predictors in the full model . There are 23 -1 = 7 possible subsets for these data. Regression models with 1 variable are X1,X2 and X3 , with two variables X1X2, X1X3 and X2X3 and with three variables, X1X2X3. The results for the model selection criterion for each of the subset with classical method using OLS for both original and clean data and Robust MAD using LTS for original data are shown below: Variable Original data with n=21 Clean data with n=17 r2 adjr2 Cp AIC r2 adjr2 Cp AIC X1 0.846 0.838
Ordinary Least Square (OLS) test to measure the relationship of entire variables. The test is to find the function which most closely approximates the data. Thus, in general terms, it is an approach to fitting a model to the observed data. The details information regarding the variables is shown in table 4.1 and table 4.2 shows the least square test that measures all the variables. Variables Description LGP Log Gold Price (MYR/oz) LCPO Log Crude Oil Price (MYR/barrel) LEX Log Exchange Rate (MYR/USD) LIR
2.8. Multiscale Principal Component Analysis Multiscale PCA (MSPCA) combines the capability of PCA to extract the cross-correlation between the variables and wavelets to divide deterministic features from stochastic processes and approximately de-correlate the autocorrelation among the measurements. Figure 2.3 illustrates the MSPCA procedures. Figure 2.3. shows the MSPCA procedures. For combining the profit of PCA and wavelets, the capacity for each variable are decomposed to its wavelet coefficients
1.Introduction: In developing countries the major driver of economic growth are financial institutions, which are interlinked through innovation in response to the forces of globalization and technology. Rigorous risk management efforts are made to strengthen the financial bodies and economy. The three possible channel of financial stress spread from one financial institution to the remainder of financial organization are: other party vulnerability, capital markets linkages, and investor confidence