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Full-Text Articles in Physical Sciences and Mathematics
Estimating The Non-Existent Mean And Variance Of The F-Distribution By Simulation, Hamid Reza Kamali, Parisa Shahnazari-Shahrezaei
Estimating The Non-Existent Mean And Variance Of The F-Distribution By Simulation, Hamid Reza Kamali, Parisa Shahnazari-Shahrezaei
Journal of Modern Applied Statistical Methods
In theory, all moments of some probability distributions do not necessarily exist. In the other words, they may be infinite or undefined. One of these distributions is the F-distribution whose mean and variance have not been defined for the second degree of freedom less than 3 and 5, respectively. In some cases, a large statistical population having an F-distribution may exist and the aim is to obtain its mean and variance which are an estimation of the non-existent mean and variance of F-distribution. This article considers a large sample F-distribution to estimate its non-existent mean and variance using Simul8 simulation …
On Simulating Univariate And Multivariate Burr Type Iii And Type Xii Distributions, Todd C. Headrick, Mohan D. Pant, Yanyan Sheng
On Simulating Univariate And Multivariate Burr Type Iii And Type Xii Distributions, Todd C. Headrick, Mohan D. Pant, Yanyan Sheng
Mohan Dev Pant
This paper describes a method for simulating univariate and multivariate Burr Type III and Type XII distributions with specified correlation matrices. The methodology is based on the derivation of the parametric forms of a pdf and cdf for this family of distributions. The paper shows how shape parameters can be computed for specified values of skew and kurtosis. It is also demonstrated how to compute percentage points and other measures of central tendency such as the mode, median, and trimmed mean. Examples are provided to demonstrate how this Burr family can be used in the context of distribution fitting using …
Creation Of Synthetic Discrete Response Regression Models, Joseph Hilbe
Creation Of Synthetic Discrete Response Regression Models, Joseph Hilbe
Joseph M Hilbe
The development and use of synthetic regression models has proven to assist statisticians in better understanding bias in data, as well as how to best interpret various statistics associated with a modeling situation. In this article I present code that can be easily amended for the creation of synthetic binomial, count, and categorical response models. Parameters may be assigned to any number of predictors (which are shown as continuous, binary, or categorical), negative binomial heterogeneity parameters may be assigned, and the number of levels or cut points and values may be specified for ordered and unordered categorical response models. I …