Open AccessESTIMASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED MENGGUNAKAN METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE)
Author(s) -
Narita Yuri Adrianingsih,
Andrea Tri Rian Dani
Publication year - 2021
Publication title -
jambura journal of probability and statistics
Language(s) - English
Resource type - Journals
ISSN - 2722-7189
DOI - 10.34312/jjps.v2i2.10255
Subject(s) - semiparametric regression , mathematics , statistics , semiparametric model , regression analysis , spline (mechanical) , nonparametric regression , polynomial regression , parametric statistics , nonparametric statistics , econometrics , engineering , structural engineering
Regression modeling with a semiparametric approach is a combination of two approaches, namely the parametric regression approach and the nonparametric regression approach. The semiparametric regression model can be used if the response variable has a known relationship pattern with one or more of the predictor variables used, but with the other predictor variables the relationship pattern cannot be known with certainty. The purpose of this research is to examine the estimation form of the semiparametric spline truncated regression model. Suppose that random error is assumed to be independent, identical, and normally distributed with zero mean and variance , then using this assumption, we can estimate the semiparametric spline truncated regression model using the Maximum Likelihood Estimation (MLE) method. Based on the results, the estimation results of the semiparametric spline truncated regression model were obtained p=(inv(M'M)) M'y