TY - JOUR
T1 - Estimation of nonparametric regression curve using mixed estimator of multivariable truncated Spline and multivariable Kernel
AU - Ratnasari, Vita
AU - Budiantara, I. Nyoman
AU - Ratna, Madu
AU - Zain, Ismaini
N1 - Publisher Copyright:
© Research India Publications.
PY - 2016
Y1 - 2016
N2 - Data given in pairs, (t1i,…tpi,z1i,…,zqi,yi), i = 1,2,…,n which follows the nonparametric regression model multivariable predictors of additives: (Formula presented) The regression curve gr(tr), r =1,2,…,p and hs(zs), s = 1,2,…,q assumed smooth, and each approached using Spline function truncated and Kernel functions. Nonparametric regression curve estimation multivariable predictor truncated Spline and Kernel mixed obtained from optimization: (Formula presented) Truncated Spline component multivariable estimator, multivariable Kernel component, and a mixture of truncated Spline and Kernel are follow: (Formula presented) Truncated Spline components multivariable estimator, Kernel multivariable components, and mix Spline Kernel truncated and each is a biased estimator, but it is a linear estimator class under observation. Spline Estimator mixture truncated and multivariable Kernel is depend on the points of knot K and bandwidth parameters. The mix of Truncted Spline and multivariable Kernel Estimator associated with knot K optimal and optimal bandwidth parameters. Point knot and optimal bandwidth parameters derived from minimum of Generalized Cross Validation (GCV).
AB - Data given in pairs, (t1i,…tpi,z1i,…,zqi,yi), i = 1,2,…,n which follows the nonparametric regression model multivariable predictors of additives: (Formula presented) The regression curve gr(tr), r =1,2,…,p and hs(zs), s = 1,2,…,q assumed smooth, and each approached using Spline function truncated and Kernel functions. Nonparametric regression curve estimation multivariable predictor truncated Spline and Kernel mixed obtained from optimization: (Formula presented) Truncated Spline component multivariable estimator, multivariable Kernel component, and a mixture of truncated Spline and Kernel are follow: (Formula presented) Truncated Spline components multivariable estimator, Kernel multivariable components, and mix Spline Kernel truncated and each is a biased estimator, but it is a linear estimator class under observation. Spline Estimator mixture truncated and multivariable Kernel is depend on the points of knot K and bandwidth parameters. The mix of Truncted Spline and multivariable Kernel Estimator associated with knot K optimal and optimal bandwidth parameters. Point knot and optimal bandwidth parameters derived from minimum of Generalized Cross Validation (GCV).
KW - Mixed Estimator
KW - Multivariable Kernel
KW - MultivariableTruncated Spline
KW - Nonparametric Regression
UR - http://www.scopus.com/inward/record.url?scp=85019521760&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85019521760
SN - 0973-1768
VL - 12
SP - 5047
EP - 5057
JO - Global Journal of Pure and Applied Mathematics
JF - Global Journal of Pure and Applied Mathematics
IS - 6
ER -