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Rudianto Artiono
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MATHunesa: Jurnal Ilmiah Matematika
Published by Universitas Negeri Surabaya
ISSN : 23019115     EISSN : 2716506X     DOI : https://doi.org/10.26740/mathunesa
Core Subject : Education,
Mathematics
MATHunesa is a mathematical scientific journal published by the Department of Mathematics, Faculty of Mathematics and Natural Sciences, The State University of Surabaya with e-ISSN 2716-506X and p-ISSN 2301-9115. This journal is published every four months in April, August, and December. One volume consists of three publication numbers. MATHunesa aims at providing a platform and encourages emerging scholars and academicians globally to share their professional and academic experiences to explore, but not limited to the following topics: 1. Analysis Mathematics, 2. Algebra, 3. Applied Mathematics, 4. Statistics, 5. Computation, 6. Combinatorics, and 7. Also giving an opportunity to show the power of innovation and finding new things in the field of mathematics. This journal was published online for the first time in 2013 as part of the graduation for students majoring in Mathematics at the State University of Surabaya.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
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Search results for , issue "Vol 1 No 2 (2013)" : 1 Documents clear
ESTIMASI MISSING DATA DALAM MULTIVARIAT BERDASARKAN DATA YANG TERAMATI Hutrisah SM Sitohang; I Ketut Budayasa
MATHunesa: Jurnal Ilmiah Matematika Vol 1 No 2 (2013)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (278.22 KB)

Abstract

Missing data adalah informasi yang tidak tersedia dalam sebuah subyek atau kasus. Fenomena missing data banyak dijumpai dalam survei. Banyak hal yang menyebabkan terjadinya missing data. Sehingga terkadang beberapa pihak tertentu mengabaikan, menghapus sebagian variabel yang mengandung missing. Mengingat data sangat mahal dan berharga, maka penelitian  ini, menduga parameter yang membuat fungsi likelihood maksimum untuk data yang seluruhnya teramati dan juga diamati sebanyak n anggota sebagai sampel (dengan n<N), dengan menggunakan data teramati maka nilai harapan bersyarat data tidak teramati  juga akan maksimum. Algoritma EM adalah salah satu metode yang menangani kasus missing. Ketika algoritma EM menghasilkan nilai penduga parameter maka penduga parameter tersebut merupakan nilai tunggal yang tetap.   Kata kunci : Missing data, Maksimum likelihood, Algoritma ekspektasi maksimum (EM).

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