Group family

In probability theory, especially as that field is used in statistics, a group family of probability distributions is a family obtained by subjecting a random variable with a fixed distribution to a suitable family of transformations such as a location-scale family, or otherwise a family of probability distributions acted upon by a group.[1]

Consideration of a particular family of distributions as a group family can, in statistical theory, lead to the identification of an ancillary statistic.[2]

Types of group families

A group family can be generated by subjecting a random variable with a fixed distribution to some suitable transformations.[1] Different types of group families are as follows :

Location Family

This family is obtained by adding a constant to a random variable. Let X {\displaystyle X} be a random variable and a R {\displaystyle a\in R} be a constant. Let Y = X + a {\textstyle Y=X+a} . Then

F Y ( y ) = P ( Y y ) = P ( X + a y ) = P ( X y a ) = F X ( y a ) {\displaystyle F_{Y}(y)=P(Y\leq y)=P(X+a\leq y)=P(X\leq y-a)=F_{X}(y-a)}
For a fixed distribution , as a {\displaystyle a} varies from {\displaystyle -\infty } to {\displaystyle \infty } , the distributions that we obtain constitute the location family.

Scale Family

This family is obtained by multiplying a random variable with a constant. Let X {\displaystyle X} be a random variable and c R + {\displaystyle c\in R^{+}} be a constant. Let Y = c X {\textstyle Y=cX} . Then

F Y ( y ) = P ( Y y ) = P ( c X y ) = P ( X y / c ) = F X ( y / c ) {\displaystyle F_{Y}(y)=P(Y\leq y)=P(cX\leq y)=P(X\leq y/c)=F_{X}(y/c)}

Location - Scale Family

This family is obtained by multiplying a random variable with a constant and then adding some other constant to it. Let X {\displaystyle X} be a random variable , a R {\displaystyle a\in R} and c R + {\displaystyle c\in R^{+}} be constants. Let Y = c X + a {\displaystyle Y=cX+a} . Then

F Y ( y ) = P ( Y y ) = P ( c X + a y ) = P ( X ( y a ) / c ) = F X ( ( y a ) / c ) {\displaystyle F_{Y}(y)=P(Y\leq y)=P(cX+a\leq y)=P(X\leq (y-a)/c)=F_{X}((y-a)/c)}

Note that it is important that a R {\textstyle a\in R} and c R + {\displaystyle c\in R^{+}} in order to satisfy the properties mentioned in the following section.

Properties of the transformations

The transformation applied to the random variable must satisfy the following properties.[1]

  • Closure under composition
  • Closure under inversion

References

  1. ^ a b c Lehmann, E. L.; George Casella (1998). Theory of Point Estimation (2nd ed.). Springer. ISBN 0-387-98502-6.
  2. ^ Cox, D.R. (2006) Principles of Statistical Inference, CUP. ISBN 0-521-68567-2 (Section 4.4.2)
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