e-journal

A Skew-Normal Mixture Regression Model

---

A challenge associated with traditional mixture regression models (MRMs), which rest on the assumption of normally distributed errors, is determining the number of unobserved groups. Specifically, even slight deviations from normality can lead to the detection of spurious classes. The current work aims to (a) examine how sensitive
the commonly used model selection indices are in class enumeration of MRMs with nonnormal errors, (b) investigate whether a skew-normal MRM can accommodate nonnormality, and (c) illustrate the potential of this model with a real data analysis.Simulation results indicate that model information criteria are not useful for class determination in MRMs unless errors follow a perfect normal distribution. The skewnormal MRM can accurately identify the number of latent classes in the presence of normal or mildly skewed errors, but fails to do so in severely skewed conditions.
Furthermore, across the experimental conditions it is seen that some parameter estimates provided by the skew-normal MRM become more biased as skewness increases whereas others remain unbiased. Discussion of these results in the context
of the applicability of skew-normal MRMs is provided.

Keywords
skew-normal distributions, mixture regression models, class identification, estimation bias

---

Ketersediaan

Tidak ada salinan data

Informasi Detail

Judul Seri

Educational and Psychological Measurement

No. Panggil

-

Penerbit

: The Author., 2014

Deskripsi Fisik

Educational and Psychological Measurement 2014, Vol 74(1) 139–162

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EKONOMI

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Wati/Agus

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar