Semiparametric Fractional Imputation Using Gaussian Mixture Models for Handling Multivariate Missing Data | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Semiparametric Fractional Imputation Using Gaussian Mixture Models for Handling Multivariate Missing Data

Sang, Hejian - Nama Orang; Kim, Jae Kwang - Nama Orang; Lee, Danhyang - Nama Orang;

Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation may be subject to bias under model misspecification. In this article, we propose a novel semiparametric fractional imputation (SFI) method using Gaussian mixture models. The proposed method is computationally efficient and leads to robust estimation. The proposed method is further extended to incorporate the categorical auxiliary information. The asymptotic model consistency and n−−√-consistency of the SFI estimator are also established. Some simulation studies are presented to check the finite sample performance of the proposed method. Supplementary materials for this article are available online.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art142952	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.538 June 2022
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 654-663
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: VARIANCE ESTIMATION
ROBUST ESTIMATION
ITEM NONRESPONSE
Info Detail Spesifik: https://doi.org/10.1080/01621459.2020.1796358
Pernyataan Tanggungjawab: Hejian Sang, Jae Kwang Kim, Danhyang Lee

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