THE COMPARISON OF TWO METHOD NONPARAMETRIC APPROACH ON SMALL AREA ESTIMATION (CASE: APPROACH WITH KERNEL METHODS AND LOCAL POLYNOMIAL REGRESSION)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

RATNANINGSIH DEWI JULIAH; FRIDAYANTI FIA; MEGAWARNI ANDI

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF MATHEMATICAL MODELLING & COMPUTATION Year:2014 | Volume:4 | Issue:2 Page(s): 115-123

Download Full-Text

View:

306

Download:

Cites:

Information Journal Paper

Title

THE COMPARISON OF TWO METHOD NONPARAMETRIC APPROACH ON SMALL AREA ESTIMATION (CASE: APPROACH WITH KERNEL METHODS AND LOCAL POLYNOMIAL REGRESSION)

Author(s)

RATNANINGSIH DEWI JULIAH | FRIDAYANTI FIA | MEGAWARNI ANDI | Issue Writer Certificate

Keywords

SMALL AREA ESTIMATION

NONPARAMETRIC METHODS

KERNEL METHODS

AND LOCAL POLYNOMIAL REGRESSION

Abstract

SMALL AREA ESTIMATION is a technique used to estimate parameters of subpopulations with small sample sizes. SMALL AREA ESTIMATION is needed in obtaining information on a small area, such as sub-district or village. Generally, in some cases, SMALL AREA ESTIMATION uses parametric modeling. But in fact, a lot of models have no linear relationship between the small area average and the covariate. This problem requires a non-parametric approach to solve, such as Kernel approach AND LOCAL POLYNOMIAL REGRESSION (LPR). The purpose of this study is comparing the results of SMALL AREA ESTIMATION using Kernel approach and LPR. Data used in this study are generated by simulation results using R language . Simulation data obtained by generating function m (x) are linear and quadratic pattern. The criteria used to compare the results of the simulation are Absolute Relative Bias (ARB), Mean Square Error (MSE), Generalized Cross Validation (GCV), and risk factors. The simulation results showed: 1) Kernel gives smaller relative bias than LPR does on both linear and quadratic data pattern. The relative bias obtained by Kernel tends to be more stable and consistent than the relative bias resulted by LPR, (2) the Kernel MSE is smaller than the LPR MSE either on linear or quadratic pattern in any combination treatment, (3) the value of GCV and the risk factors in Kernel are smaller than these in LPR in any combination of the simulated data patterns, (4) on non parametric data, for both linear data pattern and quadratic data pattern, KERNEL METHODS provide better estimation compared to LPR.

Cites

No record.

References

Cite

APA: Copy

RATNANINGSIH, DEWI JULIAH, FRIDAYANTI, FIA, & MEGAWARNI, ANDI. (2014). THE COMPARISON OF TWO METHOD NONPARAMETRIC APPROACH ON SMALL AREA ESTIMATION (CASE: APPROACH WITH KERNEL METHODS AND LOCAL POLYNOMIAL REGRESSION). INTERNATIONAL JOURNAL OF MATHEMATICAL MODELLING & COMPUTATION, 4(2), 115-123. SID. https://sid.ir/paper/328327/en

Vancouver: Copy

RATNANINGSIH DEWI JULIAH, FRIDAYANTI FIA, MEGAWARNI ANDI. THE COMPARISON OF TWO METHOD NONPARAMETRIC APPROACH ON SMALL AREA ESTIMATION (CASE: APPROACH WITH KERNEL METHODS AND LOCAL POLYNOMIAL REGRESSION). INTERNATIONAL JOURNAL OF MATHEMATICAL MODELLING & COMPUTATION[Internet]. 2014;4(2):115-123. Available from: https://sid.ir/paper/328327/en

IEEE: Copy

DEWI JULIAH RATNANINGSIH, FIA FRIDAYANTI, and ANDI MEGAWARNI, “THE COMPARISON OF TWO METHOD NONPARAMETRIC APPROACH ON SMALL AREA ESTIMATION (CASE: APPROACH WITH KERNEL METHODS AND LOCAL POLYNOMIAL REGRESSION),” INTERNATIONAL JOURNAL OF MATHEMATICAL MODELLING & COMPUTATION, vol. 4, no. 2, pp. 115–123, 2014, [Online]. Available: https://sid.ir/paper/328327/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops