Li, Housen

Authors Mösching A, Li H, Munk A Journal ArXiv Citation arXiv:2305.18578. Abstract 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

Authors Kovács S, Li H, Bühlmann P, Munk A Journal Biometrika Citation Biometrika, 2022, asac052. Abstract We propose seeded binary segmentation for large scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of

Authors Haltmeier M, Li H, Munk A Journal Annual Review of Statistics and Its Application Citation Annu. Rev. Stat. Appl. 2022.9:343-372. Abstract We present a unifying view on various statistical estimation techniques including penalization, variational, and thresholding methods. These estimators are analyzed in the context of statistical linear inverse problems

Authors del Álamo M, Li H, Munk A Journal The Annals of Statistics Citation Ann. Statist. 49(3): 1318-1346 (June 2021). Abstract Despite the popularity and practical success of total variation (TV) regularization for function estimation, surprisingly little is known about its theoretical performance in a statistical setting. While TV regularization

Authors Pelizzola M, Behr M, Li H, Munk A, Futschik A Journal Nature Computational Science Citation Nat Comput Sci 1, 262–271 (2021). Abstract Because haplotype information is of widespread interest in biomedical applications, effort has been put into their reconstruction. Here, we propose an efficient method, called haploSep, that is

Authors del Alamo M, Li H, Munk A, Werner F Journal Algorithms Citation Algorithms 2020, 13(11), 296. Abstract 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 nonparametric regression

Authors Kovács S, Li H, Bühlmann P Journal Journal of the Korean Statistical Society Citation J. Korean Stat. Soc. (2020). Abstract In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives. Furthermore, we investigate a

Authors Li H, Munk A, Sieling H, Walther G Journal Biometrika Citation Biometrika (2020), 107, 2, pp. 347–364 Abstract The histogram is widely used as a simple, exploratory way of displaying data, but it is usually not clear how to choose the number and size of the bins. We construct

Authors Li H, Schwab J, Antholzer S, Haltmeier M Journal Inverse Problems Citation Inverse Problems 36(2020) 065005 (23pp). Abstract Recovering a function or high-dimensional parameter vector from indirect measurements is a central task in various scientific areas. Several methods for solving such inverse problems are well developed and well understood.

Authors Li H, Guo Q, Munk A Journal Electronic Journal of Statistics Citation Electron. J. Statist. Volume 13, Number 2 (2019), 3254-3296. Abstract Modern multiscale type segmentation methods are known to detect multiple change-points with high statistical accuracy, while allowing for fast computation. Underpinning (minimax) estimation theory has been developed