Skip to main content
Log in

A zero-inflated logarithmic series distribution of order k and its applications

  • Original Paper
  • Published:
AStA Advances in Statistical Analysis Aims and scope Submit manuscript

Abstract

Here we develop an order k version of the zero-inflated logarithmic series distribution of Kumar and Riyaz [Staistica (accepted for publication), 2013b] through its probability generating function, and derive an expression for its probability mass function. Certain recurrence relation for its probabilities, raw moments and factorial moments are also obtained, and the maximum likelihood estimation of its parameters is discussed. We have tested the significance of the additional parameters of the distribution by generalized likelihood ratio test and illustrated all these procedures using certain real-life data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Evans, D.A.: Experimental evidence concerning contagious distributions in ecology. Biometrika 40, 186–211 (1953)

    Article  MATH  Google Scholar 

  • Fisher, R.A., Corbet, A.S., Williams, C.B.: The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42–58 (1943)

    Article  Google Scholar 

  • Jain, G.C., Gupta, R.P.: A logarithmic series type distribution. Trabaios de Estadistica 24, 99–105 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  • Johnson, N.L., Kemp, A.W., Kotz, S.: Univariate discrete distributions. Wiley, New York (2005)

    Book  MATH  Google Scholar 

  • Khang, T.F., Ong, S.H.: A new generalization of the logarithmic distribution arising from the inverse trinomial distribution. Commun. Stat. Theory Methods 36, 3–21 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  • Kempton, R.A.: A generalized form Fisher’s logarithmic series. Biometrika 62, 29–38 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  • Kumar, C.S.: Some properties of Kemp family of distributions. Statistica 69, 311–316 (2009)

    Google Scholar 

  • Kumar, C.S.: Binomial Poisson distribution revisited. Econ. Qual. Control 25, 183–188 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  • Kumar, C.S., Riyaz, A.: A modified version of logarithmic series distribution and its applications. Commun. Stat. Theory Methods (accepted for publication) (2013a)

  • Kumar, C.S., Riyaz, A.: On the zero-inflated logarithmic series distribution and its modification. Statistica (accepted for publication) (2013b)

  • Kumar, C.S., Shibu, D.S.: On intervened stuttering Poisson distribution and its application. J. Stat. Theory Pract. 7, 544–557 (2013)

    Article  MathSciNet  Google Scholar 

  • Mathai, A.M., Haubold, H.J.: Special functions for applied scientists. Springer, New York (2008)

    Book  MATH  Google Scholar 

  • Minkova, L.D.: Polya–Aeppli distribution of order k. Commun. Stat. Theory Methods 39, 408–415 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  • Ong, S.H.: On a generalization of the log-series distribution. J. Appl. Stat. Sci. 10(1), 77–88 (2000)

    MATH  MathSciNet  Google Scholar 

  • Panaretos, J., Xekalaki, E.: On some distributions arising from certain generalized sampling schemes. Commun. Stat. Theory Methods 15, 873–891 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  • Philippou, A.N.: The negative binomial distribution of order k and some of its properties. Biom. J. 26, 789–794 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  • Puig, P.: Characterizing additively closed discrete models by a property of their MLEs with an application to generalized Hermite distribution. J. Am. Stat. Assoc. 98, 687–692 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  • Rao, C.R.: Linear statistical inference and its applications. John Wiley, New York (1973)

    Book  MATH  Google Scholar 

  • Slater, L.J.: Generalized hypergeometric functions. Cambridge University Press, Cambridge (1966)

    MATH  Google Scholar 

  • Tripathi, R.C., Gupta, R.C.: A generalization of the log-series distribution. Commun. Stat. Theory Methods 14, 1779–1799 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  • Tripathi, R.C., Gupta, R.C.: Another generalization of the logarithmic series and the geometric distribution. Commun. Stat. Theory Methods 17, 1541–1547 (1988)

    Article  MATH  Google Scholar 

  • Xekalaki, E., Panaretos, J.: On some distribution arising in inverse cluster sampling. Commun. Stat. Theory Methods 18, 355–366 (1989)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the Editor-in-Chief and anonymous referees for their valuable comments on an earlier version of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Satheesh Kumar.

