Karakteristik Estimator Analisis Komponen Utama untuk Mengestimasi Model Variabel Laten Menggunakan Metode High-Dimensional AIC
Keywords:
AKU, HAIC, Model Variabel LatenAbstract
This paper aims to determine the properties of Principle Component Analysis (PCA) estimator to estimate latent variable models. The method used is the High-Dimensional AIC (HAIC) method with simulation of Bernoulli distribution data. Stages are: (1) determine the matrix PCA; (2) create a model of the PCA estimator to estimate the latent variables by using HAIC; (3) simulated the Bernoulli distribution data with repetition 1,000,748 times. The simulation results show that the PCA estimator models work well.
ABSTRAK
Makalah ini bertujuan untuk mengetahui sifat estimator Analisis Komponen Utama (AKU) untuk mengestimasi model variabel laten. Metode yang digunakan adalah metode High-Dimensional AIC (HAIC) dengan simulasi data berdistribusi Bernoulli. Tahapannya adalah: (1) menentukan matriks AKU; (2) membuat model estimator AKU untuk mengestimasi variabel laten dengan menggunakan HAIC; (3) mensimulasikan data distribusi Bernoulli dengan pengulangan 1.000.748 kali. Hasil simulasi menunjukkan model estimator AKU bekerja dengan baik.
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