type of a distribution function
Two distribution functions^{} $F,G:\mathbb{R}\to [0,1]$ are said to of the same type if there exist $a,b\in \mathbb{R}$ such that $G(x)=F(ax+b)$. $a$ is called the scale parameter, and $b$ the location parameter or centering parameter. Let’s write $F\stackrel{t}{=}G$ to denote that $F$ and $G$ are of the same type.
Remarks.

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Necessarily $a>0$, for otherwise at least one of $G(\mathrm{\infty})=0$ or $G(\mathrm{\infty})=1$ would be violated.

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If $G(x)=F(x+b)$, then the graph of $G$ is shifted to the right from the graph of $F$ by $b$ units, if $b>0$ and to the left if $$.

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If $G(x)=F(ax)$, then the graph of $G$ is stretched from the graph of $F$ by $a$ units if $a>1$, and compressed if $$.

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If $X$ and $Y$ are random variables^{} whose distribution functions are of the same type, say, $F$ and $G$ respectively, and related by $G(x)=F(ax+b)$, then $X$ and $aY+b$ are identically distributed, since
$$P(X\le z)=F(z)=G(\frac{zb}{a})=P(Y\le \frac{zb}{a})=P(aY+b\le z).$$ When $X$ and $aY+b$ are identically distributed, we write $X\stackrel{t}{=}Y$.

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Again, suppose $X$ and $Y$ correspond to $F$ and $G$, two distribution functions of the same type related by $G(x)=F(ax+b)$. Then it is easy to see that $$ iff $$. In fact, if the expectation exists for one, then $E[X]=aE[Y]+b$. Furthermore, $Var[X]$ is finite iff $Var[Y]$ is. And in this case, $Var[X]={a}^{2}Var[Y]$. In general, convergence of moments is a “typical” property.

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We can partition^{} the set of distribution functions into disjoint subsets of functions belonging to the same types, since the binary relation^{} $\stackrel{t}{=}$ is an equivalence relation^{}.

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By the same token, we can classify all real random variables defined on a fixed probability space^{} according to their distribution functions, so that if $X$ and $Y$ are of the same type $\tau $ iff their corresponding distribution functions $F$ and $G$ are of type $\tau $.

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Given an equivalence class^{} of distribution functions belonging to a certain type $\tau $, such that a random variable $Y$ of type $\tau $ exists with finite expectation and variance^{}, then there is one distribution function $F$ of type $\tau $ corresponding to a random variable $X$ such that $E[X]=0$ and $Var[X]=1$. $F$ is called the standard distribution function for type $\tau $. For example, the standard (cumulative) normal distribution^{} is the standard distribution function for the type consisting of all normal distribution functions.

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Within each type $\tau $, we can further classify the distribution functions: if $G(x)=F(x+b)$, then we say that $G$ and $F$ belong to the same location family under $\tau $; and if $G(x)=F(ax)$, then we say that $G$ and $F$ belong to the same scale family (under $\tau $).
Title  type of a distribution function 
Canonical name  TypeOfADistributionFunction 
Date of creation  20130322 16:25:48 
Last modified on  20130322 16:25:48 
Owner  CWoo (3771) 
Last modified by  CWoo (3771) 
Numerical id  13 
Author  CWoo (3771) 
Entry type  Definition 
Classification  msc 60E05 
Classification  msc 62E10 
Synonym  centering factor 
Synonym  scale parameter 
Synonym  location parameter 
Defines  type 
Defines  scale factor 
Defines  location factor 
Defines  standard distribution function 
Defines  location family 
Defines  scale family 