ESTIMASI NONLINEAR LEAST TRIMMED SQUARES (NLTS) PADA MODEL REGRESI NONLINIER YANG DIKENAI OUTLIER
ABSTRACT: Constant Elasticity
of Substitution (CES) production function is the intrinsic nonlinear regression
models that are often used to estimate the data in an industry. Intrinsic
nonlinear regression model is a kind of nonlinear regression that can not be
linearized, so as to estimate the beta parameters nonlinear statistical model
used was Nonlinear Least Squares (NLS) using a first order taylor series
approach used in the Gauss Newton iteration. One of the problems often
encountered in the analysis of data is an outlier, the presence of outliers in
the data analysis greatly influence the results of the analysis so it becomes
less valid and the estimation become biased. One method that is resistant to
outliers regression is a method of Nonlinear Least Trimmed Squares. This
research aims to determine the characteristics of parameter CES production
function which contains outlier. The result shows that parameter of the
production function CES which contains outliers are bias, inconsistent. So the
CES production function which does not contain outliers better than the are
contains outliers.
KEYWORDS: Nonlinear
Statistical Model; Parameter Estimation; Constant Elasticity of Substitution
(CES) production function; Outliers; Nonlinear Least Trimmed Square (NLTS) Method,
Gauss Newton Iteration
Penulis: Nur Laili Arofah, Sri
Harini
Kode Jurnal: jpmatematikadd151014
