TY  - JOUR
ID  - epos1531
UR  - http://dx.doi.org/10.1080/10485259608832667
IS  - 2-3
A1  - Hart, Jeffrey
N2  - Nonparametric function estimation based upon time-dependent data is a challenging problem to both the data analyst and the theoretician. This paper serves as an introduction to the problem and discusses some of the approaches that have been proposed for smoothing autocorrelated data. A principal theme will be accounting for correlation in the data driven choice of a function estimator's smoothing parameter. Data-driven smoothing is considered in various settings including probability density estimation, repeated measures data, and time series trend estimation. Both applications and theoretical issues are addressed, and some open problems will be discussed.
VL  - 6
TI  - Some automated methods of smoothing time-dependent data
AV  - none
EP  - 142
Y1  - 1996///
PB  - American Statistical Association
JF  - Journal of Nonparametric Statistics
KW  - Kernel estimators
KW  -  mean integrated squared error
KW  -  cross-validation
KW  -  plug-in rules
KW  -  autoregression
KW  -  transition densities
KW  -  block wise cross-validation
KW  -  prequential analysis
KW  -  time series cross-validation
SN  - 1048-5252
SP  - 115
ER  -