BibTex Citation Data :
@article{Medstat5666, author = {Mika Asrini and Winita Sulandari and Santoso Wiyono}, title = {ESTIMASI PARAMETER MODEL MIXTURE AUTOREGRESSIVE (MAR) MENGGUNAKAN ALGORITMA EKSPEKTASI MAKSIMISASI (EM)}, journal = {MEDIA STATISTIKA}, volume = {6}, number = {1}, year = {2013}, keywords = {}, abstract = { v\:* \{behavior:url(#default#VML);\} o\:* \{behavior:url(#default#VML);\} w\:* \{behavior:url(#default#VML);\} .shape \{behavior:url(#default#VML);\} M ixture a utoregressive (MAR) Model is a mixture of Gaussian a utoregressive (AR) components. The mixture model is capable for modelling of nonlinear time series with multimodal conditional distributions. This paper discusses about the parameters estimation using EM algorithm. All possible models are then applied to national maize production data. In this case, the BIC is used for the MAR model selection. Keywords : M ixture A utoregressive, EM A lgorithm, BIC, M aize P roduction Normal 0 false false false IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable \{mso-style-name:\"Table Normal\"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:\"\"; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:\"Calibri\",\"sans-serif\"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:\"Times New Roman\"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;\} }, issn = {2477-0647}, pages = {21--26} doi = {10.14710/medstat.6.1.21-26}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/5666} }
Refworks Citation Data :
Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The mixture model is capable for modelling of nonlinear time series with multimodal conditional distributions. This paper discusses about the parameters estimation using EM algorithm. All possible models are then applied to national maize production data. In this case, the BIC is used for the MAR model selection.
Keywords : Mixture Autoregressive, EM Algorithm, BIC, Maize Production
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