Hendayanti, Ni Putu Nanik (2015) Estimasi Kurva Regresi Nonparametrik Heteroskedastisitas Spline (Studi Kasus Berat Badan Balita di Kecamatan Kerambitan, Bali). Masters thesis, Institut Technology Sepuluh Nopember.
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Abstract
Pendekatan nonparametrik merupakan metode estimasi yang tidak terikat
asumsi bentuk kurva tertentu. Pendekatan regresi nonparametrik yang sering
digunakan adalah spline. Spline memiliki kemampuan yang sangat baik untuk
menangani data yang perilakunya berubah-ubah pada sub-sub interval tertentu.
Pada regresi nonparametrik, estimator spline sangat tergantung pada titik knot
optimal, dimana pemilihan titik knot optimal berdasarkan nilai GCV (Generalized
Cross Validation) yang minimum. Dalam penelitian ini, penulis mengestimasi
kurva gˆ dengan menggunakan optimasi Likelihood dan mengkontruksi selang
kepercayaan untuk kurva regresi g dengan pendekatan spline menggunakan
Pivotal Quantity. Model regresi yang diteliti adalah model regresi nonparametrik
spline heteroskedastisitas.
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Nonparametric approachs are an estimation methods not tied on particular
shape of the curve assumptions. The most frequently used of nonparametric
regression approach is spline. Spline has an excellent ability to handle data that
behavior change in sub-specified interval. In nonparametric regression, spline
estimator depends on the point of optimal knots, which is the selection of the
optimal knots based on the value of GCV (Generalized Cross Validation)
minimum. In this study, gˆ curve is estimated using Likelihood optimization and
confidence intervals for the regression curve by spline approach is constructed
using Pivotal Quantity. The regression models of interest is heteroskedasticity
spline nonparametric regression models. Therefore, it is necessary to give a
weight to overcome the heteroskedasticity
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTSt 519.536 Hen e |
| Uncontrolled Keywords: | Nonparametric Regression, Spline, Heteroskedasticity, Pivotal Quantity |
| Subjects: | Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49101-(S2) Master Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 04 Jun 2018 02:36 |
| Last Modified: | 04 Jun 2018 02:36 |
| URI: | http://repository.its.ac.id/id/eprint/51977 |
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