91202104

International Journal of Research in Advent Technology, Vol.9, No.1, January 2021
E-ISSN: 2321-9637
Available online at www.ijrat.org
1
doi: 10.32622/ijrat.91202104
Tests for a Single Upper Outlier in a Johnson SB Sample with
Unknown Parameters
Tanuja Sriwastava, Mukti Kant Sukla
Abstract: Outliers are unexpected observations, which
deviate from the majority of observations. Outlier
detection and prediction are challenging tasks, because
outliers are rare by definition. A test statistic for single
upper outlier is proposed and applied to Johnson SB
sample with unknown parameters. The Johnson SB
distribution have four parameters and is extremely
flexible, which means that it can fit a wide range of
distribution shapes. Because of its distributional shapes it
has a variety of applications in many fields. The test
statistic proposed for the case when parameters are
known (Sriwastava, T., 2018) used here for developing
the test statistic when parameters are unknown. Critical
points were calculated for different sample sizes and for
different level of significance. The performance of the
test in the presence of a single upper outlier is
investigated. One numerical example was given for
highlighting the result.
Keywords: Outlier, Johnson SB Distribution, Trimmed
Sample, Critical Points, Simulation.
I. INTRODUCTION
The p.d.f of Johnson SB distribution with location
parameter 𝜉, scale parameter 𝜆 and two shape parameters
𝛾 𝑎𝑛𝑑 𝛿 is given by
𝑓(𝑥; 𝜉, 𝜆, 𝛾 , 𝛿) =
𝛿
√2𝜋
𝜆
(𝜆−(𝑥−𝜉))(𝑥−𝜉)
𝑒𝑥𝑝 [−
1
2
{𝛾 +
𝛿 ln (
𝑥−𝜉
𝜆−(𝑥−𝜉)
)}
2
], (1)
𝜉 ≤ 𝑥 ≤ 𝜉 + 𝜆, 𝛿 > 0, −∞ < 𝛾 < ∞, 𝜆 > 0, − ∞ < 𝜉 <
∞.
This distribution is extremely flexible, which means that
it can fit a wide range of distribution shapes. Because of
its distributional shapes it has a variety of application in
many fields like human exposure (Flynn, 2004), forestry
data (Zang et. al, 2003), rainfall data (Kotteguda, 1987)
and manay more because of its flexible nature. One of the
most important application of this distribution is in the
complex data set like microarray data analysis (Florence
George, 2007) etc.
The detection of outliers in such situations are
considerably important. A number of test statistic have
been proposed for several distributions when their
parameters are known (Barnett & Lewis, 1994).
A test statistic has been proposed for the Johnson SB
distribution when parameters are known by (Sriwastava,
T., 2018) in one of her research paper. But in practice, no
parameter would be known. In such situations, estimates
of all the parameters are used in the construction of a test
statistic for detection of outliers in a sample from a
Johnson SB distribution. Then its critical values and the
corresponding performance study were done by
simulation technique. Since the study is about outlying
observation, the entire sample should not be considered
for the estimation of all the parameters. Hence, the
estimates given by George and Ramachandran (2011) are
used as estimates of all the parameters, using a trimmed
sample obtained after removing the suspected outlying
observation(s).
The estimates of the parameters of this
distribution as given by George & Ramachandran (2011)
using maximum likelihood least square method was
considered are as follows.
𝛾
̂ = −
𝛿 ∑ 𝑔(
𝑥𝑖−𝜉
𝜆
)
𝑛−1
𝑖=1
𝑛−1
= −𝛿𝑔̅ . (2)
𝛿
̂2
=
𝑛−1
∑ [𝑔(
𝑥𝑖−𝜉
𝜆
)]
2
𝑛−1
𝑖=1 −
1
𝑛−1
[∑ 𝑔(
𝑥𝑖−𝜉
𝜆
)
𝑛−1
𝑖=1 ]
2 =
1
𝑣𝑎𝑟(𝑔)
.
(3)
where, 𝑔 (
𝑥−𝜉
𝜆
) = log (
𝑥−𝜉
𝜆−(𝑥−𝜉)
), 𝑔̅ is the mean and
𝑣𝑎𝑟(𝑔) is the variance of the values of g defined here.
The estimates of 𝜆 and 𝜉 were as follows;
𝜆
̂ =
(𝑛−1) ∑ 𝑥𝑖
𝑛−1
𝑖=1 𝑓−1(
𝑧𝑖−𝛾
𝛿
)−∑ 𝑓−1(
𝑧𝑖−𝛾
𝛿
)
𝑛−1
𝑖=1 ∑ 𝑥𝑖
𝑛−1
𝑖=1
(𝑛−1) ∑ [𝑓−1(
𝑧𝑖−𝛾
𝛿
)]
2
𝑛−1
𝑖=1 −[∑ 𝑓−1(
𝑧𝑖−𝛾
𝛿
)
𝑛−1
𝑖=1 ]
2 .(4)
𝜉
̂ = 𝑥̅ − 𝜆∗
𝑚𝑒𝑎𝑛 [𝑓−1
(
𝑧−𝛾
𝛿
)] , (5)
where z=
𝑥−𝜉
𝜆
is a standard normal variate. Hence, the
quantiles of x and the corresponding quantiles of z can be
considered as paired observations. When there were 100
or more x values, the percentiles 1 through 99 were
considered, while for k number of data points of x, where
k is less than 100, 𝑘 − 1 quantiles of x were considered.