Appendix

Appendix

1.1 Proof of Proposition 1

From (2.5) we have

$$\begin{aligned} \displaystyle H(s)&= \sum \nolimits _{z=0}^\infty {p_z } s^z \end{aligned}$$
(5.1)
$$\begin{aligned} \ \displaystyle&= C\,\frac{-\ln \left( 1-\sum \nolimits _{j=1}^k {\theta _j } s^j \right) }{\sum \nolimits _{j=1}^k {\theta _j } s^j} \end{aligned}$$
(5.2)

On expanding the logarithmic function in (5.2), we get

$$\begin{aligned} \displaystyle H(s)&= C\,\sum \limits _{z=1}^\infty {\frac{\left( \sum \nolimits _{j=1}^k {\theta _j } s^j \right) ^{z-1}}{z}}\nonumber \\ \displaystyle&= C\sum \limits _{z=0}^\infty {\frac{\left( \sum \nolimits _{j=1}^k {\theta _j } s^j \right) ^z}{z+1}} . \end{aligned}$$
(5.3)

Now, by applying the multinomial expansion we have

$$\begin{aligned} H(s)=C\sum \limits _{z=0}^\infty {\sum \limits _{J_z } {\frac{z!}{z_1 !z_2 !\cdots z_k !}} } \frac{\theta _1^{z_1 } \theta _2^{z_2 } \cdots \theta _k^{z_k } }{(z+1)}s^\delta \end{aligned}$$
(5.4)

in which \(\delta =\sum \nolimits _{j=1}^k {j\,z_j } \), \(\sum \nolimits _{J_z }\) denote the summation over all tuples \((z_1 ,z_2, \dots ,z_k )\) of non-negative integers in the set \(J_z =\{(z_1 ,z_2 ,\dots ,z_k ):\sum \nolimits _{j=1}^k {\,z_j } =z\}\). Now, on equating the coefficient of \(s^z\) on the right hand side expressions of (5.1) and (5.4) we get (2.6).

1.2 Proof of Equation 2.10

In the light of (2.5), (2.7), (2.9), and (5.1) we have

$$\begin{aligned} \displaystyle H(s)&= \sum \limits _{z=0}^\infty {p_z } (\underline{v}+i)s^z \end{aligned}$$
(5.5)
$$\begin{aligned} \displaystyle&= R_i^{-1} { }_2F_1 \left( 1+i\;,\;1+i\;;\,\,2+i\;;\;\sum \limits _{j=1}^k {\theta _j }s^j\right) . \end{aligned}$$
(5.6)

On expanding the Gauss hypergeometric function in (5.6), we obtain

$$\begin{aligned} H(s)=R_i^{-1} \sum \limits _{z=0}^\infty {\frac{(1+i)_z (1+i)_z }{(2+i)_z }} \frac{\left( \sum \nolimits _{j=1}^k {\theta _j }s^j \right) ^z}{z!}, \end{aligned}$$
(5.7)

in which \((a)_k =a\,(a+1)\,\ldots \,(a+k-1)\) for \(k\ge 1\) and \((a)_0 =1\). By multinomial expansions, we get the following from (5.7).

$$\begin{aligned} H(s)=R_i^{-1} \sum \limits _{z=0}^\infty {\frac{(1+i)_z (1+i)_z }{(2+i)_z }} \frac{\theta _1^{z_{_1 } } \theta _2^{z_{_2 } } \dots \theta _k^{z_{_k } } }{z_1 !z_2 !\,\dots \,z_k !}s^\delta \end{aligned}$$
(5.8)

where \(\delta \) and \(\sum \nolimits _{I_z }\) are as defined in (5.4). On equating the coefficients of \(s^z\) on the right side expressions of (5.5) and (5.8) we get (2.10), since \((a-1)!(a)_k =\,(a+k-1)!\).

1.3 Proof of Proposition 2

From (2.5) we have

$$\begin{aligned} H(s)=R_0^{-1} \,_2 F_1 \left( 1\,\;,\;1\;;\;2\;;\;\sum \limits _{j=1}^k {\theta _j } s^j \right) . \end{aligned}$$
(5.9)

Differentiating (5.1) and (5.9) with respect to s, we get.

$$\begin{aligned} \sum \limits _{z=0}^\infty {z\,p_z } (\underline{v})s^{z-1}=D_0 R_0^{-1} \,_2 F_1 \left( 2\,\;,\;2\;;\;3\;;\;\sum \limits _{j=1}^k {\theta _j s^j} \right) \sum \limits _{j=1}^k {j\theta _j s^{j-1}} . \end{aligned}$$
(5.10)

From (5.1) and (5.9) we also have

$$\begin{aligned} R_1 \sum \limits _{z=0}^\infty {p_z (\underline{v}+1)\,s^z} =\;{ }_2F_1 \left( 2\,\;,\;2\,\,;\;\;3\,\;;\;\sum \limits _{j=1}^k {\theta _j s^j}\right) . \end{aligned}$$
(5.11)

By applying (5.11) in (5.10) we get

$$\begin{aligned} \sum \limits _{z=0}^\infty {z\,p_z (\underline{v})s^{z-1}} =D_0 R_0^{-1} R_1 \,\sum \limits _{z=0}^\infty {\sum \limits _{j=1}^k {j\theta _j p_z (\underline{v}+1)} s^{z+j-1}} . \end{aligned}$$
(5.12)

On equating the coefficient of \(s^z\)on both sides of (5.12) we obtain (2.13).

