In probability and statistics the extended negative binomial distribution is a discrete probability distribution extending the negative binomial distribution .
The distribution appeared in its general form in a paper by K. Hess, A. Liewald and K.D. Schmidt[ 1] when they characterized all distributions for which the extended Panjer recursion works. For the case m = 1, the distribution was already discussed by Willmot.[ 2]
Probability mass function
For a natural number m ≥ 1 and real parameters p , r with 0 ≤ p < 1 and –m < r < –m + 1, the probability mass function of a random variable with an ExtNegBin(m , r , p ) distribution is given by
f
(
k
;
m
,
r
,
p
)
=
0
for
k
∈
{
0
,
1
,
…
,
m
−
1
}
{\displaystyle f(k;m,r,p)=0\qquad {\text{ for }}k\in \{0,1,\ldots ,m-1\}}
and
f
(
k
;
m
,
r
,
p
)
=
(
k
+
r
−
1
k
)
(
1
−
p
)
k
p
−
r
−
∑
j
=
0
m
−
1
(
j
+
r
−
1
j
)
(
1
−
p
)
j
for
k
∈
N
with
k
≥
m
,
{\displaystyle f(k;m,r,p)={\frac {{k+r-1 \choose k}(1-p)^{k}}{p^{-r}-\sum _{j=0}^{m-1}{j+r-1 \choose j}(1-p)^{j}}}\quad {\text{for }}k\in {\mathbb {N} }{\text{ with }}k\geq m,}
where
(
k
+
r
−
1
k
)
=
Γ
(
k
+
r
)
k
!
Γ
(
r
)
=
(
−
1
)
k
(
−
r
k
)
(
1
)
{\displaystyle {k+r-1 \choose k}={\frac {\Gamma (k+r)}{k!\,\Gamma (r)}}=(-1)^{k}\,{-r \choose k}\qquad \qquad (1)}
is the (generalized) binomial coefficient and Γ denotes the gamma function .
Proof that the probability mass function is well defined
Note that for all k ≥ m
(
k
+
r
−
1
k
)
=
(
∏
j
=
1
m
j
+
r
−
1
j
)
∏
j
=
m
+
1
k
(
1
+
r
−
1
j
)
{\displaystyle {\binom {k+r-1}{k}}={\biggl (}\prod _{j=1}^{m}{\frac {j+r-1}{j}}{\biggr )}\prod _{j=m+1}^{k}{\Bigl (}1+{\frac {r-1}{j}}{\Bigr )}}
has the same sign and, using log(1 + x ) ≤ x for x > –1 and noting that r – 1 < 0,
log
∏
j
=
m
+
1
k
(
1
+
r
−
1
j
)
≤
∑
j
=
m
+
1
k
r
−
1
j
≤
(
r
−
1
)
∫
m
+
1
k
+
1
d
x
x
=
log
(
k
+
1
m
+
1
)
r
−
1
.
{\displaystyle {\begin{aligned}\log \prod _{j=m+1}^{k}{\Bigl (}1+{\frac {r-1}{j}}{\Bigr )}&\leq \sum _{j=m+1}^{k}{\frac {r-1}{j}}\\&\leq (r-1)\int _{m+1}^{k+1}{\frac {dx}{x}}=\log {\Bigl (}{\frac {k+1}{m+1}}{\Bigr )}^{r-1}.\end{aligned}}}
Therefore,
∑
k
=
m
∞
|
(
k
+
r
−
1
k
)
|
≤
|
∏
j
=
1
m
k
+
r
−
1
j
|
∑
k
=
m
∞
(
m
+
1
k
+
1
)
1
−
r
<
∞
{\displaystyle \sum _{k=m}^{\infty }{\biggl |}{\binom {k+r-1}{k}}{\biggr |}\leq {\biggl |}\prod _{j=1}^{m}{\frac {k+r-1}{j}}{\biggr |}\sum _{k=m}^{\infty }{\Bigl (}{\frac {m+1}{k+1}}{\Bigr )}^{1-r}<\infty }
by the integral test for convergence , because 1 – r > 1. Using (1) and Abel's theorem ,
we see that the binomial series representation
(
1
−
x
)
−
r
=
∑
k
=
0
∞
(
−
r
k
)
(
−
x
)
k
{\displaystyle (1-x)^{-r}=\sum _{k=0}^{\infty }{\binom {-r}{k}}(-x)^{k}}
holds for all x in [–1,1]. Hence, the probability mass functions actually sums up to one.
Probability generating function
Using the above binomial series representation and the abbreviation q = 1 − p , it follows that the probability generating function is given by
φ
(
s
)
=
∑
k
=
m
∞
f
(
k
;
m
,
r
,
p
)
s
k
=
(
1
−
q
s
)
−
r
−
∑
j
=
0
m
−
1
(
j
+
r
−
1
j
)
(
q
s
)
j
p
−
r
−
∑
j
=
0
m
−
1
(
j
+
r
−
1
j
)
q
j
for
|
s
|
≤
1
q
.
{\displaystyle {\begin{aligned}\varphi (s)&=\sum _{k=m}^{\infty }f(k;m,r,p)s^{k}\\&={\frac {(1-qs)^{-r}-\sum _{j=0}^{m-1}{\binom {j+r-1}{j}}(qs)^{j}}{p^{-r}-\sum _{j=0}^{m-1}{\binom {j+r-1}{j}}q^{j}}}\qquad {\text{for }}|s|\leq {\frac {1}{q}}.\end{aligned}}}
For the important case m = 1, hence r in (–1,0), this simplifies to
φ
(
s
)
=
1
−
(
1
−
q
s
)
−
r
1
−
p
−
r
for
|
s
|
≤
1
q
.
{\displaystyle \varphi (s)={\frac {1-(1-qs)^{-r}}{1-p^{-r}}}\qquad {\text{for }}|s|\leq {\frac {1}{q}}.}
References
Discrete univariate
with finite support with infinite support
Continuous univariate
supported on a bounded interval supported on a semi-infinite interval supported on the whole real line with support whose type varies
Mixed univariate
Multivariate (joint) Directional Degenerate and singular Families