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| Title: | 二元時間序列分析：運用AIC準則選取gbAR模型的階數
Using AIC for Order Selection in a gbAR Model for Binary Time Series |
| Authors: | 黃詩涵
Huang, Shih-Han |
| Contributors: | 薛慧敏
Hsueh, Huey-Miin
黃詩涵
Huang, Shih-Han |
| Keywords: | 自迴歸
廣義二元自迴歸
二元時間序列
模型選取
赤池訊息量準則
gbAR
AR
Binary time series
Model selection
AIC |
| Date: | 2024 |
| Issue Date: | 2024-08-05 13:59:52 (UTC+8) |
| Abstract: | 時間序列分析是統計學中分析具有時間順序的資料點的方法，主要解釋資料趨勢和季節性變化，被廣泛應用於多個領域。近年來，除了一般常見的連續型態資料，在生醫、資訊、自然領域也可見類別型態時間序列資料。針對最簡化的類別型態時間序列—二元時間序列資料，Jentsch與Reichmann（2019）提出了廣義二元自迴歸（gbAR，generalized binary Autoregressive）模型及廣義二元自迴歸移動平均（gbARMA，generalized binary Autoregressive Moving Average）模型來描述前後觀測值的正負相關趨勢，他們在論文中主要介紹該模型的性質，以一實例說明模型估計的結果，但未深入研究模型估計的表現。我們針對gbAR模型提出兩種依據赤池信息量準則（AIC，Akaike Information Criterion）選取模型階數的方法：在第一個方法中，我們確實推導各階gbAR模型對應的AIC；第二個便捷的分析方法—將資料視為連續型時間序列並配適AR模型，以各階AR模型對應的AIC準則選取階數。透過模擬研究，我們發現雖然第一個方法在多數情況有較高準確率，但兩者差異不大。我們也透過一個實例來應用這兩個方法。最終，我們認為在時間有限的情況下，可以將二元時間序列資料直接配適AR模型，並利用現有的公開且免費的電腦計算套件選取階數。給定階數後，再在gbAR模型下進行模型配適、估計參數等資料分析。
Time series analysis is a statistical method for analyzing sequential data points over time. It helps in understanding data trends and seasonal changes and is widely applied to various fields. In recent years, in addition to continuous-type time series, categorical time series data has also gained prominence in biomedicine, information science, and the natural sciences. Specifically, binary time series is the simplest form. Jentsch and Reichmann (2019) propose the generalized binary autoregressive (gbAR) model and the generalized binary autoregressive moving average (gbARMA) model, which enable the description of positive and/or negative correlations between observations in a binary time series. In their study, the authors introduce the properties of these models. Except for providing an illustrative real example, they do not investigate the performance of statistical inference. In this study, we focus on the problem of order selection of the gbAR model. We propose two methods based on the Akaike Information Criterion (AIC) to evaluate their performance. In the first method, the AICs corresponding to various gbAR models are derived. In the second method, we naively treat the data as a continuous time series and select the order based on the AIC criterion corresponding to AR models. We compare the two methods through a simulation study. A real example is also provided for demonstration. We find that the first method has higher accuracy than the second one. However, the difference between the two methods is slight. In summary, we conclude that for order selection of a gbAR model, simply using the existing public computer packages developed for AR models can produce satisfactory results. |
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| Description: | 碩士
國立政治大學
統計學系
111354020 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111354020 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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