Ansgar Steland

Orcid: 0000-0001-9395-7458

According to our database1, Ansgar Steland authored at least 15 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices.
J. Multivar. Anal., 2024

Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
On Extreme Value Asymptotics of Projected Sample Covariances in High Dimensions with Applications in Finance and Convolutional Networks.
CoRR, 2023

2022
Is there a role for statistics in artificial intelligence?
Adv. Data Anal. Classif., 2022

2021
Segmentation of photovoltaic module cells in uncalibrated electroluminescence images.
Mach. Vis. Appl., 2021

Cross-Validation and Uncertainty Determination for Randomized Neural Networks with Applications to Mobile Sensors.
CoRR, 2021

2020
Testing and estimating change-points in the covariance matrix of a high-dimensional time series.
J. Multivar. Anal., 2020

Extreme Learning and Regression for Objects Moving in Non-Stationary Spatial Environments.
CoRR, 2020

2019
Automatic processing and solar cell detection in photovoltaic electroluminescence images.
Integr. Comput. Aided Eng., 2019

2018
On Convergence of Moments for Approximating Processes and Applications to Surrogate Models.
CoRR, 2018

2013
Nonparametric Sequential Signal Change Detection Under Dependent Noise.
IEEE Trans. Inf. Theory, 2013

2012
Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction.
Commun. Stat. Simul. Comput., 2012

New approaches to nonparametric density estimation and selection of smoothing parameters.
Comput. Stat. Data Anal., 2012

2010
Nonparametric sequential change-point detection by a vertically trimmed box method.
IEEE Trans. Inf. Theory, 2010

2008
Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted Regression.
Int. J. Appl. Math. Comput. Sci., 2008


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