RIDGE LEAST ABSOLUTE DEVIATION PERFORMANCE IN ADDRESSING MULTICOLLINEARITY AND DIFFERENT LEVELS OF OUTLIER SIMULTANEOUSLY

If there is multicollinearity and outliers in the data, the inference about parameter estimation in the LS method will deviate due to the inefficiency of this method in estimating. To overcome these two problems simultaneously, it can be done using robust regression, one of which is ridge least absolute deviation method. This study aims to evaluate the performance of the ridge least absolute deviation method in surmounting multicollinearity in divers sample sizes and percentage of outliers using simulation data. The Monte Carlo study was designed in a multiple regression model with multicollinearity (ρ=0.99) between variables  and  and outliers 10%, 20%, 30% on response variables with different sample sizes (n = 25, 50,75,100,200; =0, and β=1 otherwise). The existence of multicollinearity in the data is done by calculating the correlation value between the independent variables and the VIF value. Outlier detection is done by using boxplot. Parameter estimation was carried out using the RLAD and LS methods. Furthermore, a comparison of the MSE values of the two methods is carried out to see which method is better in overcoming multicollinearity and outliers. The results showed that RLAD had a lower MSE than LS. This signifies that RLAD is more precise in estimating the regression coefficients for each sample size and various outlier levels studied

Modified Lorenz Curve and Its Computation

The Lorenz curve is generally used to find out the inequality of income distribution. Mathematically a standard form of the Lorenz curve can be modified with the aim of simplicity of its symmetric analysis and calculation of the Gini coefficient that usually accompanies it. One way to modify the shape of the Lorenz curve without losing its characteristics but is simple in the analysis of geometric shapes is through a transformation (rotation). To be efficient and effective in computing and analyzing a Lorenz curve it is necessary to consider using computer software. In this article, in addition to describing the development of the concept of using transformations (rotations) of the standard Lorenz curve in an easy-to-do form, the symmetric analysis is also described by computational techniques using Mathematica® software. From the results of the application of the development of the concept of the Lorenz curve which is carried out on a data gives a simpler picture of the computational process with relatively similar computational results

Simulasi Jumlah Klaim Agregasi Berdistribusi Poisson Dengan Besar Klaim Berdistribusi Gamma dan Rayleigh

A claim is a transfer of risk from the insured to the guarantor. Claims that occur individually are called individual claims, whereas collections of individual claims are called aggregation claims in a single period of vehicle insurance. Aggregation claims consist of a pattern of the number and amount (nominal value) of individual claims, so that the model of aggregation claims is formed from each distribution of the number and amount of claims. The distribution of claims is based on the probability density function and the cumulative density function. One method that can be used to obtain a claim aggregation model is to use convolution, which is by combining the distribution of the number of claims and the distribution of the amount of claims so that the expected value can be obtained to predict the value of pure premiums. In this paper, aggregation claim modeling will be carried out with the number of claims distributed Poisson and the amount of claims distributed Gamma. As comparison, we compare it with claim amount distributed Rayleigh. By using VaR (value at risk) and MSE (Mean Square Error) indicators, the results of the analysis show that the Rayleigh distribution is better used for distributing data that has extreme values

Simulasi Jumlah Klaim Agregasi Berdistribusi Poisson dengan Besar Klaim Berdistribusi Gamma dan Rayleigh

A claim is a transfer of risk from the insured to the guarantor. Claims that occur individually are called individual claims, whereas collections of individual claims are called aggregation claims in a single period of vehicle insurance. Aggregation claims consist of a pattern of the number and amount (nominal value) of individual claims, so that the model of aggregation claims is formed from each distribution of the number and amount of claims. The distribution of claims is based on the probability density function and the cumulative density function. One method that can be used to obtain a claim aggregation model is to use convolution, which is by combining the distribution of the number of claims and the distribution of the amount of claims so that the expected value can be obtained to predict the value of pure premiums. In this paper, aggregation claim modeling will be carried out with the number of claims distributed Poisson and the amount of claims distributed Gamma. As comparison, we compare it with claim amount distributed Rayleigh. By using VaR (value at risk) and MSE (Mean Square Error) indicators, the results of the analysis show that the Rayleigh distribution is better used for distributing data that has extreme values