Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Isabella Verdinelli"'
Publikováno v:
Motor Control. 20:255-265
This paper carries out a full Bayesian analysis for a data set examined in Chen & Cesari (2015). These data were collected for assessing people’s ability in evaluating short intervals of time. Chen & Cesari (2015) showed evidence of the existence o
Autor:
Isabella Verdinelli, Larry Wasserman
Publikováno v:
Electron. J. Statist. 12, no. 2 (2018), 4288-4312
Mode-based clustering methods define clusters in terms of the modes of a density estimate. The most common mode-based method is mean shift clustering which defines clusters to be the basins of attraction of the modes. Specifically, the gradient of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b691c55f1191842babae9c08c287d27d
http://arxiv.org/abs/1805.04187
http://arxiv.org/abs/1805.04187
Autor:
Isabella Verdinelli, Larry Wasserman
Publikováno v:
Electron. J. Statist. 13, no. 2 (2019), 5088-5119
We define a modified Wasserstein distance for distribution clustering which inherits many of the properties of the Wasserstein distance but which can be estimated easily and computed quickly. The modified distance is the sum of two terms. The first t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::405308835d653d29c1b0076188451954
We present a method for finding high density, low-dimensional structures in noisy point clouds. These structures are sets with zero Lebesgue measure with respect to the D-dimensional ambient space and belong to a d < D-dimensional space. We call them
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25f6b7e6139cac003bcd299797167e51
http://hdl.handle.net/11385/182622
http://hdl.handle.net/11385/182622
Publikováno v:
Journal of Statistical Planning and Inference. 137:43-56
We propose the use of the generalized fractional Bayes factor for testing fit in multinomial models. This is a non-asymptotic method that can be used to quantify the evidence for or against a sub-model. We give expressions for the generalized fractio
Summary We derive non-parametric confidence intervals for the eigenvalues of the Hessian at modes of a density estimate. This provides information about the strength and shape of modes and can also be used as a significance test. We use a data-splitt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f4ab1b1368d0cbdc10620d9ba55ba3c
http://hdl.handle.net/11573/781610
http://hdl.handle.net/11573/781610
Autor:
Isabella Verdinelli, Eric D. Miller, David A. Romero, Phil G. Campbell, Susan Finger, Lynn M. Walker, Lee E. Weiss, Cristina H. Amon
Publikováno v:
Computer-Aided Design. 37:1127-1139
This paper presents a Bayesian methodology for computer-aided experimental design of heterogeneous scaffolds for tissue engineering applications. These heterogeneous scaffolds have spatial distributions of growth factors designed to induce and direct
Publikováno v:
Journal of the American Statistical Association. 99:1002-1014
This article extends false discovery rates to random fields, for which there are uncountably many hypothesis tests. We develop a method for finding regions in the field's domain where there is a significant signal while controlling either the proport
Autor:
Isabella Verdinelli, Refik Soyer
Publikováno v:
Applied Stochastic Models in Business and Industry. 33:259-259
Publikováno v:
Ann. Statist. 42, no. 4 (2014), 1511-1545
We study the problem of estimating the ridges of a density function. Ridge estimation is an extension of mode finding and is useful for understanding the structure of a density. It can also be used to find hidden structure in point cloud data. We sho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62e3535bbaa6be4829a3e3265c5a47ae
https://projecteuclid.org/euclid.aos/1407420007
https://projecteuclid.org/euclid.aos/1407420007