1.4 Proof of Proposition 3

On differentiating the right side expressions of (2.11) and (2.12) with respect to s, we obtain

$$\begin{aligned} \sum \limits _{r=1}^\infty {\mu _r (\underline{v})} \frac{(is)^{r-1}}{(r-1)!}=D_0 R_0^{-1} \sum \limits _{j=1}^k {j\theta _j } \mathrm{e}^{ijs}\;{ }_2F_1\left( 2\;,2\;;\;3\;;\sum \limits _{j=1}^k {\theta _j } \mathrm{e}^{ijs}\right) . \end{aligned}$$
(5.13)

From (2.11) and (2.12) we obtain the following.

$$\begin{aligned} R_1 \sum \limits _{r=0}^\infty {\mu _r (\underline{v}} +1)\frac{(is)^r}{r!}=\;{ }_2F_1 \left( 2\;,2\;;\;3\;;\;\sum \limits _{j=1}^k {\theta _j \mathrm{e}^{ijs}} \right) . \end{aligned}$$
(5.14)

Equations (5.13) and (5.14) together leads to

$$\begin{aligned} \sum \limits _{r=1}^\infty {\mu _r (\underline{v})} \frac{(is)^{r-1}}{(r-1)!}=D_0 R_0^{-1} R_1 \sum \limits _{r=0}^\infty {\sum \limits _{j=1}^k {j\theta _j \mathrm{e}^{ijs}\mu _z (\underline{v}+1)\frac{(is)^r}{r\,!}} } \; \end{aligned}$$
$$\begin{aligned} =D_0 R_0^{-1} R_1 \sum \limits _{r=0}^\infty {\sum \limits _{m=0}^\infty {\sum \limits _{j=1}^k {j^{m+1}\theta _j \mu _r (\underline{v}+1)} } } \frac{(is)^{r+m}}{r!m!}. \end{aligned}$$
(5.15)

Equating the coefficient of \(\frac{(is)^r}{r!}\) on both sides of (5.15) we get (2.14).

1.5 Proof of Proposition 4

From (2.5) we have the following factorial moment generating function \(F(s)\) of the ZILSD\(_{k}\)

$$\begin{aligned}&\displaystyle F(s)=\sum \limits _{r=0}^\infty {\mu _{\left[ r \right] } (\underline{v})\frac{s^{r}}{r!}} \end{aligned}$$
(5.16)
$$\begin{aligned}&\displaystyle =R_0^{-1}{ }_2 F_1 \left[ 1\,\;,\;1\;;\;2\;;\;\sum \limits _{j=1}^k {\theta _j (1+s)^j}\right] . \end{aligned}$$
(5.17)

On differentiating the right hand sides of (5.16) and (5.17) with respect to s, we obtain

$$\begin{aligned} \sum \limits _{r=1}^\infty {\mu _{\left[ r \right] } (\underline{v})\frac{s^{r-1}}{(r-1)!}} =D_0 R_0^{-1} \,\sum \limits _{j=1}^k {j\theta _j (1+s)^{j-1}} \,_2 F_1 [2\,\;,\;2\;;\;3\;;\;\sum \limits _{j=i}^k {\theta _j (1+s)^j}]. \end{aligned}$$
(5.18)

From (5.16) and (5.17) we have

$$\begin{aligned} R_1 \sum \limits _{r=0}^\infty {\mu _{[r]} (\underline{v}+1)\frac{s^r}{r!}} =_2 F_1 \left[ 2\,\;,\;2\;;\;3\;;\sum \limits _{j=1}^k {\theta _j (1+s)^j}\right] . \end{aligned}$$
(5.19)

Equations (5.18) and (5.19) together implies

$$\begin{aligned} \sum \limits _{r=1}^\infty {\mu \,_{\left[ r \right] } (\underline{v})\frac{s^{r-1}}{(r-1)!}} =D_0 R_0^{-1} R_1 \,\sum \limits _{r=0}^\infty {\sum \limits _{j=1}^k {j\theta _j (1+s)^{j-1}} } \mu _{\left[ r \right] } (\underline{v}+1)\frac{s^r}{r!} \end{aligned}$$
$$\begin{aligned} =D_0 R_0^{-1} R_1 \sum \limits _{r=0}^\infty {\sum \limits _{j=1}^k {\sum \limits _{m=0}^{j-1} {\left( \begin{array}{l} {j-1} \\ m \\ \end{array}\right) j\theta _j } } } \mu _{\left[ r \right] } (\underline{v}+1)\frac{s^{r+m}}{r!}. \end{aligned}$$
(5.20)

Now on equating the coefficient of \((r!)^{-1}s^r\) on both sides of (5.20) we get (2.15).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Satheesh Kumar, C., Riyaz, A. A zero-inflated logarithmic series distribution of order k and its applications. AStA Adv Stat Anal 99, 31–43 (2015). https://doi.org/10.1007/s10182-014-0229-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10182-014-0229-1

Keywords

Navigation