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A COMPARISON OF M-ESTIMATION AND S-ESTIMATION ON THE FACTORS AFFECTING IR DHF IN EAST JAVA IN 2017

The Indonesian Journal of Public Health · Vol. 16 No. 3 (2021) · pp. 349–362

DOI
10.20473/ijph.v16i3.2021.349-362
ISSN
2540-8836, 1829-7005
Published
2021-11-30
Publisher
Universitas Airlangga
Language
eng

Abstract

ABSTRACTRobust regression on M estimation and S estimation is the Ordinary Least Square (OLS) regression on the data outlier. East Java is one of the provinces in Indonesia with a high case fatalitiy rate (1.34%). The raising of Dengue Haemoragic Fever (DHF) in East Java has been influenced by climate, population density, human behavior, and environmental sanitation. This study aimed to compare robust regression research by using M estimation and S estimation on the factors that affect IR DHF. This was done to get the best model regression on the data outlier based on the biggest R2 adjusted and the smallest MSE. This study utitlized observational research with a non-reactive research design using secondary data. The independent variable consisted of population density, healthy behavior, healthy living environment house, and precipitation in East Java in 2017. The dependent variable was incident rate of DHF in 2017. The population included 38 regencies in East Java, while the sample was 35 regencies/cities selected using simple random sampling. The analysis used robust regression on M estimation and S estimation weighting by Tukey's Bisquare. Robust regression on S estimation was found to be the best robust regression on data outlier with R2 adjusted (0.996) and MSE (0.229). Robust regression on S estimation was = 54.826 + 0.011 (population density) – 0.136 (% healthy behavior) - 0,404 (% healthy house ) - 0,005 (precipitation).Some factors that affect IR DHF can be the main focus for the prevention and control of DHF for the government and society. Keywords: robust regression, outlier, estimation, estimation, DHF

Keywords

Collaboration network of the authors

40 authors · 90 co-authorship links · 3 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Cenderawasih University
  • Universitas Airlangga
  • Universitas Raharja
  • University of Indonesia
  • Universitas Syiah Kuala
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Trias MahmudionoAffiliation unknown4
Soenarnatalina MelanianiUniversitas Airlangga4
Diah IndrianiUniversitas Airlangga4
Rachmah IndawatiAffiliation unknown2
Ika SartikaAffiliation unknown1
Resky Ana AbadiAffiliation unknown1
Ida Ayu Purba RianiCenderawasih University1
Charley M. BisaiCenderawasih University1
Evi NormalasariAffiliation unknown1
Ninda Atik NabilahAffiliation unknown1
Ritma Siwi MirantiAffiliation unknown1
Afifah Nur IndrastutiAffiliation unknown1
Galuh Nita PrameswariAffiliation unknown1
Aryantika Devi OctaviaAffiliation unknown1
Siti Lutfiyah UlfaAffiliation unknown1
Pupung Puspa ArdiniAffiliation unknown1
Irvin Novita ArifinAffiliation unknown1
Ninda LutfianiUniversitas Raharja1
Rivialdo Sultan SagaAffiliation unknown1
Arulita Ika FibrianaAffiliation unknown1
Retno Purnama IrawatiAffiliation unknown1
Eky Fadhildansyah AzharAffiliation unknown1
Sita Dewi MulyawatiAffiliation unknown1
Adhe Aprilia NurshaviraAffiliation unknown1
Defi FitrianingrumAffiliation unknown1
Yanesti Nuravianda LestariAffiliation unknown1
Shinta Amelia WardhaniAffiliation unknown1
Nurul MushthafiyahAffiliation unknown1
Widya LarasatiAffiliation unknown1
Siti Namira SalsabilaAffiliation unknown1
Anisa Nur RohmahUniversity of Indonesia1
Usman UsmanUniversitas Syiah Kuala1
Nur Sulis RamadaniAffiliation unknown1
Nensilianti NensiliantiAffiliation unknown1
Maya TrianaAffiliation unknown1
Shoofia Rihadhatul AisyAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/IJPH/oai, first harvested 10 Nov 2026, last seen 10 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A COMPARISON OF M-ESTIMATION AND S-ESTIMATION ON THE FACTORS AFFECTING IR DHF IN EAST JAVA IN 2017</dc:title>
	<dc:creator>Mardiana, Mardiana</dc:creator>
	<dc:creator>Wibowo, Arief</dc:creator>
	<dc:creator>Mahmudah, Mahmudah</dc:creator>
	<dc:creator>Festi W, Pipit</dc:creator>
	<dc:subject xml:lang="en-US">robust regression</dc:subject>
	<dc:subject xml:lang="en-US">outlier</dc:subject>
	<dc:subject xml:lang="en-US">estimation</dc:subject>
	<dc:subject xml:lang="en-US">DHF</dc:subject>
	<dc:description xml:lang="en-US">ABSTRACTRobust regression on M estimation and S estimation is the Ordinary Least Square (OLS) regression on the data outlier. East Java is one of the provinces in Indonesia with a high case fatalitiy rate (1.34%). The raising of  Dengue Haemoragic Fever (DHF) in East Java has been influenced by climate, population density, human behavior, and environmental sanitation. This study aimed to compare robust regression research by using M estimation and  S estimation on the factors that affect IR DHF. This was done to get the best model regression on the data outlier based on the biggest R2 adjusted and the smallest MSE. This study utitlized observational research with a non-reactive research design using secondary data. The independent variable consisted of population density, healthy behavior, healthy living environment house, and precipitation in East Java in 2017. The dependent variable was incident rate of DHF in 2017. The population included 38 regencies in East Java, while the sample was 35 regencies/cities selected using simple random sampling. The analysis used robust regression on M estimation and S estimation weighting by Tukey's Bisquare. Robust regression on S estimation was found to be the best robust regression on data outlier with R2 adjusted (0.996) and MSE (0.229). Robust regression on S estimation  was = 54.826 + 0.011 (population density) – 0.136 (% healthy behavior) - 0,404 (% healthy house ) - 0,005 (precipitation).Some factors that affect IR DHF can be the main focus for the prevention and control of DHF for the government and society.  Keywords: robust regression, outlier, estimation, estimation, DHF</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2021-11-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/IJPH/article/view/19000</dc:identifier>
	<dc:identifier>10.20473/ijph.v16i3.2021.349-362</dc:identifier>
	<dc:source xml:lang="en-US">The Indonesian Journal of Public Health; Vol. 16 No. 3 (2021): THE INDONESIAN JOURNAL OF PUBLIC HEALTH; 349-362</dc:source>
	<dc:source>2540-8836</dc:source>
	<dc:source>1829-7005</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/IJPH/article/view/19000/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 The Indonesian Journal of Public Health</dc:rights>
</oai_dc:dc>