Judul: How Robust Are Robust Regression Methods Really With Respect To Outliers and Influential Points?
Abstrak: Since the first time Least Squares Regression was developed, many researchers use this method to predict and describe the relationship between explanatory variable and response variable. The basic concept of classical least squares regression is minimizing the sum of the squared residuals. This concept has weaknesses whenever there are outliers and/or influential points. So many researchers have produced more robust versions of this estimator. Huber M Regression and Least Median of Square methods are some robust methods. In order to know when we have to use robust methods, this paper attempts to study robustness of M Regression and Least Median squares (LMS) toward extreme outliers and influential points with regard to accuracy of the estimator utility data simulation. This research is focused on simple linear regression with four data cases. The cases are data sets with influential points, position of outliers, proportion of outliers, and combination of influential point and outlier. It seems that Huber M Regression call handle influential points. and outliers in small proportions while Least Median Square is better to be used against outliers in large proportions. Parameter bias, R square and, standard error were used to compare the methods.
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Judul: Perbandingan metode regresi kuadrat terkecil dengan metode kekar
Abstrak: Ordinary least squares method is generally used to estimate parameter of a linear model. However, ordinary least squares is vulnerable against outliers because this method treats every element of data with equal weight. To overcome this problem, several studies have been done to find methods that produce more robust estimator. The robust methods being studied in this paper are weighted least squares, least absolute deviations, least median squares, and least trimmed squares methods. The comparison among those methods indicates that the method of least absolute deviation and weighted least squares are more resistant in case of the presence of outliers in the dependent variable. Meanwhile, least median squares and least trimmed squares methods are resistant to the presence of outliers both in the dependent and independent variable.
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Judul: Sintesis 1-Kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai Prekursor Asam (S)-(+)-(6-Metoksi-2-naftil)propanoat
Abstrak: Senyawa 1-kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai prekursor asam (S)-(+)-(6-metoksi-2-naftil)propanoat disintesis dari bahan awal 2-metoksinaftalena. Tahapan sintesis diawali dengan klorinasi menggunakan 2 jenis pereaksi klorinasi, yakni isosianurat terklorinasi dan NaOCl. Klorinasi dengan isosianurat terklorinasi berhasil menyintesis 1-kloro-2-metoksinaftalena sebagai produk monoklorinasi melalui pencirian dengan kromatograf gas-spektrometer massa (GC-MS). Nisbah konversi produk paling efisien diperoleh pada laju penambahan 2.5 mmol/jam. Sebaliknya, klorinasi dengan NaOCl menghasilkan produk triklorinasi. 1-Kloro-2-metoksinaftalena selanjutnya diasilasi dengan propanoil klorida, tetapi analisis dengan GC menunjukkan bahwa produk asilasi tidak terbentuk. Sistem bebas-air diperlukan untuk keberhasilan proses asilasi tersebut.
Keyword: isosianurat terklorinasi, NaOCl, nisbah konversi