Sampling Student's T distribution – use of the inverse cumulative distribution function

2006 ◽  
Vol 9 (4) ◽  
pp. 37-73 ◽  
Author(s):  
William T. Shaw
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Cumulative Distribution ◽  
Student’S T Distribution ◽  
Student’S T ◽  
Student's T Distribution ◽  
T Distribution ◽  
Inverse Cumulative Distribution Function

scholarly journals Transmuted Probability Distributions: A Review

2020 ◽  
pp. 83-94
Author(s):  
Md. Mahabubur Rahman ◽  
Bander Al-Zahrani ◽  
Saman Hanif Shahbaz ◽  
Muhammad Qaiser Shahbaz
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Probability Distributions ◽  
Quantile Function ◽  
Cumulative Distribution ◽  
Functional Composition ◽  
Inverse Cumulative Distribution Function ◽  
Family Of Distributions

Transmutation is the functional composition of the cumulative distribution function (cdf) of one distribution with the inverse cumulative distribution function (quantile function) of another. Shaw and Buckley(2007), first apply this concept and introduced quadratic transmuted family of distributions. In this article, we have presented a review about the transmuted families of distributions. We have also listed the transmuted distributions, available in the literature along with some concluding remarks.

scholarly journals Application of Generalized Student’s T-Distribution In Modeling The Distribution of Empirical Return Rates on Selected Stock Exchange Indexes

2014 ◽  
Vol 13 (2) ◽  
pp. 37-48
Author(s):  
Jan Purczyńskiz ◽  
Kamila Bednarz-Okrzyńska
Keyword(s):  
Stock Exchange ◽  
Likelihood Method ◽  
Return Rates ◽  
Student’S T Distribution ◽  
Warsaw Stock Exchange ◽  
Distribution Parameters ◽  
Student’S T ◽  
Logarithmic Moments ◽  
Student's T Distribution ◽  
T Distribution

Abstract This paper examines the application of the so called generalized Student’s t-distribution in modeling the distribution of empirical return rates on selected Warsaw stock exchange indexes. It deals with distribution parameters by means of the method of logarithmic moments, the maximum likelihood method and the method of moments. Generalized Student’s t-distribution ensures better fitting to empirical data than the classical Student’s t-distribution.

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