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pro vyhledávání: '"Mueller, Hans-Georg"'
Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This paper addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We prop
Externí odkaz:
http://arxiv.org/abs/2407.21407
Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular tool to achieve this. Causal inference has been developed mainly for
Externí odkaz:
http://arxiv.org/abs/2406.19604
Autor:
Kundu, Poorbita, Müller, Hans-Georg
Shape-constrained functional data encompass a wide array of application fields especially in the life sciences, such as activity profiling, growth curves, healthcare and mortality. Most existing methods for general functional data analysis often igno
Externí odkaz:
http://arxiv.org/abs/2406.12817
Autor:
Zhou, Hang, Müller, Hans-Georg
We develop an inferential toolkit for analyzing object-valued responses, which correspond to data situated in general metric spaces, paired with Euclidean predictors within the conformal framework. To this end we introduce conditional profile average
Externí odkaz:
http://arxiv.org/abs/2405.00294
Autor:
Zhu, Changbo, Müller, Hans-Georg
Classical regression models do not cover non-Euclidean data that reside in a general metric space, while the current literature on non-Euclidean regression by and large has focused on scenarios where either predictors or responses are random objects,
Externí odkaz:
http://arxiv.org/abs/2312.15376
Autor:
Zhou, Hang, Müller, Hans-Georg
We develop statistical models for samples of distribution-valued stochastic processes featuring time-indexed univariate distributions, with emphasis on functional principal component analysis. The proposed model presents an intrinsic rather than tran
Externí odkaz:
http://arxiv.org/abs/2310.20088
Autor:
Zhou, Yidong, Müller, Hans-Georg
The problem of modeling the relationship between univariate distributions and one or more explanatory variables has found increasing interest. Traditional functional data methods cannot be applied directly to distributional data because of their inhe
Externí odkaz:
http://arxiv.org/abs/2308.12540
Mixed effect modeling for longitudinal data is challenging when the observed data are random objects, which are complex data taking values in a general metric space without linear structure. In such settings the classical additive error model and dis
Externí odkaz:
http://arxiv.org/abs/2307.05726
Autor:
Chen, Han, Müller, Hans-Georg
While statistical modeling of distributional data has gained increased attention, the case of multivariate distributions has been somewhat neglected despite its relevance in various applications. This is because the Wasserstein distance, commonly use
Externí odkaz:
http://arxiv.org/abs/2306.10601
Autor:
Zhou, Yidong, Müller, Hans-Georg
Sparse functional/longitudinal data have attracted widespread interest due to the prevalence of such data in social and life sciences. A prominent scenario where such data are routinely encountered are accelerated longitudinal studies, where subjects
Externí odkaz:
http://arxiv.org/abs/2306.10221