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pro vyhledávání: '"Song, Hyebin"'
Weighted shape-constrained estimation for the autocovariance sequence from a reversible Markov chain
Autor:
Song, Hyebin, Berg, Stephen
We present a novel weighted $\ell_2$ projection method for estimating autocovariance sequences and spectral density functions from reversible Markov chains. Berg and Song (2023) introduced a least-squares shape-constrained estimation approach for the
Externí odkaz:
http://arxiv.org/abs/2408.03024
Autor:
Song, Hyebin, Berg, Stephen
Markov chain Monte Carlo (MCMC) is a commonly used method for approximating expectations with respect to probability distributions. Uncertainty assessment for MCMC estimators is essential in practical applications. Moreover, for multivariate function
Externí odkaz:
http://arxiv.org/abs/2310.06330
Efficient shape-constrained inference for the autocovariance sequence from a reversible Markov chain
Autor:
Berg, Stephen, Song, Hyebin
In this paper, we study the problem of estimating the autocovariance sequence resulting from a reversible Markov chain. A motivating application for studying this problem is the estimation of the asymptotic variance in central limit theorems for Mark
Externí odkaz:
http://arxiv.org/abs/2207.12705
Datacenters execute large computational jobs, which are composed of smaller tasks. A job completes when all its tasks finish, so stragglers -- rare, yet extremely slow tasks -- are a major impediment to datacenter performance. Accurately predicting s
Externí odkaz:
http://arxiv.org/abs/2203.08339
Publikováno v:
In Journal of Molecular Biology 15 March 2024 436(6)
We consider a high-dimensional monotone single index model (hdSIM), which is a semiparametric extension of a high-dimensional generalize linear model (hdGLM), where the link function is unknown, but constrained with monotone and non-decreasing shape.
Externí odkaz:
http://arxiv.org/abs/2105.07587
In a variety of settings, limitations of sensing technologies or other sampling mechanisms result in missing labels, where the likelihood of a missing label in the training set is an unknown function of the data. For example, satellites used to detec
Externí odkaz:
http://arxiv.org/abs/2103.13555
We study the problem of estimation and testing in logistic regression with class-conditional noise in the observed labels, which has an important implication in the Positive-Unlabeled (PU) learning setting. With the key observation that the label noi
Externí odkaz:
http://arxiv.org/abs/1910.02348
We study the bias of the isotonic regression estimator. While there is extensive work characterizing the mean squared error of the isotonic regression estimator, relatively little is known about the bias. In this paper, we provide a sharp characteriz
Externí odkaz:
http://arxiv.org/abs/1908.04462
Autor:
Song, Hyebin1 (AUTHOR), Berg, Stephen1 (AUTHOR) sqb6128@psu.edu
Publikováno v:
Journal of Computational & Graphical Statistics. Sep2024, p1-13. 13p. 3 Illustrations.