AMApplied Mathematics2152-7385Scientific Research Publishing10.4236/am.2020.118051AM-102380ArticlesPhysics&Mathematics Mean Difference and Mean Deviation of Tukey Lambda Distribution GiovanniGirone1AntonellaMassari2FabioManca3AngelaMaria D’Uggento4*Faculty of Economics, University of Bari, Largo Abbazia S. Scolastica, Bari, ItalyDepartment of Economics, Management and Business Law, University of Bari, Largo Abbazia S. Scolastica, Bari, ItalyDepartment of Economics and Finance, University of Bari, Largo Abbazia S. Scolastica, Bari, ItalyDepartment of Education, Psychology, Communication, University of Bari, Bari, Italy1708202011087717781, July 202021, August 2020 24, August 2020© Copyright 2014 by authors and Scientific Research Publishing Inc. 2014This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

The purpose of this paper is to broaden the knowledge of mean difference and, in particular, of an important distribution model known as Tukey lambda, which is generally used to choose a model to fit data. We have obtained compact formulas, which are not yet reported in literature, of mean deviation and mean difference related to the said distribution model. These results made it possible to analyze the relationships among variability indexes, namely standard deviation, mean deviation and mean difference, regarding Tukey lambda model.

Mean Difference Mean Deviation Tukey Lambda Distribution Variability Indexes’ Relationships
1. Introduction

The purpose of this work is to increase the methodological contributions on the mean difference and on the relationships of the mean difference with other variability indexes  . The studies on the mean difference, introduced by Corrado Gini in 1912 as a measure of the variability of the characters according to the aspect of inequality, have aroused the interest of many scholars over years and also recently  . The importance of mean difference is also due to the fact that the sample mean difference is a correct estimate of that of the population distribution model and, therefore, functional for inferential purposes . The theoretical contributions on the mean difference concern the main continuous distribution models (normal, rectangular, exponential, ...) , however, for other distribution models, such as Tukey’s, no contributions are known in literature.

2. Tukey Lambda Distribution

Tukey lambda distribution is usually used to choose a distribution model to fit data and its direct use is less usual. In general, its characteristic is that neither its density function f ( x ) nor its cumulative function F ( x ) is known, but only the inverse of this latter F − 1 ( x ) , that is the quantile function Q(p)  .

A complete Tukey distribution shape includes three parameters: one of position, one of scale and one of shape  .

In order to calculate the mean difference and the mean deviation, it is better to refer to a reduced distribution in which the position parameter is set to zero and the scale to one. Formulas of mean difference and mean deviation of complete distribution are equal to the ones of reduced distribution multiplied by the scale parameter value. Tukey lambda distribution is defined by the quantile function

x = Q ( p ) = p λ − ( 1 − p ) λ λ ,     0 < p < 1. (1)

Said function is not always analytically invertible and, therefore, allows to obtain cumulative function and density function only for some values of λ  which are λ = − 1 , 0 , 1 / 4 , 1 / 3 , 1 / 2 , 1 , 3 / 2 , 2 , 3 , 4 . Cumulative functions of Tukey lambda distribution for such values are listed below:

λ = − 1 ,   F ( x ) = − 2 + x + 4 + x 2 2 x ,   − ∞ < x < ∞ (2)

λ = 0 ,   F ( x ) = 1 1 + e − x ,   − ∞ < x < ∞ (3)

λ = 1 4 ,   F ( x ) = 1 2 + x 512 − 3584 x 2 − 17 x 6 + ( 1024 + 12 x 4 ) 512 + 2 x 4 ,   − 4 < λ < 4 (4)

λ = 1 3 , F ( x ) = 1 2 − 5 x 3 216 + x 5 72 ( 5832 + x 6 + 108 2916 + x 6 ) 1 / 3   + x 72 ( 5832 + x 6 + 108 2916 + x 6 ) 1 / 3 ,   − 3 < x < 3 (5)

λ = 1 2 ,   F ( x ) = 1 8 ( 4 − x 8 − x 2 ) ,   − 2 < x < 2 (6)

λ = 1 ,   F ( x ) = 1 + x 2 ,   − 1 < x < 1 (7)

λ = 3 2 , F ( x ) = 1 2 [ 1 − − 2 + 1 + 18 x 2 ( 1 − 45 x 2 − 81 x 4 2 + 3 2 x ( − 4 + 9 x 2 ) 3 ) 1 / 3 + ( 1 − 45 x 2 − 81 x 4 2 + 3 2 x ( − 4 + 9 x 2 ) 3 ) 1 / 3 ] , − 2 3 < x < 2 3 (8)

λ = 2 ,   F ( x ) = 1 + 2 x 2 ,   − 1 2 < x < 1 2 (9)

λ = 3 , F ( x ) = 1 2 ( 1 − 1 ( 6 x + 1 + 36 x 2 ) 1 / 3 + ( 6 x + 1 + 36 x 2 ) 1 / 3 ) , − 1 3 < x < 1 3 (10)

λ = 4 , F ( x ) = 1 2 ( 1 − 1 3 1 / 3 ( 36 x + 3 1 + 432 x 2 ) 1 / 3 + ( 36 x + 3 1 + 432 x 2 ) 1 / 3 3 2 / 3 ) , − 1 4 < x < 1 4 (11)

It is necessary to use numerical inversion of Q ( p ) to get a cumulative function for other λ values.

