ESTIMASI DAN INFERENSI MODEL REGRESI SEMI-PARAMETRIK PROSES PRODUKSI

• المؤلفون الرئيسيون: , TUBAGUS PAMUNGKAS, , Prof. Dr. Sri Haryatmi, M.Sc
• التنسيق: Theses and Dissertations NonPeerReviewed
• منشور في: [Yogyakarta] : Universitas Gadjah Mada 2012
• الموضوعات: ETD
• الوصول للمادة أونلاين: https://repository.ugm.ac.id/118390/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=58337
• المؤسسة: Universitas Gadjah Mada

Multiple regression has special case in a regression analysis. In multiple regression, there is dependent variable to predict. However, when there are two or more independent variables, the best model selected will be performed based on 3 (three) methods, i.e. Quadratic Mode Estimator (QME), Symmetrically Trimmed Least Squares (STLS) and Left Truncated (LT) methods. Data utilized involved data on air pollution that were produced by 7 (seven) independent variables, involving total number of vehicles passing by, air temperature, wind speed, temperature difference, wind, active hours. Censoring on response variable in a regression model is one of the problems frequently identified in some applications. Based on the discussion and simulation, several important points are able to be concluded. In selecting semiparametric regression model, the best one is selected by using QME method. This may be identified from the least RMSE value. In partial test, there were 2 (two) significant variables on the dependent variables, i.e. variables in terms of car and wind speed, and constants, in the form of intercept. In diagnostic checking, it is concluded that normality test using Kolmogorov Smirnov Test showed that data was not normally distributed