Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Li, Housen"'
Graph cuts are among the most prominent tools for clustering and classification analysis. While intensively studied from geometric and algorithmic perspectives, graph cut-based statistical inference still remains elusive to a certain extent. Distribu
Externí odkaz:
http://arxiv.org/abs/2407.15297
Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding of such cooperativity remains elusive, primarily due to limitations in measuring separat
Externí odkaz:
http://arxiv.org/abs/2403.13197
Autor:
Liu, Zhi, Li, Housen
For robust and efficient detection of change points, we introduce a novel methodology MUSCLE (multiscale quantile segmentation controlling local error) that partitions serial data into multiple segments, each sharing a common quantile. It leverages m
Externí odkaz:
http://arxiv.org/abs/2403.11356
For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed to be sparse segment-wise, which enables many existing methods to estimate the parameters locally via $\ell_1$-regularised maximum likel
Externí odkaz:
http://arxiv.org/abs/2402.06915
Due to their computational complexity, graph cuts for cluster detection and identification are used mostly in the form of convex relaxations. We propose to utilize the original graph cuts such as Ratio, Normalized or Cheeger Cut to detect clusters in
Externí odkaz:
http://arxiv.org/abs/2308.09613
Hidden Markov models (HMMs) are characterized by an unobservable (hidden) Markov chain and an observable process, which is a noisy version of the hidden chain. Decoding the original signal (i.e., hidden chain) from the noisy observations is one of th
Externí odkaz:
http://arxiv.org/abs/2305.18578
Autor:
Li, Housen, Werner, Frank
Publikováno v:
Inverse Problems, Volume 40, Number 2, 2024
We consider statistical linear inverse problems in separable Hilbert spaces and filter-based reconstruction methods of the form $\hat f_\alpha = q_\alpha \left(T^*T\right)T^*Y$, where $Y$ is the available data, $T$ the forward operator, $\left(q_\alp
Externí odkaz:
http://arxiv.org/abs/2304.10356
Publikováno v:
Annual Review of Statistics and Its Application, 9:343--372, 2022
We present a unifying view on various statistical estimation techniques including penalization, variational and thresholding methods. These estimators will be analyzed in the context of statistical linear inverse problems including nonparametric and
Externí odkaz:
http://arxiv.org/abs/2106.05828
Publikováno v:
Algorithms 2020, 13(11), 296
Many modern statistically efficient methods come with tremendous computational challenges, often leading to large-scale optimisation problems. In this work, we examine such computational issues for recently developed estimation methods in nonparametr
Externí odkaz:
http://arxiv.org/abs/2010.10660
Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching through all candidates requires $O(n)$ evaluations of the gain function for an interval with $n$ o
Externí odkaz:
http://arxiv.org/abs/2010.10194