Regarding Tukey distribution, some characteristic values as function of λ are known: average, mode, median, standard deviation, asymmetry index, disnormality excess index, entropy, characteristic function. Expressions of mean difference and mean deviation are unknown.

3. Variability Indexes of Tukey Lambda Distribution

The variance of Tukey lambda distribution as a function of λ parameter  is

σ 2 = 2 λ 2 [ 1 1 + 2 λ − Γ ( λ + 1 ) 2 Γ ( 2 λ + 2 ) ] , λ > − 1 2 . (12)

By using the cumulative functions derived by the inversion of quantile functions of Tukey lambda distribution, mean difference and mean deviation values are obtained and shown in Table 1.

Mean difference values for integers from 1 to 10 are arranged exactly on a parabolic hyperbola

Δ ( λ ) = 4 2 + 3 λ + λ 2 ,   λ > 1. (13)

Some values of Δ calculated numerically for other values of λ parameter are also all arranged over the said function, which can be then considered a general expression of the mean difference of Tukey lambda distribution. Said function takes not-negative finite values for λ > − 1 , as it can be shown in Figure 1.

Therefore, the mean difference in Tukey lambda distribution has a domain λ > − 1 which is wider than the one of standard deviation λ > − 1 / 2 .

Let us now consider the mean deviation. First of all, we can see that the average of our distribution exists only for λ > − 1 and, therefore, said domain also applies to mean deviation. Mean deviation values for integers from 1 to 10 are arranged exactly over the function

δ ( λ ) = 2 1 − λ ( 2 λ − 1 ) λ ( λ + 1 ) ,   λ > − 1. (14)

Values of δ calculated numerically for other values of λ parameter are also all arranged exactly over the said function, which can be then considered the expression

Values of mean difference and mean deviation for some values of λ parameter in Tukey lambda distribution
λΔΔ
−1
−4/550/39.263764082403105
−3/464/57.27245685874591
−2/395.240144005205601
−3/550/74.297623404313451
−1/216/33.3137084989847696
−1/318/52.339289449053423
−1/464/212.0182092266958453
022log2
1/464/651.018262942376231
1/39/70.9283476330715509
1/216/154 3 ( 2 − 2 )
3/525/260.7088459262782351
2/39/100.6660710550945809
3/464/770.6177469599979266
12/31/2
3/216/350.34477152501692165
21/31/4
5/216/630.1881653270194103
31/57/48
42/153/32
52/2131/480
61/143/64
71/18127/3584
82/4585/3072
92/55511/23040
101/3393/5120

of mean deviation of the Tukey lambda distribution. Said function takes not-negative finite values for λ > − 1 as it can be shown in Figure 2.

The mean deviation of Tukey lambda distribution has, therefore, a domain wider than the one of standard deviation.

4. Relations between Variability Indexes of Tukey Lambda Distribution

By inverting the expression of mean difference in Tukey lambda distribution as a function of λ parameter (13), the following two roots come out

λ 1 = − 3 Δ + Δ 16 + Δ 2 Δ (15)

and

λ 2 = − 3 Δ − Δ 16 + Δ 2 Δ . (16)

The second solution, which is always negative, is not usable to obtain the relationship between ∆ and σ .

By substituting the first solution λ 1 (15) in the standard deviation expression, it comes out an analytical relationship of the same one related to the mean difference of Tukey lambda distribution:

σ = 2 2 16 / Δ + 1 − 2 − 2 Γ [ 1 2 ( 16 / Δ + 1 − 1 ) ] 2 Γ [ 16 / Δ + 1 − 1 ] 16 / Δ + 1 − 3 , Δ > 0. (17)

Said relationship is represented in Figure 3.

As it can be seen, standard deviation increases quickly when mean difference increases.

Let us, now, consider the relationship between mean difference and mean deviation.

By substituting root λ 1 in the formula of mean deviation (14), it comes out the following analytical relationship

δ ( Δ ) = 2 ( 2 3 2 − 16 Δ + 1 2 − 1 ) 16 Δ + 1 − 4 Δ − 1 ,   Δ > 0. (18)

As shown in Figure 4, it is evident that the relationship between the two indexes is almost linear.

Finally, let us consider the relationship between mean deviation and standard deviation of Tukey lambda distribution.

Since it is not possible to obtain λ parameter as a function of mean deviation, it is necessary to use a numerical procedure to calculate the two variability indexes values for a consistent set of λ parameter values and to represent pairs of values on a Cartesian axis.

By choosing values of λ: −0.49, −0.48, ..., 5.00, it comes out a numerical relationship as shown in Figure 5.

As it can be seen, the relationship between mean deviation and standard deviation of Tukey Lambda distribution increases with slow acceleration.

5. Conclusive Remarks

In this work, the formulas of mean difference and mean deviation of Tukey Lambda distribution have been obtained. It is an original contribution aimed at increasing the knowledge about this distribution model. These results allowed us to investigate the relationships among the three main variability indexes, standard deviation, mean deviation and mean difference, regarding Tukey lambda model.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

Cite this paper

Girone, G., Massari, A., Manca, F. and D’Uggento, A.M. (2020) Mean Difference and Mean Deviation of Tukey Lambda Distribution. Applied Mathematics, 11, 771-778. https://doi.org/10.4236/am.2020.118051