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pro vyhledávání: '"Arlene K. H. Kim"'
Autor:
Arlene K. H. Kim
Publikováno v:
Journal of the Korean Statistical Society. 49:673-701
Minimax lower bounds determine the complexity of given statistical problems by providing fundamental limit of any procedures. This paper gives a review on various aspects of obtaining minimax lower bounds focusing on a recent development. We first in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59248d49a9a858cbcb408d9dcb4846c4
https://doi.org/10.1007/s42952-019-00027-7
https://doi.org/10.1007/s42952-019-00027-7
Autor:
Hyunwoo Chung, Arlene K. H. Kim
Publikováno v:
Stat. 10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5eef9d8512ae9851c6d52fe8d8f3688
https://doi.org/10.1002/sta4.384
https://doi.org/10.1002/sta4.384
Autor:
Arlene K. H. Kim
Publikováno v:
The Korean Data Analysis Society. 21:673-686
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bec55641ca39043cbe5e534178f40051
https://doi.org/10.37727/jkdas.2019.21.2.673
https://doi.org/10.37727/jkdas.2019.21.2.673
Autor:
Arlene K. H. Kim, Kwon So Young
Publikováno v:
The Korean Data Analysis Society. 20:2853-2864
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02ed12100b327d1b5e2793b02303493f
https://doi.org/10.37727/jkdas.2018.20.6.2853
https://doi.org/10.37727/jkdas.2018.20.6.2853
Publikováno v:
Ann. Statist. 49, no. 1 (2021), 129-153
We study the adaptation properties of the multivariate log-concave maximum likelihood estimator over three subclasses of log-concave densities. The first consists of densities with polyhedral support whose logarithms are piecewise affine. The complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::864c9b7690fcfdf4479672e7f09fbdb2
https://www.repository.cam.ac.uk/handle/1810/301217
https://www.repository.cam.ac.uk/handle/1810/301217
We prove minimax bounds for estimating Gaussian location mixtures on $\mathbb{R}^d$ under the squared $L^2$ and the squared Hellinger loss functions. Under the squared $L^2$ loss, we prove that the minimax rate is upper and lower bounded by a constan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6e98eb646185130c43b277b0ee7d042
Publikováno v:
Computational Statistics & Data Analysis. 164:107306
Double censoring often occurs in biomedical research, such as HIV/AIDS clinical trials, when an outcome of interest is subject to both left censoring and right censoring. It can also be seen as a mixture of exact and current status data and has long
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45b7612d348536dc41316d8e55fe356a
https://doi.org/10.1016/j.csda.2021.107306
https://doi.org/10.1016/j.csda.2021.107306
Autor:
Seung Jun Shin, Arlene K. H. Kim
Publikováno v:
Statistics & Probability Letters. 126:238-243
We propose a cumulative Kolmogorov filter to improve the fused Kolmogorov filter proposed by Mai and Zou (2015) via cumulative slicing. We establish an improved asymptotic result under relaxed assumptions and numerically demonstrate its enhanced fini
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2494c27fe3cfd9c9c24ff4714fd7230
https://doi.org/10.1016/j.spl.2017.03.012
https://doi.org/10.1016/j.spl.2017.03.012
Autor:
Alexandra Carpentier, Arlene K. H. Kim
We consider the problem of low rank matrix recovery in a stochastically noisy high-dimensional setting. We propose a new estimator for the low rank matrix, based on the iterative hard thresholding method, that is computationally efficient and simple.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64dc7448de5cbf879f4f7865438a2420
https://publishup.uni-potsdam.de/frontdoor/index/index/docId/52629
https://publishup.uni-potsdam.de/frontdoor/index/index/docId/52629
Publikováno v:
Ann. Statist. 46, no. 5 (2018), 2279-2306
The log-concave maximum likelihood estimator of a density on the real line based on a sample of size $n$ is known to attain the minimax optimal rate of convergence of $O(n^{-4/5})$ with respect to, e.g., squared Hellinger distance. In this paper, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::539fd952afee9c61d6ebc6052508e598