Additional technical details

Small sample bias

The ssdtools package uses the method of Maximum Likelihood (ML) to estimate parameters for each distribution that is fit to the data. Statistical theory says that maximum likelihood estimators are asymptotically unbiased, but does not guarantee performance in small samples. A detailed account of the issue of small sample bias in estimates can be found in the following pdf.

The inverse Pareto and inverse Weibull as limiting distributions of the Burr Type-III distribution

Burr III distribution

The probability density function, \({f_X}(x;b,c,k)\) and cumulative distribution function, \({F_X}(x;b,c,k)\) for the Burr III distribution (also known as the Dagum distribution) as used in ssdtools are:

Burr III distribution\[\begin{array}{*{20}{c}} {{f_X}(x;b,c,k) = \frac{{b\,k\,c}}{{{x^2}}}\frac{{{{\left( {\frac{b}{x}} \right)}^{c - 1}}}}{{{{\left[ {1 + {{\left( {\frac{b}{x}} \right)}^c}} \right]}^{k + 1}}}}{\rm{ }}}&{b,c,k,x > 0} \end{array}\]
\[\begin{array}{*{20}{c}} {{F_X}(x;b,c,k) = \frac{1}{{{{\left[ {1 + {{\left( {\frac{b}{x}} \right)}^c}} \right]}^k}}}{\rm{ }}}&{b,c,k,x > 0} \end{array}\]

Inverse Pareto distribution

Let \(X \sim Burr(b,c,k)\) have the pdf given in the box above. It is well known that the distribution of \(Y = \frac{1}{X}\) is the inverse Burr distribution (also known as the SinghMaddala distribution) for which:\[\begin{array}{*{20}{c}} {{f_Y}(y;b,c,k) = \frac{{c{\kern 1pt} {\kern 1pt} k{{\left( {\frac{y}{b}} \right)}^c}}}{{y{\kern 1pt} {{\left[ {1 + {{\left( {\frac{y}{b}} \right)}^c}} \right]}^{k + 1}}}}}&{b,c,k,y > 0} \end{array}\]

\[\begin{array}{*{20}{c}} {{F_Y}(y;b,c,k) = 1 - \frac{1}{{{{\left[ {1 + {{\left( {\frac{y}{b}} \right)}^c}} \right]}^k}}}}&{b,c,k,y > 0} \end{array}\]

We now consider the limiting distribution when \(c \to \infty\) and \(k \to 0\) in such a way that the product \(ck\) remains constant, i.e. \(ck = \lambda\).

Now, \[\begin{array}{l} \mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \left\{ {{F_Y}(y;b,c,k)} \right\} = 1 - \mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \frac{1}{{{{\left[ {1 + {{\left( {\frac{y}{b}} \right)}^c}} \right]}^k}}}\\ \\ and\\ \\ \mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } {\left[ {1 + {{\left( {\frac{y}{b}} \right)}^c}} \right]^k} = \mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \left\{ {{{\left( {\frac{y}{b}} \right)}^{ck}}{{\left[ {1 + {{\left( {\frac{b}{y}} \right)}^c}} \right]}^k}} \right\}\\ \\ and\\ \\ \mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \left\{ {{{\left( {\frac{y}{b}} \right)}^{ck}}{{\left[ {1 + {{\left( {\frac{b}{y}} \right)}^c}} \right]}^k}} \right\} = \mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \left\{ {{{\left( {\frac{y}{b}} \right)}^{ck}}} \right\}\mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \left\{ {{{\left[ {1 + {{\left( {\frac{b}{y}} \right)}^c}} \right]}^k}} \right\}\\ = \mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \left\{ {{{\left( {\frac{y}{b}} \right)}^{ck}}} \right\}\; \cdot \,1\\ = {\left( {\frac{y}{b}} \right)^\lambda } \end{array}\]

Therefore, \[\begin{array}{*{20}{c}} {\mathop {\mathop {\lim }\limits_{(c,k) \to (\infty ,0)} }\limits_{ck = \lambda } \left\{ {{F_Y}(y;b,c,k)} \right\} = 1 - {{\left( {\frac{b}{y}} \right)}^\lambda }}&{y \ge b} \end{array}\]

which we recognise as the (American) Pareto distribution. So, if the limiting distribution of \(Y = \frac{1}{X}\) is a Pareto distribution, then the limiting distribution of \(X = \frac{1}{Y}\) is the (American) inverse Pareto distribution:

\[\begin{array}{l} {f_X}\left( {x;\alpha ,\beta } \right) = \lambda {b^\lambda }{x^{\lambda - 1}};{\rm{ }}0 \le x \le {\textstyle{1 \over b}};{\rm{ }}\lambda {\rm{,}}b > 0\\ {F_X}\left( {x;\alpha ,\beta } \right) = {\left( {xb} \right)^\lambda };{\rm{ }}0 \le x \le {\textstyle{1 \over b}};{\rm{ }}\lambda {\rm{,}}b > 0 \end{array}\]

For completeness, the MLEs of this distribution have closed-form expressions and are given by: \[\begin{array}{l} \hat \lambda = {\left[ {\ln \left( {\frac{{{g_X}}}{{\hat b}}} \right)} \right]^{ - 1}}\\ \hat b = \frac{1}{{\max \left\{ {{X_i}} \right\}}}{\rm{ }} \end{array}\]

and \({\rm{ }}{g_X}\)is the geometric mean of the data.

Inverse Weibull distribution

Let \(X \sim Burr(b,c,k)\) have the pdf given in the box above. We make the transformation \[Y = \frac{{b{\kern 1pt} {k^{\tfrac{1}{c}}}{\kern 1pt} \theta }}{X}\] where \(\theta\) is a parameter (constant). The distribution of \(Y\) is also a Burr distribution and has cdf \[{G_Y}\left( y \right) = 1 - \frac{1}{{{{\left[ {1 + {{\left( {\frac{y}{{{k^{\tfrac{1}{c}}}{\kern 1pt} \theta }}} \right)}^c}} \right]}^k}}}\].

We are interested in the limiting behaviour of this Burr distribution as \(k \to \infty\).
Now,\[\mathop {\lim }\limits_{k \to \infty } {G_Y}\left( y \right) = 1 - \mathop {\lim }\limits_{k \to \infty } {\left[ {1 + {{\left( {\frac{y}{{{k^{\tfrac{1}{c}}}{\kern 1pt} \theta }}} \right)}^c}} \right]^{ - k}}\]

\[{ = 1 - \mathop {\lim }\limits_{k \to \infty } {{\left[ {1 + \frac{{{{\left( {\frac{y}{\theta }} \right)}^c}}}{{k{\kern 1pt} }}} \right]}^{ - k}}}\]

\[\begin{matrix} =1-\exp \left[ -{{\left( \frac{y}{\theta } \right)}^{c}} \right] \\ \left\{ \text{using the fact that }\underset{n\to \infty }{\mathop{\lim }}\,{{\left( 1+{}^{z}\!\!\diagup\!\!{}_{n}\; \right)}^{-n}}={{e}^{-z}} \right\} \\ \end{matrix}\]

We recognise the last expression as the cdf of a Weibull distribution with parameters \(c\) and \(\theta\).

Licensing

The documentation is released under the CC BY 4.0 License

The code is released under the Apache License 2.0

sstools Team

2024-05-02