Package 'GWI'

Count and continuous generalized variability indexes

Description

Univariate Poisson dispersion index di.fun, univariate exponential variation index vi.fun functions are performed. Next, the univariate binomial dispersion index dib.fun, the univariate negative binomial dispersion index dinb.fun and the univariate inverse Gaussian variation index viiG.fun functions are given. Finally, the generalized dispersion index and its marginal one gmdi.fun , the generalized variation index and its marginal one gmvi.fun functions are displayed.

Details

The univariate Poisson dispersion index (DI) and its relative versions with respect to binomial and negative binomial distributions:

The Poisson dispersion phenomenon is well-known and very widely used in practice; see, e.g., Kokonendji (2014) for a review of count (or discrete integer-valued) models. There are many interpretable mechanisms leading to this phenomenon which makes it possible to classify count distributions and make inference; see, e.g., Mizère et al. (2006) and Touré et al. (2020) for approximative statistical tests. Introduced from Fisher (1934), the Poisson dispersion index, also called the Fisher dispersion index, of a count random variable $X$ on $S=\{0,1,2,\ldots\}=:N_0$ can be defined as

$DI(X)=\frac{VarX}{EX},$

the ratio of variance to mean. In fact, the positive quantity $DI(X)$ is the ratio of two variances since $EX$ is the expected variance under the Poisson distribution. Hence, one easily deduces the concept of the relative dispersion index (denoted by RDI) by choosing another reference than the Poisson distribution. Indeed, if $X$ and $Y$ are two count random variables on the same support $S\subseteq N_0$ such that $EX=EY$, then

$RDI_Y(X):=\frac{VarX}{Var Y}=\frac{DI(X)}{DI(Y)} >=< 1;$

i.e. $X$ is over-, equi- and under-dispersed compared to $Y$ if $VarX > VarY$, $VarX = VarY$ and $VarX < VarY$, respectively.

For instance, the binomial dispersion index is defined as

$RDI_B(X)=\frac{var X}{EX(1-EX/N)},$

where $N\in \{1,2,\ldots\}$ is the fixed number of trials. Also, the negative binomial dispersion index is defined as

$RDI_NB(X)=\frac{varX}{EX(1+EX/ \lambda)},$

where $\lambda > 0$ is the dispersion parameter. See also, Weiss (2018, page 15) and Abid et al. (2021) for more details.

The univariate variation index (VI) and its relative version with respect to inverse Gaussian distribution:

More recently, Abid et al. (2020) have introduced the exponential variation index for positive continuous random variable $X$ on $[0,\infty)$ as

$VI(X)=\frac{VarX}{(EX)^2}.$

It can be viewed as the squared coefficient of variation. It is used in the framework of reliability to discriminate distribution of increasing/decreasing failure rate on the average (IFRA/DFRA); see, e.g., Barlow and Proschan (1981) in the sense of the coefficient of variation. See also Touré et al. (2020) for more details. Following RDI, the relative variation index (RVI) is defined, for two continuous random variables $X$ and $Y$ on the same support $S = [0,\infty)$ with $EX = EY$, by

$RVI_Y(X):=\frac{VarX}{VarY}=\frac{VI(X)}{VI(Y)} >=< 1;$

i.e. $X$ is over-, equi- and under-varied compared to $Y$ if $VarX > VarY$, $VarX = VarY$ and $VarX < VarY$, respectively. For instance, the inverse Gaussian variation index is defined as

$RVI_IG(X)=\lambda^2\frac{var X}{(EX)^3},$

where $\lambda > 0$ is the shape parameter.

Next, consider the following notations. Let $Y$ = $(Y_1,\ldots,Y_k)^{\top}$ be a nondegenerate count or continuous $k$-variate random vector, $k\ge 1$. Let also $EY$ be the mean vector of $Y$ and $covY$= $(cov(Y_i,Y_j) )_{i,j\in \{1,\ldots,k\}}$ the covariance matrix of $Y$.

The generalized dispersion index (GDI) and marginal dispersion index (MVI):

Kokonendji and Puig (2018) have introduced the generalized dispersion index for count vector $Y$ on $\{0,1,2,\ldots\}^k$ by

$GDI(Y) =\frac{\sqrt{EY}^{\top} ( covY)\sqrt{EY}}{EY^{\top}EY}.$

Note that when $k=1$, $GDI(Y)$ is just the classical Fisher dispersion index DI. $GDI$($Y$) makes it possible to compare the full variability of $Y$ (in the numerator) with respect to its expected uncorrelated Poissonian variability (in the denominator) which depends only on $EY$. $GDI(Y)$ takes into account the correlation between variables. For only taking into account the dispersion information coming from the margins, the authors defined the "marginal dispersion index":

$MDI(Y) = \frac{\sqrt{EY}^{\top}( diag varY )\sqrt{EY}}{EY^{\top}EY}=\sum_{j=1}^k\frac{\{E(Y_j)\}^2}{EY^{\top}EY} DI(Y_j).$

