Sains Malaysiana 48(1)(2019): 237–242
http://dx.doi.org/10.17576/jsm-2019-4801-27
On
Robust Estimation for Slope in Linear Functional Relationship Model
(Penganggaran
Teguh bagi Kecerunan dalam Model Linear Hubungan Fungsian)
AZURAINI MOHD ARIF1, YONG ZULINA ZUBAIRI2* & ABDUL GHAPOR HUSSIN3
1Institute of Graduate
Studies, Universiti Malaya, 50603 Kuala Lumpur, Federal Territory, Malaysia
2Centre for Foundation
Studies in Science, Universiti Malaya, 50603 Kuala Lumpur, Federal Territory, Malaysia
3National Defense
University Malaysia, Sungai Besi Camp, 57000 Kuala Lumpur, Federal Territory, Malaysia
Received: 29 August 2017/Accepted: 3 August 2018
ABSTRACT
In this paper, we propose a robust parameter estimation method for
the linear functional relationship model. We improved the maximum likelihood
estimation using robust estimators and robust correlation coefficients to
estimate the slope parameter. The performance of the propose method, MMLE,
is compared with the standard maximum likelihood estimation (MLE)
and the nonparametric method in terms of mean square error. The results for
simulation studies suggested the performance of the MMLE and
nonparametric methods gives better estimate than the standard MLE in
the presence of outliers. The novelty of the proposed method is that it is not
affected by the presence of outliers and is simple to use. To illustrate
practical application of the methods, we obtain the estimate of the slope
parameter in a study of body-composition techniques for children.
Keywords: Linear functional relationship model; mean square error;
modified maximum likelihood estimation; outliers; robust
ABSTRAK
Dalam kertas ini, kami mencadangkan kaedah penganggaran parameter
teguh bagi model linear hubungan fungsian. Kami menambah baik
kaedah kebolehjadian maksimum menggunakan penganggar teguh dan
pekali korelasi teguh bagi menganggarkan parameter kecerunan.
Kuasa pretasi diukur bagi kaedah yang disyorkan iaitu MMLE, MLE dan
kaedah tidak berparameter menggunakan ralat kuasa dua min. Keputusan
simulasi menujukkan prestasi bagi kaedah yang disyorkan, MMLE dan
kaedah tidak berparameter adalah lebih teguh daripada kaedah kebolehjadian
maksimum apabila terdapat data terpencil. Kepentingan kaedah yang
dicadangkan adalah ia tidak terjejas dengan kehadiran data terpencil
dan juga mudah digunakan. Penggunaan kesemua kaedah yang dicadangkan
ditunjukkan melalui data set sebenar dengan kaedah untuk menganggarkan
kecerunan model bagi data komposisi badan untuk kanak-kanak.
Kata kunci: Kebolehjadian
maksimum yang diubah suai; min ralat kuasa dua; model linear hubungan fungsian;
teguh; terpencil
REFERENCES
Abdullah, M.B. 1989. On robust alternatives to the maximum
likelihood estimators of a linear functional relationship. Pertanika 12(1):
89-98.
Al-Nasser, A.D. & Ebrahem, M.A.H. 2005. A new
nonparametric method for estimating the slope of simple linear measurement
model in the presence of outliers. Pak. J. Statist. 21(3): 265-274.
Fuller, W.A. 1987. Measurement Error Models. New
York: John Wiley & Sons.
Gençay, R. & Gradojevic, N. 2011. Errors-in-variables
estimation with wavelets. Journal of Statistical Computation and Simulation 81(11):
1545-1564.
Ghapor, A.A., Zubairi, Y.Z. & Imon, A.H.M.R. 2017.
Missing value estimation methods for data in linear functional relationship
model. Sains Malaysiana 46(2): 317-326.
Ghapor, A.A., Zubairi, Y.Z., Mamun, A.S.M. & Imon,
A.H.M.R. 2015. A robust nonparametric slope estimation in linear functional
relationship model. Pak. J. Statist. 31(3): 339-350.
Goran, M., Driscoll, P., Johnson, R., Nagy, T. & Hunter,
G. 1996. Cross-calibration of body-composition techniques against dual-energy
X- ray absorptiometry in young children. Am. J. Clin. Nutr. 63(3):
299-305.
Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. & Stahel,
W.A. 1986. Robust Statistics: The Approach Based on Influence Functions.
New York: John Wiley & Sons.
Imon, A.H.M.R. & Hadi, A.S. 2008. Identification of
multiple outliers in logistic regression. Communications in Statistics-
Theory and Methods 37(11): 1697-1709.
Kendall, M.G. 1951. Regression, structure and functional
relationship. Part I. Biometrika 38(1/2): 11-25.
Kendall, M.G. & Stuart, A. 1979. The Advanced Theory
of Statistics. Vol. 2. London: Griffin.
Kim, M.G. 2000. Outliers and influential observations in the
structural errors-in-variables model. Journal of Applied Statistics 27(4):
451-460.
Lindley, D.V. 1947. Regression lines and the linear
functional relationship. Supplement to the Journal of the Royal Statistical
Society 9(2): 218-244.
Moran, P.A.P. 1971. Estimating structural and functional
relationships. Journal of Multivariate Analysis 1(2): 232-255.
Patefield, W.M. 1985. Information from the maximized
likelihood function. Biometrika 72(3): 664-668.
Rousseeuw, P.J. & Croux, C. 1993. Alternatives to the
median absolute deviation. Journal of the American Statistical Association 88(424):
1273-1283.
Shevlyakov, G. & Smirnov, P. 2011. Robust estimation of
the correlation coefficient: An attempt of survey. Austrian Journal of
Statistics 40(1): 147-156.
Solari, M.E. 1969. The “maximum likelihood solution” of the
problem of estimating a linear functional relationship. Journal of the Royal
Statistical Society: Series B (Methodological) 31(2): 372-375.
*Corresponding
author; email: yzulina@um.edu.my