
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
  <dc:subject xml:lang="srp">KLJUČNE REČI: MULTIVARIJACIONI NEDOSTAJUĆI PODACI, MCAR TEST, EM ALGORITAM, OCENE (OGRANIČENE) MAKSIMALNE VERODOSTOJNOSTI</dc:subject>
  <dc:subject xml:lang="eng">KEY WORDS: MULTIVARIATE INCOMPLETE DATA, MCAR TEST, EM ALGORITHM, (RESTRICTED) MAXIMUM LIKELIHOOD ESTIMATES</dc:subject>
  <dc:identifier>https://phaidrabg.bg.ac.rs/o:28748</dc:identifier>
  <dc:identifier>ISSN: 2217-6217</dc:identifier>
  <dc:description xml:lang="srp">APSTRAKT:
Problem nedostajućih podataka dosta je prisutan kod anketnog istraživanja. Ukoliko se
ne utvrdi tip mehanizma nedostajućih podataka, ocene nepoznatih parametara analizi-
ranog statističkog modela, mogu biti pristrasne. Data neželjena osobina, može se prevazići pravilnim tretiranjem nedostajućih podataka, među kojima je svakako upotreba EM
algoritma.</dc:description>
  <dc:description xml:lang="eng">ABSTRACT:
The problem of missing data is quite present in the survey research. If the type of missing
data mechanism is not determined, the unknown parameter estimates of the analysed
statistical model may be biased. An unwanted feature may be overcome by properly han-
dling missing data, among which is the certainly using the EM algorithm.</dc:description>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:creator id="https://orcid.org/0000-0001-9694-8071 https://plus.cobiss.net/cobiss/sr/sr/conor/12793703">Vasić, Vladimir</dc:creator>
  <dc:date>2018</dc:date>
  <dc:language>srp</dc:language>
  <dc:rights>All rights reserved</dc:rights>
  <dc:source>Ekonomske ideje i praksa(30)</dc:source>
  <dc:title xml:lang="srp">Rešavanje problema multivarijacionih nedostajućih anketnih podataka primenom EM algoritma</dc:title>
  <dc:format>application/pdf</dc:format>
  <dc:format>179886 bytes</dc:format>
</oai_dc:dc>