The generalized variation index (GVI) and marginal variation index (MVI):

Similarly, Kokonendji et al. (2020) defined the generalized variation index for positive continuous vector $Y$ on $[0, \infty)^k$ by

$GVI(Y) =\frac{EY^{\top} ( covY) EY}{(EY^{\top}EY)^2}.$

Remark that when $k=1$, $GVI(Y)$ is the univariate variation index VI. $GVI(Y)$ makes it possible to compare the full variability of $Y$ (in the numerator) with respect to its expected uncorrelated exponential variability (in the denominator) which depends only on $EY$. Also, $GVI(Y)$ takes into account the correlation between variables. To only take into account the variation information coming from the margins, Kokonendji et al. (2020) defined the "marginal variation index":

$MVI(Y) = \frac{EY^{\top}( diag varY )EY}{(EY^{\top}EY)^2}=\sum_{j=1}^k\frac{(EY_j)^4}{(EY^{\top}EY)^2} VI(Y_j).$

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

Maintainer: Aboubacar Y. Touré <[email protected]>

References

Abid, R., Kokonendji, C.C. and Masmoudi, A. (2020). Geometric Tweedie regression models for continuous and semicontinuous data with variation phenomenon, AStA Advances in Statistical Analysis 104, 33-58.

Abid, R.,Kokonendji, C.C. and Masmoudi, A. (2021). On Poisson-exponential-Tweedie models for ultra-overdispersed count data, AStA Advances in Statistical Analysis 105, 1-23.

Barlow, R.A. and Proschan, F. (1981). Statistical Theory of Reliability and Life Testing : Probability Models, Silver Springs, Maryland.

Fisher, R.A. (1934). The effects of methods of ascertainment upon the estimation of frequencies, Annals of Eugenics 6, 13-25.

Kokonendji, C.C., Over- and underdispersion models. In: N. Balakrishnan (Ed.) The Wiley Encyclopedia of Clinical Trials- Methods and Applications of Statistics in Clinical Trials, Vol.2 (Chap.30), pp. 506-526. Wiley, New York (2014).

Kokonendji, C.C. and Puig, P. (2018). Fisher dispersion index for multivariate count distributions : A review and a new proposal, Journal of Multivariate Analysis 165, 180-193.

Kokonendji, C.C., Touré, A.Y. and Sawadogo, A. (2020). Relative variation indexes for multivariate continuous distributions on $[0,\infty)^k$ and extensions, AStA Advances in Statistical Analysis 104, 285-307.

Mizère, D., Kokonendji, C.C. and Dossou-Gbété, S. (2006). Quelques tests de la loi de Poisson contre des alternatives géenérales basées sur l'indice de dispersion de Fisher, Revue de Statistique Appliquée 54, 61-84.

Touré, A.Y., Dossou-Gbété, S. and Kokonendji, C.C. (2020). Asymptotic normality of the test statistics for relative dispersion and relative variation indexes, Journal of Applied Statistics 47, 2479-2491.

Weiss, C.H. (2018). An Introduction to Discrete-Valued Times Series. Wiley, Hoboken NJ.

---

Function for DI

Description

The function computes the univariate Poisson dispersion index for a count random variable.

Usage

di.fun(X)

Arguments

X	A count random variable

Details

di.fun provides the univariate Poisson dispersion index (Fisher, 1934). We can refer to Touré et al. (2020) for more details on the Poisson dispersion index.

Value

Returns

di	The Poisson dispersion index

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

References

Fisher, R.A. (1934). The effects of methods of ascertainment upon the estimation of frequencies, Annals of Eugenics 6, 13-25.

Touré, A.Y., Dossou-Gbété, S. and Kokonendji, C.C. (2020). Asymptotic normality of the test statistics for relative dispersion and relative variation indexes, Journal of Applied Statistics 47, 2479-2491.

Examples

X<-c(6,7,8,9,8,4,7,6,12,8,0)
di.fun(X)
T<-c(61,72,83,94,85,46,77,68,129,80,10,12,12,3,4,5)
di.fun(T)

---

Function for DIb

Description

The function computes the binomial dispersion index for a given number of trials $N\in \{1,2,\ldots\}$.

Usage

dib.fun(X, N)

Arguments

X	A count random variable
N	The number of trials of binomial distribution

Details

dib.fun computes the dispersion index with respect to the binomial distribution. See Touré et al. (2020) and Weiss (2018) for more details.

Value

Returns

dib	The binomial dispersion index

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

References

Touré, A.Y., Dossou-Gbété, S. and Kokonendji, C.C. (2020). Asymptotic normality of the test statistics for relative dispersion and relative variation indexes, Journal of Applied Statistics 47, 2479-2491.

Weiss, C.H. (2018). An Introduction to Discrete-Valued Times Series. Wiley, Hoboken NJ.

Examples

X<-c(12,9,0,8,5,7,6,5,3,4,9,4)
dib.fun(X,12)
Y<-c(0,0,1,1,0,1,1)
dib.fun(Y,7)

---

Function for DInb

Description

The function computes the negative binomial dispersion index for a given dispersion parameter $l\in (0,\infty)$.

