ベイジアンARIMAモデルを用いたインドにおける幼児死亡率のタイムシリーズ予測
Anuj Singh1, Tripti Tripathi2, Rakesh Ranjan2
1Department of Sciences (Mathematics), Indian Institute of Information Technology Ranchi, Jharkhand, India. anujsingh11185@gmail.com.
BMC public health
|August 21, 2025
まとめ
ベイジアンARIMAモデルを用いた乳児死亡率 (IMR) 分析は,IMRの安定した低下を予測している. この研究は,人口予測と公衆衛生計画における先進的な統計学的方法の有効性を強調しています.
科学分野:
- 人口統計
- バイオ統計学
- 公衆衛生
背景:
- 乳児死亡率 (IMR) は,国の健康と社会経済状態の重要な指標です.
- IMRの評価は,母と子供の福祉の評価に不可欠です.
- タイムシリーズ分析は 人口動向を理解するための貴重なツールです
研究 の 目的:
- 幼児死亡率のデータを自動回帰集積移動平均 (ARIMA) モデルを使って分析する.
- ARIMAモデルのパラメータの古典的およびベイジアン推定方法を比較する.
- 幼児死亡率の将来的な傾向を先端の統計技術を用いて予測する.
主な方法:
- カルマンフィルタリングを用いて,ARIMAモデルの確率を推定した.
- ランダム・ウォーク・メトロポリスのアルゴリズムで ベイジアン分析を行いました
- AIC,BIC,K-fold クロス・バリデーションを使用して最高のARIMAモデルを選択しました.
主要な成果:
- ARIMA ((5,1,0) モデルは,インドのIMRデータ (1950年−2023年) に最も適していると考えられた.
- ベイジアン推定とカルマンフィルタリングは,堅固なパラメータ推定を提供しました.
- 2024年から2033年の間,IMRは一貫して減少すると予測されています.
結論:
- ベイジアンARIMAモデリングは人口予測に有効です.
- この研究は,公衆衛生計画におけるこれらの方法の有用性を示しています.
- 正確なIMR予測は,標的を絞った健康介入と政策開発を支援します.
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