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Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
Available online at http://jeasiq.uobaghdad.edu.iq
IRWs
)dromar72@coadec.uobaghdad.edu.iq(
)mohammad.stat.145@gmail.com(
Published:11/3/2020 Accepted:3/5/2020 Received :August / 2020
NC 4.0)-(CC BY NonCommercial 4.0 International-Attribution
Muggeo
IRWm
M-estimator Tukey
S-estimator Tukey
IRWs
M
S
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
-1Introduction
Segmented linear regression model
join point
(change point)(break points)
[10]broken line model ([16] joinpoint regression model)[17]piecewise linear model
([11][12]Quandt,1958,1960)
([9]Hudson,1966), ([6]Feder,1975), ([7]Gallant and Fuller,1973)
[10]Muggeo,2003)
(Linear reparametrization technique)
([17]F. Zhang and Q. Li,2017)
robust rank-based estimator bent linear regression
Muggeo,2003) rank dispersion
([4]O.A.Ali, M.A.Abbas,2019)
(M-Estimator)
HuberTukeyHampel(Muggeo)
(Tukey) [1]Sukru A., Birdal S.,2020
[9] Hudson 1966 [10] Muggeo2003 (MML)
(two-segmented linear regression model)
(S-estimator)(Muggeo)(S-estimator) MM-scales
(M-estimator)
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
2
2-1
[8:pp.569]
(
(1)
0
2-2Estimation methods1-2-2(Muggeo)
(maximum likelihood)
(ML)
([10]Vito M. R. Muggeo,2003)
[10:pp3057]
(3)
reparameterization
x
(Muggeo)
(Taylor expansion)
[17:pp.6]
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
(5)(6)
(5)
(ML)
[10:pp.3059-3061]
(10)
(9) (ML)
(
(9)3
24
4s
4
ML
sth
2-2-2MRobust IRWm-estimator
(IRWm-estimator) ([4]O.A.Ali, M.A.Abbas,2019)(M-estimator)
(Muggeo)
(loss function
[4:pp.393-394]
[4:pp.394-395]
0.01
M
(9)(Muggeo) a
(9)Muggeo
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
b c
M
[5:pp56]
(14)
(15) (WLS)
[5:pp.57]
n n
d ac[17:pp.7]
(11)(Muggeo)
(Muggeo)
[10:pp.3061]
sth
(IRWm-estimator)
p
(Tukey) ([4]O.A.Ali, M.A.Abbas,2019)
(Tukey bisquare)[14:pp.6]
c = 4.685
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
3-2-2 (S-estimator)
S MM-scales
[13]Rousseeuw and Yohai,1984 M
weaknesses [15:pp.354]
(Muggeo)
M(2-2-2)
S
(IRWs-estimator)
0.01
S
(9)(Muggeo) a
(9)Muggeo
b
c
S[15:pp. 354]
(Tukey)
(19)(2-2-2)
s(WLS)
[15:pp. 355]
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
n n
d
ac[17:pp.7]
(11)(Muggeo)
(Muggeo)
[10:pp.3061]
sth
(IRWs-estimator)
2-3The standard error for the change point
Wald Wald
[17:pp.8]
var(.)cov(. , ,)
converges(Muggeo)
[10:pp.3061]
3The Application
3-1Real Data Description
(Sediment transport)
[2:pp.7-8]
8
([3]Ammar A. Ali, Nadhir A. Al-Ansari, Qusay Al-Suhail and
Sven Knutsson,2017) 16
(1)
[3:pp.67] CS6-1
8(1)(2)
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
:(1)
[3:p p.60]
(1)CS6-1
(van Rijn 1984)
1
1000
900
800
700
600
500
400
300
X
4.011
3.122
2.529
2.157
2.003
1.866
1.954
2.251
Y
[3]
1
XDischarge Y bed load
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
(2) CS6-1
(van Rijn 1984)
3-2Results Discussion
(2)
(3)(S.E.( ))
(MSE)
(2)
(2)
CS6-1
Parameters of the
segmented linear
regression model
Methods
Muggeo
IRWm-
estimator
IRWs-estimator
Tukey Bisquare
Tukey Bisquare
737.0557
737.4091
733.4267
2.1157
2.1049
1.8560
-0.00014
-0.00012
0.00025
-3.4483
-3.4475
-3.3990
0.00741
0.00741
0.00741
S.E.( )
34.151
33.203
0.124
MSE
0.02743
0.02522
7.6698e-8
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
Muggeo
IRWm-estimator(Tukey Bisquare)
IRWs-estimator(Tukey Bisquare)
(3)CS6-1
(2) (MSE
S.E.( ) (IRWs-estimator) (Tukey bisquare)(IRWm-estimator)(Tukey bisquare)
4Conclusions and Recommendations
4-1Conclusions
(IRWs-estimator)(Tukey)
ML (Muggeo) (IRWm-estimator)
(MSE)S.E.( ) (IRWs-estimator) (Tukey)
(IRWs-estimator) (Tukey)
.
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
4-2Recommendations
(IRWs-estimator)
3-4Future Studies
Acknowledgments
We introduce our thanks to the editors and the reviewers of (JEAS) journal for
their efforts in enriching the research with their valuable comments. Thanks also
to Dr. Ammar Adel Ali for providing us with data on the hydrology of the Tigris
River at Baghdad city. References
1. Acitas,S., Senoglu,B.,(2020). Robust change point estimation in two-phase
linear regression models: An application to metabolic pathway data, Journal of
Computational and Applied Mathematics,Vol.363,pp. 337–349.
