Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Adityanand Guntuboyina"'
Publikováno v:
IEEE Transactions on Information Theory. 68:1851-1885
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a90de091db72ca9f2d6734fc650e9162
https://doi.org/10.1109/tit.2021.3130717
https://doi.org/10.1109/tit.2021.3130717
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
Publikováno v:
Ann. Statist. 48, no. 1 (2020), 205-229
We study trend filtering, a relatively recent method for univariate nonparametric regression. For a given positive integer $r$, the $r$-th order trend filtering estimator is defined as the minimizer of the sum of squared errors when we constrain (or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a343bbce34f2cd35d84dac9ae9fc37c
https://doi.org/10.1214/18-aos1799
https://doi.org/10.1214/18-aos1799
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
We consider the problem of nonparametric regression when the covariate is $d$-dimensional, where $d \geq 1$. In this paper we introduce and study two nonparametric least squares estimators (LSEs) in this setting---the entirely monotonic LSE and the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef3542a73cc2f42b4d41573134e96ddc
http://arxiv.org/abs/1903.01395
http://arxiv.org/abs/1903.01395
Publikováno v:
Electron. J. Statist. 13, no. 2 (2019), 3243-3253
It is well known that the isotonic least squares estimator is characterized as the derivative of the greatest convex minorant of a random walk. Provided the walk has exchangeable increments, we prove that the slopes of the greatest convex minorant ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ef341d9319575b945034ef911579172
http://arxiv.org/abs/1812.04249
http://arxiv.org/abs/1812.04249
Publikováno v:
Statist. Sci. 33, no. 4 (2018), 568-594
We consider the problem of nonparametric regression under shape constraints. The main examples include isotonic regression (with respect to any partial order), unimodal/convex regression, additive shape-restricted regression, and constrained single i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7e23052cd648476d5c26bcec2fd7d10
https://doi.org/10.1214/18-sts665
https://doi.org/10.1214/18-sts665
Publikováno v:
Bernoulli 24, no. 2 (2018), 1072-1100
We consider the problem of estimating an unknown $n_{1}\times n_{2}$ matrix $\mathbf{\theta}^{*}$ from noisy observations under the constraint that $\mathbf{\theta}^{*}$ is nondecreasing in both rows and columns. We consider the least squares estimat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a14e3ace88d086ad3a92b7630cb2c93
https://projecteuclid.org/euclid.bj/1505980890
https://projecteuclid.org/euclid.bj/1505980890
In this paper, we study a generalization of the two-groups model in the presence of covariates --- a problem that has recently received much attention in the statistical literature due to its applicability in multiple hypotheses testing problems. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2fc39f56820f6098837a9fe5fb5891b
Autor:
Adityanand Guntuboyina
Publikováno v:
Constructive Approximation. 43:135-151
We prove bounds for the covering numbers of classes of convex functions and convex sets in Euclidean space. Previous results require the underlying convex functions or sets to be uniformly bounded. We relax this assumption and replace it with weaker
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7399c0f5581c70b3c3009777c1e937eb
https://doi.org/10.1007/s00365-015-9279-1
https://doi.org/10.1007/s00365-015-9279-1