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Herni Utami
Jurusan Matematika, Fakultas MIPA Universitas Gajah Mada
Mathematics

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UJI LINEARITAS BERDASARKAN ESTIMASI MEAN DAN VARIANSI BERSYARAT UNTUK PROSES RUNTUN WAKTU Supriyanto Supriyanto; Herni Utami
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 1 No 1 (2009): Jurnal Ilmiah Matematika dan Pendidikan Matematika
Publisher : Jurusan Matematika FMIPA Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2009.1.1.2976

Abstract

This study aims to examine the benefits of testing linearity in the case of live test data. Bootstrap procedure is used to form the estimators of the statistics. Hypothetical form is used to follow the linear model. And compare the value of criticism from the distribution of this value with the test statistics that have been calculated based on the observed time series data existing. This procedure starts with a model determines autoregression to the data. By using the Akaike information criterion, order estimation obtained from the autoregression models.
UJI LINEARITAS BERDASARKAN ESTIMASI MEAN DAN VARIANSI BERSYARAT UNTUK PROSES RUNTUN WAKTU Supriyanto Supriyanto; Herni Utami
Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Vol 1 No 1 (2009): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2009.1.1.2976

Abstract

This study aims to examine the benefits of testing linearity in the case of live test data. Bootstrap procedure is used to form the estimators of the statistics. Hypothetical form is used to follow the linear model. And compare the value of criticism from the distribution of this value with the test statistics that have been calculated based on the observed time series data existing. This procedure starts with a model determines autoregression to the data. By using the Akaike information criterion, order estimation obtained from the autoregression models.
Co-Authors Supriyanto Supriyanto