2. Ali A. A .(2016) "Three Dimensional Hydro- Morphological of Tigris River". A
thesis submitted to Lulea University of Technology. Sweden .
3. Ali, A.A., Al-Ansari N.A., Al-Suhail, Q., and Knutsson S., (2017). Spatial
Measurement of Bedload Transport in Tigris River. Journal of Earth Sciences and
Geotechnical Engineering, vol.7, no. 4, 55-75.
4. Ali,O.A., Abbas,M.A.,(2019),New Robust Estimator Of Change Point In
Segmented Regression Model For Bed-Load Of Rivers, Journal of Mechanics of
Continua and Mathematical Sciences, Vol.14, No.6, pp.384-402.
5. Almetwally,E.M., Almongy,H.M.,(2018), Comparison Between M-estimation, S-
estimation, And MM Estimation Methods of Robust Estimation with Application
and Simulation, International Journal of Mathematical Archive, 55-63 .
6. Feder,P.I.,(1975),The log likelihood Ratio In Segmented Regression, Annals of
Statistics, pp. 84-97.
7. Gallant,A. R., Fulle, W.A. ,(1973), Fitting Segmented Polynomial Regression
Models Whose Join Points have to be Estimated, Journal of the American Statistical
Association, Vol. 68, No. 341, pp. 144-147.
8. Greene, M.E., Rolfson, O.,Garellick, G.,Gordon, M. and Nemes, S., (2014),
Improved statistical analysis of pre- and post-treatment patient-reported
outcome measures (PROMs) the applicability of piecewise linear regression
splines, Quality of Life Research, Vol.24,Issue3,pp.567–573.
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
9. Hudson, D.J., (1966), Fitting Segmented Curves Whose Join Points Have to be
Estimated, Journal of the American Statistical Association, 61:316, 1097-1129.
10. Muggeo, V. M.R., (2003),Estimating regression models with unknown
breakpoints,Statistics in Medicine 22, 3055–3071.
11. Quandt, R.E., (1958),The estimation of the parameters of a linear regression
system obeying two separate regimes. J. Amer. Statist. Assoc,53,873–880.
12. Quandt, R.E., (1960), Tests of the Hypothesis That a Linear Regression System
Obeys Two Separate Regimes, J. Amer. Statist. Assoc,55:290, 324-330.
13. Rousseeuw, P. J, Yohai, V. J.,(1984).Robust Regression by Mean of
SEstimators,Robust and Nonlinear Time Series Analysis, New York, ,256-274, doi:
10.1007/978-1-4615-7821-5-15.
14. Salini, S., Cerioli, A., Laurini, F. and Riani, M.,(2015), Reliable Robust
Regression Diagnostics, International Statistical Review, 0, 0, 1–29.
15. Susanti, Y., Pratiwi, H., Sulistijowati, H.S. and Liana, T.,(2014).M estimation, S
estimation, and MM Estimation in Robust Regression. Int. J. Pure Appl. Math. Vol.
91 No. 3,pp. 349-360.
16. Yu, B., Barrett, M. J., Kim, H.-J. and Feuer, E. J. (2007). Estimating joinpoints in
continuous time scale for multiple change-point models. Computational Statistics &
Data Analysis51, 2420–2427.
17. Zhang, F., Li, Q.,(2017), Robust bent line regression, Journal of Statistical
Planning and Inference.
Journal of Economics and Administrative Sciences
Vol.26 (NO. 121) 2020, pp. 415-427
Proposing Robust IRWs Technique to Estimate Segmented Regression Model
for the Bed load Transport of Tigris River with Change Point of Water
Discharge Amount at Baghdad City
Mohammed Ahmed Abbas
Omar Abdulmohsin Ali
Researcher. Sunni Endowment
Diwan, Department of Religious
Education and Islamic Studies,
Baghdad, Iraq
Assist. Prof. Department of
Statistics, College of Management
and Economics, Baghdad
University, Baghdad, Iraq
(mohammad.stat.145@gmail.com)
(dromar72@coadec.uobaghdad.edu.iq)
Published:11/3/2020 Accepted:3/5/2020 Received :August / 2020
NonCommercial 4.0 -Creative Commons AttributionThis work is licensed under a NC 4.0)-(CC BY International
Abstract
Segmented regression consists of several sections separated by different
points of membership, showing the heterogeneity arising from the process of
separating the segments within the research sample. This research is concerned
with estimating the location of the change point between segments and estimating
model parameters, and proposing a robust estimation method and compare it
with some other methods that used in the segmented regression. One of the
traditional methods (Muggeo method) has been used to find the maximum
likelihood estimator in an iterative approach for the model and the change point
as well. Moreover, robust estimation method (IRW method) has used which
depends on the use of the robust M-estimator technique in segmentation idea and
using the Tukey weight function. The research’s contribution lies in the
suggestion to use the S-estimator technique and using the Tukey weight function,
to obtain a robust method against cases of violation of the normal distribution
condition for random errors or the effect of outliers, and this method will be
called IRWs. The mentioned methods have been applied to a real data set related
to the bed-load of Tigris River/ Baghdad city as a response variable and the
amount of water discharge as an explanatory variable. The results of the
comparison showed the superiority of the proposed method.
Keywords: segmented linear regression, change point, reparameterization,
M-estimator, S-estimator, bed-load transport, hydrology of water bodies.