ESTIMASI MODEL PERMUKAAN RESPON MULTIVARIAT DENGAN DATA OUTLIER; ESTIMATION OF MULTIVARIATE RESPONSE SURFACE MODEL WITH DATA OUTLIERS

Widodo, Edy, Pekik Nurwantoro

2015 | Tesis | FMIPA

The main goal of response surface methodology is to find the input variable settings that achieve the optimal compromise in the response variable. In general, there are three main steps in response surface methodology problems, namely data collection, modeling, and optimization. This study focused on how the response sur- face model formed, using the assumption that the resulting data are correct. Usually the response surface model parameters estimated by OLS. However, this method is very sensitive to outliers. Outliers can generate substantial residual and often affect the model parameter estimator. Model parameter estimators can be biased and could result in errors in the determination of the optimal point of fact, that the main purpose of response surface methodology is not reached. Meanwhile, in life, the collected data often contains some response variable and a set of independent variables. Treat each response separately and apply single response procedures can result in incorrect in- terpretations. And so we need a development model for multirespon case. Therefore, it takes a multivariate response surface models that are resistant to outliers. As an al- ternative, in this study discussed as an estimator M-estimation parameters in response surface models containing multivariate outlier.

Kata Kunci : Multivariate Response Surface Model; Outliers; M-estimation.