Results 171 to 180 of about 1,350 (213)

Sparse maximum likelihood estimation of regression models

Canadian Journal of Statistics, EarlyView.
Abstract For regression model selection and estimation, we study a small set of candidate models of maximum likelihood from which all information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) choose their models.
Min Tsao
wiley   +1 more source

Function-on-Function Partial Quantile Regression [PDF]

Journal of Agricultural, Biological, and Environmental Statistics, 2021
30 pages, 5 figures, to appear at the Journal of Agricultural, Biological and Environmental ...
Aylin Alin, Ufuk Beyaztas, Han Lin Shang   +2 more
exaly   +7 more sources

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FUNCTIONAL ADDITIVE QUANTILE REGRESSION

Statistica Sinica, 2021
Summary: We investigate a functional additive quantile regression that models the conditional quantile of a scalar response based on the nonparametric effects of a functional predictor. We model the nonparametric effects of the principal component scores as additive components, which are approximated by B-splines.
Zhang, Yingying, Lian, Heng, Li, Guodong, Zhu, Zhongyi   +3 more
openaire   +1 more source

On function-on-function linear quantile regression

Journal of Applied Statistics
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional ...
Ufuk Beyaztas, Filiz Karaman, Han Lin Shang   +2 more
exaly   +5 more sources

Nonparametric estimation of quantile density function

Computational Statistics and Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kanchan Jain, Pooja Soni
exaly   +2 more sources

Sketched quantile additive functional regression

Neurocomputing, 2021
Abstract The quantile additive functional regression (QAFR) relates the response to the integral of F ( t , X ( t ) ) over t, where F is an unknown function and X ( t ) is the predictor in the form of a curve (function). This model incorporates functional linear quantile regression as a special case and the appearance of ...
Yingying Zhang, Heng Lian
openaire   +1 more source

Pseudo-quantile functional data clustering

Journal of Multivariate Analysis, 2020
This paper studies the problem of functional data clustering. Functional data are inherently infinite-dimensional, so classical clustering technique are not appropriate for them. A new approach for functional data clustering based on the concept of an asymetric norm is considered.
Joonpyo Kim, Hee-Seok Oh
openaire   +1 more source

Estimation of quantile density function based on regression quantiles

Statistics & Probability Letters, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dodge, Yadolah, Jurečková, Jana
openaire   +2 more sources

Statistical Modelling With Quantile Functions

Technometrics, 2001
(2001). Statistical Modelling With Quantile Functions. Technometrics: Vol. 43, No. 4, pp. 488-489.
openaire   +1 more source

Simultaneous Functional Quantile Regression

Statistica Sinica
Summary: The conventional method for functional quantile regression (FQR) is to fit the regression model for each quantile of interest separately. Therefore, the slope function of the regression, as a bivariate function of time and quantile, is estimated as a univariate function of time for each fixed quantile. However, there are several limitations to
Hu, Boyi, Hu, Xixi, Liu, Hua, You, Jinhong, Cao, Jiguo   +4 more
openaire   +2 more sources