Usage

dinb.fun(X, l)

Arguments

X	A count random variable
l	The dispersion parameter of negative binomial distribution

Details

dinb.fun computes the dispersion index with respect to negative binomial distribution. See Touré et al. (2020) and Abid et al. (2021) for more details.

Value

Returns

dinb	The negative binomial dispersion index

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

References

Abid, R.,Kokonendji, C.C. and Masmoudi, A. (2021). On Poisson-exponential-Tweedie models for ultra-overdispersed count data, AStA Advances in Statistical Analysis 105, 1-23.

Touré, A.Y., Dossou-Gbété, S. and Kokonendji, C.C. (2020). Asymptotic normality of the test statistics for relative dispersion and relative variation indexes, Journal of Applied Statistics 47, 2479-2491.

Examples

X<-c(12,9,0,8,5,7,6,5,3,4,9,4)
dinb.fun(X,12)
Y<-c(0,6,1,3,4,2,5)
dinb.fun(Y,7)

---

Function for GDI and MDI

Description

The function computes the GDI and MDI indexes for multivariate count data.

Usage

gmdi.fun(Y)

Arguments

Y	A matrix of count random variables

Details

gmdi.fun computes GDI and MDI indexes introduced by Kokonendji and Puig (2018).

Value

Returns:

gdi	The generalized dispersion index
mdi	The marginal dispersion index

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

References

Kokonendji, C.C. and Puig, P. (2018). Fisher dispersion index for multivariate count distributions : A review and a new proposal, Journal of Multivariate Analysis 165, 180-193.

Examples

Y<-cbind(c(1,2,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8))
gmdi.fun(Y)
Z<-cbind(c(1,2,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8))
gmdi.fun(Z)

---

Function for GVI and MVI

Description

The function computes GVI and MVI indexes for multivariate positive continuous data.

Usage

gmvi.fun(Y)

Arguments

Y	A matrix of positive continuous random variables

Details

gmvi.fun computes the GVI and MVI indexes defined in Kokonendji et al. (2020).

Value

Returns:

gvi	The generalized variation index
mvi	The marginal variation index

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

References

Kokonendji, C.C., Touré, A.Y. and Sawadogo, A. (2020). Relative variation indexes for multivariate continuous distributions on $[0,\infty)^k$ and extensions, AStA Advances in Statistical Analysis 104, 285-307.

Examples

Y<-cbind(c(2.3 ,26.1 ,8.7 ,10.9 ,1.2,1.4),c(9.7 ,7.3,9.3 ,9.4 ,10.5 ,9.8))
gmvi.fun(Y)
Z<-cbind(c(2.3 ,26.1 ,8.7),c(9.7 ,7.3,9.3),c(9.7 ,7.3,9.3),c(9.7 ,7.3,9.3))
gmvi.fun(Z)

---

Function for VI

Description

The function calculates the univariate exponential variation index for a positive continuous random variable.

Usage

vi.fun(X)

Arguments

X	A positive continuous random variable

Details

vi.fun computes the univariate exponential variation index defined by Abid et al. (2020). See also Touré et al. (2020) for more details on this index.

Value

Returns

vi	The exponential variation index

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

References

Abid, R., Kokonendji, C.C. and Masmoudi, A. (2020). Geometric Tweedie regression models for continuous and semicontinuous data with variation phenomenon, AStA Advances in Statistical Analysis 104, 33-58.

Touré, A.Y., Dossou-Gbété, S. and Kokonendji, C.C. (2020). Asymptotic normality of the test statistics for relative dispersion and relative variation indexes, Journal of Applied Statistics 47, 2479-2491.

Examples

X<-c(6.23,7.02,8.94,9.56,8.01,4.34,7.44,6.66,12.72,8.34,0)
vi.fun(X)
T<-c(6.231,7.022,8.943,9.789,8.014,4.423)
vi.fun(T)

---

Function for VIiG

Description

The function computes the inverse Gaussian variation index with shape parameter $l\in (0,\infty)$.

Usage

viiG.fun(X, l)

Arguments

X	A positive continuous random variable
l	The shape parameter of the inverse Gaussian distribution

Details

viiG.fun computes the variation index with respect to the inverse Gaussian distribution. See Touré et al. (2020) for more details.

Value

Returns

viiG	The inverse Gaussian variation index

Author(s)

Aboubacar Y. Touré and Célestin C. Kokonendji

References

Touré, A.Y., Dossou-Gbété, S. and Kokonendji, C.C. (2020). Asymptotic normality of the test statistics for relative dispersion and relative variation indexes, Journal of Applied Statistics 47, 2479-2491.

Examples

X<-c(0.12,9.11,0.03,8.71,5.02,7.12,6.42,5.73)
viiG.fun(X,0.05)
Y<-c(0.003,6.283,1.001,3.112,4.342,2.890,5.005)
viiG.fun(Y,0.3)

---