming increasingly popular to demonstrate the performance or superiority of new computational procedures and algorithms. Despite this status quo, previous surveys of the literature have shown that the reporting of statistical simulation studies often lacks relevant information and structure. The latter applies in particular to Bayesian simulation studies, and in this paper the Bayesian simulation study framework (BASIS) is presented as a step towards improving the situation. The BASIS framework provides a structured skeleton for planning, coding, executing, analyzing, and reporting Bayesian simulation studies in biometrical research and computational statistics. It encompasses various features of previous proposals and recommendations in the methodological literature and aims to promote neutral comparison studies in statistical research. Computational aspects covered in the BASIS include algorithmic choices, Markov–chain‐Monte‐Carlo convergence diagnostics, sensitivity analyses, and Monte Carlo standard error calculations for Bayesian simulation studies. Although the BASIS framework focuses primarily on methodological research, it also provides useful guidance for researchers who rely on the results of Bayesian simulation studies or analyses, as current state‐of‐the‐art guidelines for Bayesian analyses are incorporated into the BASIS.Source Type:Article1 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , The evolution of statistical hypothesis testing(2021)Statistical hypothesis testing is a central method for the judgement of empirical findings in the medical, social and natural sciences. In recent years, the ongoing problems with null hypothesis significance testing and p-values have shown that the underlying paradigm for quantifying statistical evidence about a research hypothesis is highly problematic, and the situation has been termed a replication crisis. In this thesis, the evolution of statistical hypothesis testing is reconstructed, and it is shown that various of the recently observed problems with the reproducibility of research can be attributed to the underlying statistical theory of widely used inferential statistical methods. In the first part, the development of an inconsistent hybrid approach to statistical hypothesis testing which emerged out of Fisher’s theory of significance tests, p-values and the Neyman-Pearson theory is analyzed. In part two, the evolution of Bayesian approaches to hypothesis testing is detailed with a focus on the Bayes factor. Part three discusses the development of modern Markov-Chain-Monte-Carlo algorithms and their impact on Bayesian hypothesis testing. Part four then provides an axiomatic analysis of the concept of statistical evidence in the context of statistical hypothesis testing and it is shown that various substantial problems which were observed in the replication crisis can be attributed to purely axiomatic inconsistencies and conflicts with the likelihood principle. Based on the axiomatic analysis, it is shown that robust Bayesian methods, in particular robust Bayesian hypothesis tests provide a solution to some substantial problems with the reproducibility of research. Bayesian statistical solutions to the replication crisis are provided in the fifth part with a focus on widely used Bayesian statistical models in the biomedical sciences. New results demonstrate that the implicit error control of Bayesian hypothesis tests is comparable to frequentist tests based on p-values, and that a variety of Bayesian evidence measures attains reasonable type I error control and power in practice. Also, a shift towards the Hodges-Lehmann paradigm which advocates testing small interval instead of point null hypotheses is explored, and new theoretical results show that such a shift may be an appealing additional step towards increasing the reproducibility of science which has not received enough attention in the discussion about the validity of statistical hypotheses and the reproducibility of scientific research.Source Type:Doctoral Thesis2 7