Manuscript revised on January 29, 2021 and published on February
10, 2021
Dr. Tanuja Sriwastava, Assistant Professor, Department of Statistics, Sri
Venkateswara College, University of Delhi. Email ID:
tanujastat24@gmail.com,
Dr. Mukti Kanta Sukla, Associate Professor, Department of Statistics,
Sri Venkateswara College, University of Delhi. Email ID:
suklamk@gmail.com

E-ISSN: 2321-9637
2
doi: 10.32622/ijrat.91202104
These 𝑘 − 1 quantiles of x and the corresponding 𝑘 − 1
quantiles of z were considered as paired observations.
II.PROPOSED OUTLIER DETECTION TEST
STATISTICS
Let 𝑋1, 𝑋2, ⋯ , 𝑋𝑛 be a random sample from a Johnson SB
distribution with 𝛾 and 𝛿 as shape parameters, 𝜆 a scale
parameter and 𝜉 a location parameter and
𝑋(1), 𝑋(2), ⋯ , 𝑋(𝑛) be the corresponding order statistics of
the n observations.
In the paper of (Sriwastava, T. 2018), a test statistic was
proposed for detection of an upper outlier for a sample
from Johnson SB distribution, when all parameters were
assumed to be known. But in all these, if all the
parameters of Johnson SB distribution were assumed to be
unknown, then we propose to use the MLE-least square
estimators of George and Ramachandran (2011) in place
of all the parameters in the above said test statistics.
The test statistic for the case of an upper outlier
(unknown parameters) obtained is given as
𝑊′
={(
𝑋(𝑛)−𝜉
̂
𝑋(𝑛−1)−𝜉
̂) (
𝜆
̂−(𝑋(𝑛−1)−𝜉
̂)
𝜆
̂−(𝑋(𝑛)−𝜉
̂)
)}
𝛿
̂
, (6)
where 𝑋(𝑘) is the kth
order statistic, 𝑘 = 1,2, ⋯ , 𝑛.
2.1 Critical values of the test statistic
To detect an upper outlying observation in a sample from
a Johnson SB distribution, the test statistic 𝑊′
was used.
This test statistic should reject the null hypothesis for
larger value of 𝑊′
. Critical values of the test statistic 𝑊′
were obtained using simulation technique with 10,000
replications for different sample sizes. For this, a random
sample was generated from a Johnson SB distribution and
the estimates of all the parameters were calculated using
equation 2, 3, 4, 5 with this sample. Then the value of the
test statistic was calculated and the whole process was
replicated 10,000 times and the percentile value at 90, 95
and 99 percent was calculated, which is the critical values
at 10, 5 and 1 percent levels of significance respectively.
The critical value table for sample sizes n=3,
10(10)40(20)100, 200, 500, 1000 and at 10, 5 and 1
percent levels of significance are shown in table 1.
Table 1. Critical values 𝑊
𝛼 of the statistic 𝑊′
n
100 α% Level
10% 5% 1%
3 3.90214 4.45028 13.7898
10 3.22234 4.30638 7.82518
20 2.79391 3.66518 6.0721
30 2.54645 3.2718 5.2587
40 2.41443 2.99493 4.93746
60 2.35604 2.90261 4.75126
80 2.30658 2.85917 4.48976
100 2.26234 2.82311 4.30515
200 2.12077 2.59739 3.83966
500 1.96466 2.37174 3.52657
1000 1.91691 2.27917 3.39466
It can be seen from the table that the values are decreases
with increase in sample size and on comparison of these
critical values with that of the one with known
parameters, it can be seen that the values are very close to
each other for sample sizes 10 onwards.
2.2 Numerical Example
For highlighting the utility of the statistic, the following
20 observations were taken from the Census data of India
taken in the year 2011, (in, 000).
1028610, 1045547, 1062388, 1095722, 1112186,
1128521, 1160813, 1176742, 1192506, 1223581,
1238887, 1254019, 1283600, 1298041, 1312240,
1339741, 1352695, 1365302, 1388994, 1399838.
Using the above data for the case when the outlying
observation is from another sample with a shift in all the
parameters. The critical value at 5% level of significance
for considered below is 3.5966. The parameters were
estimated using a sample obtained by leaving out the
largest observation (as that is the suspected outlying
observation). Then the value of test statistic 𝑊′
was
calculated with the estimated values of the parameters
and was found to be 5.99957. On comparing this with the
critical value at 5% level of significance for sample size
20, the null hypothesis gets rejected i.e. the largest
observation of the sample is confirmed as outlying.
III. PERFORMANCE STUDY
The performance study was done using simulation
technique for detection of an upper outlier. A random
sample of size n was generated using R software from a
Johnson SB distribution with location parameter 𝜉(=10),
scale parameter 𝜆(=30), with two shape parameters 𝛾(=1)
and 𝛿(=2) (known). Then a contaminant observation was
introduced in the sample.
