TY - GEN
T1 - Shortest confidence interval of parameter semi parametric regression model using spline truncated for longitudinal data
AU - Budiantara, I. Nyoman
AU - Ratnasari, Vita
AU - Permatasari, Erma Oktania
AU - Prawanti, Dasty Dewi
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/12/18
Y1 - 2019/12/18
N2 - Regression analysis is one method in statistics that used to know the pattern of functional relationships between response variables and predictor variables. Combination of parametric and nonparametric regression is semi parametric regression. The most popular estimator for nonparametric or semi parametric regression is spline truncated estimator. Problems in everyday life often using regression modeling with longitudinal data. Longitudinal data is a combination of cross-section data and time-series data. In longitudinal data, between subjects are independent of each other but between observations in the subject are dependent. One of the most important parts of statistical inference is interval estimation. Interval estimation aims to determine predictor variables that have a significant effect on the response variable. This study aims to obtain the form of interval estimation for parameters of semi parametric regression models using spline truncated estimator in longitudinal data. To solve this problem, the Weighted Least Square method and a pivotal quantity method were used for unknown population variance cases. The result of the theoretical study was that pivotal quantity distributed student-t. The shortest parameter interval estimation of semi parametric spline truncated regression model was obtained through the optimization process using the method of Lagrange.
AB - Regression analysis is one method in statistics that used to know the pattern of functional relationships between response variables and predictor variables. Combination of parametric and nonparametric regression is semi parametric regression. The most popular estimator for nonparametric or semi parametric regression is spline truncated estimator. Problems in everyday life often using regression modeling with longitudinal data. Longitudinal data is a combination of cross-section data and time-series data. In longitudinal data, between subjects are independent of each other but between observations in the subject are dependent. One of the most important parts of statistical inference is interval estimation. Interval estimation aims to determine predictor variables that have a significant effect on the response variable. This study aims to obtain the form of interval estimation for parameters of semi parametric regression models using spline truncated estimator in longitudinal data. To solve this problem, the Weighted Least Square method and a pivotal quantity method were used for unknown population variance cases. The result of the theoretical study was that pivotal quantity distributed student-t. The shortest parameter interval estimation of semi parametric spline truncated regression model was obtained through the optimization process using the method of Lagrange.
UR - http://www.scopus.com/inward/record.url?scp=85077714678&partnerID=8YFLogxK
U2 - 10.1063/1.5139746
DO - 10.1063/1.5139746
M3 - Conference contribution
AN - SCOPUS:85077714678
T3 - AIP Conference Proceedings
BT - 2nd International Conference on Science, Mathematics, Environment, and Education
A2 - Indriyanti, Nurma Yunita
A2 - Ramli, Murni
A2 - Nurhasanah, Farida
PB - American Institute of Physics Inc.
T2 - 2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019
Y2 - 26 July 2019 through 28 July 2019
ER -