Sains Malaysiana 51(11)(2022): 3819-3827
http://doi.org/10.17576/jsm-2022-5111-25
Confidence Interval
for Parameters Estimates in Circular Simultaneous Functional Relationship Model
(CSFRM) for Equal Variances using Normal Asymptotic and Bootstrap Confidence
Intervals
(Selang Keyakinan Anggaran Parameter untuk Model Hubungan Fungsian Membulat
Serentak (CSFRM) dengan Andaian Ralat Varians sama menggunakan Pendekatan
Asimptot dan Pembutstrapan)
FATIN NAJIHAH BADARISAM1,*, MOHD SYAZWAN
MOHAMAD ANUAR2, ABDUL GHAPOR HUSSIN1, ADZHAR RAMBLI3 & NURUL RAUDHAH ZULKIFLI3
1Faculty
of Defence Science and Technology, National Defence University of Malaysia, Kem
Sungai Besi, 57000 Kuala Lumpur, Federal Territory, Malaysia
2Centre
for Defence Foundation Studies, National Defence University of Malaysia, Kem
Sungai Besi, 57000 Kuala Lumpur, Federal Territory, Malaysia
3Faculty
of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah
Alam, Selangor Darul Ehsan, Malaysia
Received:
24 January 2022/Accepted: 13 July 2022
Abstract
Few studies
have considered the functional relationship model for circular variables. Anuar
has proposed a new model of Circular Simultaneous Functional Relationship Model
for equal variances. However, the confidence interval for all parameter
estimates in this model has not received any consideration in any literature.
This paper proposes the confidence interval for all parameter estimates of von
Mises distribution in this model. The parameters are estimated using minimum
sum (ms) and polyroot function provided in (built-in package) Splus statistical
software. The parameters confidence may be obtained from parameter estimation.
Those estimation values are obtained by minimizing the negative value of the
log-likelihood function. Then, the confidence interval for all parameters based
on the bootstrap method will be compared with the normal asymptotic confidence
interval via simulation studies. It is found that bootstrap method is the
superior method by measuring the performance using coverage probability and
expected length. The confidence intervals are illustrated using real wind
direction data of Bayan Lepas that collected at 16.3 m above ground level,
latitude 05°18’N and longitude 100°16’E. The results showed that the estimate
parameters fall between the estimate interval, and we note that the method
works well for this model.
Keywords: Bootstrap confidence interval; circular simultaneous
functional relationship model; normal asymptotic confidence interval;
parameters estimate; von Mises distribution
AbstraK
Beberapa kajian telah mempertimbangkan model hubungan
fungsian untuk pemboleh ubah membulat. Anuar telah mencadangkan model baru
iaitu Model Hubungan Fungsian Membulat Serentak dengan Andaian Ralat Varians
Sama. Walau bagaimanapun, selang keyakinan semua anggaran parameter untuk model
ini tidak mendapat pertimbangan di mana-mana kepustakaan. Kajian ini
mencadangkan selang keyakinan untuk semua anggaran parameter taburan von Mises
dalam model ini. Parameter dianggarkan menggunakan fungsi minimum sum (ms) dan
fungsi polyroot yang dibekalkan (built-in) dalam perisian statistik
Splus. Keyakinan parameter boleh didapati daripada anggaran parameter. Nilai
anggaran tersebut boleh diperoleh dengan meminimumkan nilai negatif fungsi
kemungkinan log. Kemudian, selang keyakinan terhadap semua anggaran parameter
berdasarkan kaedah pembustrapan dibandingkan dengan kaedah normal asimptot
melalui kajian simulasi. Didapati kaedah pembustrapan adalah kaedah unggul
dengan mengukur prestasi menggunakan kebarangkalian liputan dan jangkaan
panjang. Kaedah ini diilustrasikan menggunakan data arah angin Bayan Lepas yang
dikumpul pada 16.3 m di atas paras tanah, latitud 05°18’N dan longitud
100°16’E. Hasil kajian menunjukkan bahawa semua anggaran parameter jatuh antara
selang anggaran dan kaedah tersebut berfungsi dengan baik untuk model ini.
Kata kunci: Anggaran
parameter; model hubungan fungsian membulat serentak; selang keyakinan pembutstrapan; selang keyakinan
normal asimptot; taburan von Mises
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*Corresponding author;
email: m.syazwan@upnm.edu.my
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