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Full-Text Articles in Statistics and Probability
A Comparison Of Some Confidence Intervals For Estimating The Kurtosis Parameter, Guensley Jerome
A Comparison Of Some Confidence Intervals For Estimating The Kurtosis Parameter, Guensley Jerome
FIU Electronic Theses and Dissertations
Several methods have been proposed to estimate the kurtosis of a distribution. The three common estimators are: g2, G2 and b2. This thesis addressed the performance of these estimators by comparing them under the same simulation environments and conditions. The performance of these estimators are compared through confidence intervals by determining the average width and probabilities of capturing the kurtosis parameter of a distribution. We considered and compared classical and non-parametric methods in constructing these intervals. Classical method assumes normality to construct the confidence intervals while the non-parametric methods rely on bootstrap techniques. The bootstrap …
On Some Test Statistics For Testing The Population Skewness And Kurtosis: An Empirical Study, Yawen Guo
On Some Test Statistics For Testing The Population Skewness And Kurtosis: An Empirical Study, Yawen Guo
FIU Electronic Theses and Dissertations
The purpose of this thesis is to propose some test statistics for testing the skewness and kurtosis parameters of a distribution, not limited to a normal distribution. Since a theoretical comparison is not possible, a simulation study has been conducted to compare the performance of the test statistics. We have compared both parametric methods (classical method with normality assumption) and non-parametric methods (bootstrap in Bias Corrected Standard Method, Efron’s Percentile Method, Hall’s Percentile Method and Bias Corrected Percentile Method). Our simulation results for testing the skewness parameter indicate that the power of the tests differs significantly across sample sizes, the …
Comparison Of Some Improved Estimators For Linear Regression Model Under Different Conditions, Smit Shah
Comparison Of Some Improved Estimators For Linear Regression Model Under Different Conditions, Smit Shah
FIU Electronic Theses and Dissertations
Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis of risk was made among ridge, Liu and LASSO estimators under orthonormal regression model. I found that …