Kumaraswamy-Odd Rayleigh-G Family of Distributions with Applications  

作　　者：Jamilu Yunusa Falgore Sani Ibrahim Doguwa Jamilu Yunusa Falgore;Sani Ibrahim Doguwa(Department of Statistics, Ahmadu Bello University, Zaria, Nigeria)

机构地区：[1]Department of Statistics, Ahmadu Bello University, Zaria, Nigeria

出　　处：《Open Journal of Statistics》2020年第4期719-734,共16页统计学期刊（英文）

摘　　要：We propose a new generator of continuous distributions with at least four positive parameters called the Kumaraswamy-Odd Rayleigh-G family. Some special cases were presented. The plots of the Kumaraswamy Odd Rayleigh Log-Logistic (</span><i><span style="font-family:Verdana;">KORLL</span></i><span style="font-family:Verdana;">) distribution indicate that the distribution can take many shapes depending on the parameter values. The negative skewness and kurtosis indicates that the distribution has li</span><span style="font-family:Verdana;">ghter tails than the normal distribution. The Monte Carlo simulation results indicate that the estimated biases decrease when the sample size increases. Furthermore, </span></span><span style="font-family:Verdana;">the root mean squared error estimates decay</span><span style="font-family:""><span style="font-family:Verdana;"> towards zero as the sample size increases. This part shows the consistency of the maximum likelihood estimators. From the considered analytical measures, the new </span><i><span style="font-family:Verdana;">KORLL</span></i><span style="font-family:Verdana;"> provides </span></span><span style="font-family:Verdana;">the best fit to the analysed five real data sets indicating that this new model outclasses its competitors.We propose a new generator of continuous distributions with at least four positive parameters called the Kumaraswamy-Odd Rayleigh-G family. Some special cases were presented. The plots of the Kumaraswamy Odd Rayleigh Log-Logistic (</span><i><span style="font-family:Verdana;">KORLL</span></i><span style="font-family:Verdana;">) distribution indicate that the distribution can take many shapes depending on the parameter values. The negative skewness and kurtosis indicates that the distribution has li</span><span style="font-family:Verdana;">ghter tails than the normal distribution. The Monte Carlo simulation results indicate that the estimated biases decrease when the sample size increases. Furthermore, </span></span><span style="font-family:Verdana;">the root mean squared error estimates decay</span><span style="font-family:""><span style="font-family:Verdana;"> towards zero as the sample size increases. This part shows the consistency of the maximum likelihood estimators. From the considered analytical measures, the new </span><i><span style="font-family:Verdana;">KORLL</span></i><span style="font-family:Verdana;"> provides </span></span><span style="font-family:Verdana;">the best fit to the analysed five real data sets indicating that this new model outclasses its competitors.

关 键 词：Odd Rayleigh-G Family Kumuraswamy Odd Rayleigh-G Family Kumuraswamy Odd Rayleigh-Loglogistic Distribution Monte Carlo Simulation SKEWNESS 

分 类 号：O17[理学—数学]