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Found 11 papers, 6 papers with code
Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models
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.
Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications to ion channels
We propose and investigate a hidden Markov model (HMM) for the analysis of dependent, aggregated, superimposed two-state signal recordings.
Methodology
The ultrametric Gromov-Wasserstein distance
In this paper, we investigate compact ultrametric measure spaces which form a subset $\mathcal{U}^w$ of the collection of all metric measure spaces $\mathcal{M}^w$.
Metric Geometry Populations and Evolution
Randomised Wasserstein Barycenter Computation: Resampling with Statistical Guarantees
We propose a hybrid resampling method to approximate finitely supported Wasserstein barycenters on large-scale datasets, which can be combined with any exact solver.
Computation Methodology 62G99, 65C60, 62P10, 90C08
Variational multiscale nonparametric regression: Algorithms
Many modern statistically efficient methods come with tremendous computational challenges, often leading to large scale optimization problems.
Computation Optimization and Control 62G05, 68U10
Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries
Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data.
Multiscale quantile segmentation
We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments.
Methodology 62G08, 62G15, 62G30, 62G35, 90C39
Frame-constrained Total Variation Regularization for White Noise Regression
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.
Statistics Theory Statistics Theory 62G05, 62M40, 62G20
Minimax estimation in linear models with unknown design over finite alphabets
To this end we quantify in the noiseless case, that is, Z = 0, the perturbation range of Y in order to obtain stable recovery of F and W. Based on this, we derive an iterative Lloyd's type estimation procedure that attains minimax estimation rates for W and F for Gaussian error matrix Z.
Statistics Theory Statistics Theory Primary 62F12, 62H30, Secondary 62F30, 62J05
The Essential Histogram
We define the essential histogram as the histogram in the confidence set with the fewest bins.
Statistics Theory Methodology Statistics Theory 62G10, 62H30
FDR-Control in Multiscale Change-point Segmentation
In this paper, we propose a multiscale segmentation method, FDRSeg, which controls the false discovery rate (FDR) in the sense that the number of false jumps is bounded linearly by the number of true jumps.
Statistics Theory Statistics Theory