For introducing a contaminant observation, another
sample of Johnson SB distribution with a shift (𝑎𝜆),
where 0 < a < 1 in the location parameter was generated.
The largest observation of the original sample was
replaced with the largest observation of the second
sample. Since the estimates of the parameters depend
upon only n-1 largest observations, the largest
observation of the sample need not be removed for
estimation purpose. Thus the values of the estimates of all
the parameters were calculated using MLE least square

E-ISSN: 2321-9637
3
doi: 10.32622/ijrat.91202104
method. Using these estimated values of the parameters,
the value of the test statistic 𝑊𝑈
′
was calculated at 5%
level of significance. Here, the process was simulated
10,000 times and the number of times the test statistic
falling in the critical region was noted. Simulation study
was carried out for different sample sizes. The
probabilities of rejection of null hypothesis for sample
sizes n = 10(10)30, 60, 100, 200, 500, 1000 and for
different values of ‘a’ are shown in the table 2.
Table 2: Probabilities of identification of the upper
contaminant observation.
a
n
0.033 0.067 0.1 0.133
10 0.4056 0.5249 0.6475 0.7569
20 0.3058 0.4362 0.5843 0.7315
30 0.3242 0.4845 0.6557 0.8001
60 0.2379 0.4095 0.6038 0.7856
100 0.2055 0.3971 0.6323 0.8253
200 0.2899 0.4253 0.7987 0.8541
500 0.3643 0.6603 0.8878 0.9821
1000 0.2401 0.5449 0.8283 0.963
a
n
0.2 0.27 0.3
10 0.9056 0.9753 0.9898
20 0.9166 0.9847 0.9942
30 0.9583 0.9948 0.9985
60 0.9669 0.9964 0.9987
100 0.9781 0.9994 0.9995
200 0.9887 1 1
500 0.9868 1 1
1000 0.9994 1 1
It can be observed from this table that, the test statistic
performs well for higher values of the shift i.e. for a>0.1.
As the values of shift increases, performance of the test
statistic also increases. On comparison with that of the
known parameter case, it can be seen that in this case,
performance is better for higher values of the shift,
whereas in the earlier case, performance was better for
lower values of the shift.
IV. CONCLUSION
It can be concluded from above study that the suggested
test statistic for an upper outlier is performing very well
for higher values of the shift. For the lower shift of values
performance of the test statistic was not satisfactory. The
higher values of shift and large sample sizes the test
statistic 𝑊′
is best for one contaminant observation. The
proposed work for future research work and
implementation includes:
 The generalization of proposed work to multiple
outlier case.
 Apply the proposed outlier detection technique
to variety of application.
 The method used here is may useful for
developing outlier detection test statistic for
complicated distribution like; Johnson SB.
REFERENCES
[1] Barnett V, Lewis T. Outliers in Statistical Data. John Wiley, 1994.
[2] Flynn MR. The 4 parameter lognormal (SB) model of human
exposure. Ann Occup Hyg. 2004; 48:617-22.
[3] George F. Johnson’s system of distribution and Microarray data
analysis. Graduate Theses and Dissertations, University of South
Florida, 2007.
[4] George, F. and Ramachandran, K.M. (2011). Estimation of
parameters of Johnson’s system of distributions. Journal of Modern
Applied Statistical Methods, 10, no. 2.
[5] Johnson NL. Systems of frequency curves generated by methods of
translation. Biometrica. 1949; 58:547-558.
[6] Kottegoda NT. Fitting Johnson SB curve by method of maximum
likelihood to annual maximum daily rainfalls. Water Resour Res.
1987; 23:728-732.
[7] Sriwastava T. An upper outlier detection procedure in a sample
from a Johnson SB distribution with known parameters.
International Journal of Applied Statistics and Mathematics. 2018;
3(2), 194-198.
[8] Zhang L, Packard PC, Liu C. A comparison of estimation methods
for fitting Weibull and Johnson’s SB distributions to mixed spruce-
fir stands in northeastern North America. Can J Forest Res. 2003;
33:1340-1347.
AUTHORS PROFILE
Dr. Tanuja Sriwastava She has completed her M.Sc.
(Statistics) from Banaras Hindu University, Varanasi and
D.Phil in Statistics from University of Allahabad,
Prayagraj. She has Qualified UGC-NET(JRF) and has
published 5 international and national publications. Her
area of interest in research is mainly focused on
Distribution theory and Statistical Inference.
Dr. Mukti Kanta Sukla A committed senior
Associate Professor in the Department of Statistics
with over 25 years of experience in one of the leading
colleges of Delhi University, Sri Venkateswara
College. Focused on research and has 10 prior
international and national publications. Did his Ph.D
from Utkal University, the research focusing on
Stochastic Modelling based on the modelling of the rainfall data of
Mahanadi Delta Region. Has vast teaching experience on courses like
Linear Models, Econometrics, Algebra, Sample Survey Methods etc.
Possessing excellent administrative, verbal communication and
leadership skills along with constructive and effective methods that
promote a stimulating learning environment.

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91202104