Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Young Kyung Lee"'
Publikováno v:
Journal of Nonparametric Statistics. :1-20
Publikováno v:
Statistics and Its Interface. 15:129-149
Publikováno v:
Statistics and Its Interface. 15:261-265
Publikováno v:
Bernoulli. 28
Publikováno v:
Communications for Statistical Applications and Methods. 27:149-161
In this paper we extend the functional principal component analysis for real-valued random functions to the case of Hilbert-space-valued functional random objects. For this, we introduce an autocovariance operator acting on the space of real-valued f
Publikováno v:
Computational Statistics & Data Analysis. 123:50-65
A new varying coefficient model that relates functional response to functional predictors is proposed and studied. The model accommodates the influence of the functional predictors on the time-varying coefficient functions. A powerful kernel smoothin
Autor:
Young Kyung Lee
Publikováno v:
Journal of the Korean Statistical Society. 46:339-348
In this paper we discuss the estimation of a bivariate additive model where the multivariate regression function is expressed as a sum of unknown univariate and bivariate component functions. We discuss the identifiability of the component functions
Autor:
Young Kyung Lee
Publikováno v:
Journal of the Korean Statistical Society. 45:647-653
This paper discusses the estimation of a semiparametric structured density model that is very useful in forecasting the density on a region where the data are not observed. The model has many applications in actuarial science and mortality forecastin
Autor:
Seong Jun Yang, Young Kyung Lee
Publikováno v:
Journal of the Korean Statistical Society. 43:315-321
This paper is concerned with semiparametric efficient estimation of a generalized partially linear varying coefficient model. The model studied in this paper is very flexible, accommodating various nonlinear relations between the response variable an
Publikováno v:
International Statistical Review. 83:36-64
Summary Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression m