121 citations · 197 across the 18 of their papers we have counts for
23 papers
Estimation and inference in penalized function-on-function regression with dependent residual curves
Fabian Scheipl
Penalized function-on-function regression, as implemented in the R package refund, treats all grid values of all curves as independent. Residual curves are smooth, so values within…
Area-based epigraph and hypograph indices for functional outlier detection
Belen Pulido, Alba M. Franco-Pereira, Rosa E. Lillo +1
Detecting outliers in Functional Data Analysis is challenging because curves can stray from the majority in many different ways. The Modified Epigraph Index (MEI) and Modified Hypo…
DCSI -- An improved measure of cluster separability based on separation and connectedness
Jana Gauss, Fabian Scheipl, Moritz Herrmann
Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be q…
A geometric framework for outlier detection in high-dimensional data
Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework that exploits the metric structure…
Enhancing cluster analysis via topological manifold learning
Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl +1
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: t…
A geometric perspective on functional outlier detection
Moritz Herrmann, Fabian Scheipl
We consider functional outlier detection from a geometric perspective, specifically: for functional data sets drawn from a functional manifold which is defined by the data's modes…
Registration for Incomplete Non-Gaussian Functional Data
Alexander Bauer, Fabian Scheipl, Helmut Küchenhoff +1
Accounting for phase variability is a critical challenge in functional data analysis. To separate it from amplitude variation, functional data are registered, i.e., their observed…
Developing Open Source Educational Resources for Machine Learning and Data Science
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio +4
Education should not be a privilege but a common good. It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machi…
Multivariate Functional Additive Mixed Models
Alexander Volkmann, Almond Stöcker, Fabian Scheipl +1
Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary like precipitation, temperature, and wind speeds over time at a giv…
Statistical Inference for Ordinal Predictors in Generalized Linear and Additive Models with Application to Bronchopulmonary Dysplasia
Jan Gertheiss, Fabian Scheipl, Tina Lauer +1
Discrete but ordered covariates are quite common in applied statistics, and some regularized fitting procedures have been proposed for proper handling of ordinal predictors in stat…
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction
Moritz Herrmann, Fabian Scheipl
In recent years, manifold methods have moved into focus as tools for dimension reduction. Assuming that the high-dimensional data actually lie on or close to a low-dimensional nonl…
A General Machine Learning Framework for Survival Analysis
Andreas Bender, David Rügamer, Fabian Scheipl +1
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, an…
Benchmarking time series classification -- Functional data vs machine learning approaches
Florian Pfisterer, Laura Beggel, Xudong Sun +2
Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA…
A General Framework for Multivariate Functional Principal Component Analysis of Amplitude and Phase Variation
Clara Happ, Fabian Scheipl, Alice-Agnes Gabriel +1
Functional data typically contains amplitude and phase variation. In many data situations, phase variation is treated as a nuisance effect and is removed during preprocessing, alth…
pammtools: Piece-wise exponential Additive Mixed Modeling tools
Johannes Piller, Fabian Scheipl, Philipp Kopper +1
Piecewise exponential additive mixed models (PAMMs) provide a flexible framework for analyzing censored and truncated time-to-event data, bridging classical hazard-based modeling w…
Fast symmetric additive covariance smoothing
Jona Cederbaum, Fabian Scheipl, Sonja Greven
We propose a fast bivariate smoothing approach for symmetric surfaces that has a wide range of applications. We show how it can be applied to estimate the covariance function in lo…
Generalized Functional Additive Mixed Models
Fabian Scheipl, Jan Gertheiss, Sonja Greven
We propose a comprehensive framework for additive regression models for non-Gaussian functional responses, allowing for multiple (partially) nested or crossed functional random eff…
Identifiability in penalized function-on-function regression models
Fabian Scheipl, Sonja Greven
Regression models with functional responses and covariates constitute a powerful and increasingly important model class. However, regression with functional data poses well known a…
Bayesian Functional Generalized Additive Models with Sparsely Observed Covariates
Mathew W. McLean, Fabian Scheipl, Giles Hooker +2
The functional generalized additive model (FGAM) was recently proposed in McLean et al. (2013) as a more flexible alternative to the common functional linear model (FLM) for regres…
Penalized Likelihood and Bayesian Function Selection in Regression Models
Fabian Scheipl, Thomas Kneib, Ludwig Fahrmeir
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In compariso…
Functional Additive Mixed Models
Fabian Scheipl, Ana-Maria Staicu, Sonja Greven
We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects wi…
spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
Fabian Scheipl
The R package spikeSlabGAM implements Bayesian variable selection, model choice, and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and Poisson respo…
Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models
Fabian Scheipl, Ludwig Fahrmeir, Thomas Kneib
Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear…