Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Mizera, Ivan"'
Functional data such as curves and surfaces have become more and more common with modern technological advancements. The use of functional predictors remains challenging due to its inherent infinite-dimensionality. The common practice is to project f
Externí odkaz:
http://arxiv.org/abs/1709.02069
In this manuscript, we study quantile regression in partial functional linear model where response is scalar and predictors include both scalars and multiple functions. Wavelet basis are adopted to better approximate functional slopes while effective
Externí odkaz:
http://arxiv.org/abs/1706.02353
We propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression (SIMPQR) algorithm to efficiently extract partial quantile regressi
Externí odkaz:
http://arxiv.org/abs/1511.00632
Publikováno v:
In Computational Statistics and Data Analysis August 2019 136:12-29
Autor:
Koenker, Roger, Mizera, Ivan
Publikováno v:
Annals of Statistics 2010, Vol. 38, No. 5, 2998-3027
Maximum likelihood estimation of a log-concave probability density is formulated as a convex optimization problem and shown to have an equivalent dual formulation as a constrained maximum Shannon entropy problem. Closely related maximum Renyi entropy
Externí odkaz:
http://arxiv.org/abs/1007.4013
Autor:
Kong, Linglong, Mizera, Ivan
Publikováno v:
Annals of Statistics 2010, Vol. 38, No. 2, 685-693
Discussion of "Multivariate quantiles and multiple-output regression quantiles: From $L_1$ optimization to halfspace depth" by M. Hallin, D. Paindaveine and M. Siman [arXiv:1002.4486]
Comment: Published in at http://dx.doi.org/10.1214/09-AOS723C
Comment: Published in at http://dx.doi.org/10.1214/09-AOS723C
Externí odkaz:
http://arxiv.org/abs/1002.4509
Autor:
Kong, Linglong, Mizera, Ivan
Publikováno v:
Statsitica Sinica, 2012, Vol. 22, No. 4. 1589-1610
The use of quantiles to obtain insights about multivariate data is addressed. It is argued that incisive insights can be obtained by considering directional quantiles, the quantiles of projections. Directional quantile envelopes are proposed as a way
Externí odkaz:
http://arxiv.org/abs/0805.0056
Autor:
Koenker, Roger, Mizera, Ivan
Publikováno v:
Statistical Science, 2018 Nov 01. 33(4), 510-526.
Externí odkaz:
https://www.jstor.org/stable/26771017
Autor:
Koenker, Roger, Mizera, Ivan
Publikováno v:
Journal of the Royal Statistical Society. Series B (Statistical Methodology), 2004 Jan 01. 66(1), 145-163.
Externí odkaz:
https://www.jstor.org/stable/3647632
Autor:
Mizera, Ivan
Publikováno v:
The Annals of Statistics, 2002 Dec 01. 30(6), 1681-1736.
Externí odkaz:
https://www.jstor.org/stable/1558737