Judul: Kajian terhadap Penduga Komponen Utama Dua Parameter dan Penduga Regresi Komponen Utama dalam Multikolinieritas
Abstrak: Multicollinearity is one of linear multiple regression problems. The existence of multicollinearity causes the least squares method becomes no longer the best estimator. To overcome this problem, many new biased estimator have been proposed, one of them is Principal Component Regression (PCR) estimator. Principal Component Two Parameter (PCTP) filed as a superior estimator than the PCR estimator (Yang and Chang 2012). This study aimed to examine the PCTP estimator and PCR estimator with the distinguishing criteria to be studied areMean Square Error (MSE) and relative bias of parameter prediction with a Monte Carlo simulation method and also assess the comparative value of MSE( y􀷜������) PCTP estimators and PCR estimators based on rill data. Simulation results show that the MSE of PCTP similar to the MSE of PCR estimator. Based on evaluation, bias value of PCTP estimator is bigger than the PCR. The rill data evaluation conformance to simulations that have been carried out.
Keyword: Bogor Agricultural University (IPB), Principal Component Two Parameter, Multicollinearity, Multiple Regression Analysis

Judul: Which one use to overcome multicollinearity ? Ridge regression or principal component regression
Abstrak: In multiple linear regression the effect of nonorthogonality of the prediction vectors is to pull the least squares estimates of the regression coefficient away from the true coefficient that one is trying to estimate. Separating the effects of each factor of the regressors has been found to be difficult because of high correlation among independent variables. Although the multicollinearity problem has been recognized by many ways, ordinary least squares is still the main technique used in many fields, but the estimation of regression coefficient can present problems when the data vectors for the predictors are not orthogonal, or in this case are multicollinear. In using ordinary least squares, the regression coefficient may be unstable, too large in absolute value, and incorrect with respect to sign. Introduced biased estimation refer to unbiased one that measure the average closeness to the parameter being estimated corresponding to its mean square error (MSE). Ridge regression and principal component can produce better estimates by its own way than those given by ordinary least squares. It may well be preferred estimator since it will have larger probability of being close to true parameter value. Two of the biased regression methods, ridge regression and principal component regression will be applied into this research and discuss briefly for each of its properties. These methods have its particular idiosyncrasy. The biased regression methods attack the collinearity problem by computationally suppressing the effects of the collinearity. Ridge regression does this correctly by modifying the correlation matrix adding a biased constant driving through nonsingular matrix. Principal component regression attacks the problem by regressing Y on the important principal component and then parceling out the effect of the principal component variables to the original variables. Finally translated back the obtained variables into original variables yield the same sign and nearly the same coefficients for both methods.
Keyword: Regresssion analysis

Judul: Embriogenesis ikan buta (Astyanax fasciatus)
Abstrak: Ikan buta Astyanax fasciatus adalah spesies ikan unik dari famili Characidae yang menempati perairan dalam gua di Wilayah La Cueva Chica-San Luis Potasi-Mexico. Ikan ini memiliki mata yang tereduksi (Blind Cavefish) dan warna kulit merah muda, pucat serta mengkilap.
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