エントロピーと複雑性の同時漸近分布
Angelika Silbernagel1, Christian H Weiß1
1Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany.
Chaos (Woodbury, N.Y.)
|February 23, 2026
まとめ
この研究は、弱い依存性下での時系列における順序パターン頻度とエントロピー複雑性を分析します。複雑なデータ分析におけるシリアル依存性テストと不確実性の推定のための新しい方法を提供します。
科学分野:
- 時系列分析
- 統計的推論
- 情報理論
背景:
- 順序パターンは時系列分析において重要です。
- エントロピー複雑性尺度は時系列ダイナミクスを要約します。
- 漸近分布の理解は統計的推論の鍵となります。
研究 の 目的:
- 順序パターン頻度とエントロピー複雑性の漸近分布を導出すること。
- さまざまなプロセスに対する長期共分散行列を調査すること。
- シリアル依存性テストを開発および評価すること。
主な方法:
- 漸近分布理論。
- 移動平均、ガウス、一般化コイン投げプロセスに対する分析的導出。
- シミュレーションベースの近似と有限サンプル性能チェック。
主要な成果:
- 順序パターン頻度とエントロピー複雑性ペアの漸近分布を確立しました。
- 分析的および近似的な長期共分散行列を導出しました。
- 有限サンプル性能を評価したシリアル依存性テストを開発しました。
結論:
- 導出された漸近結果は、時系列特性の理解に役立ちます。
- この研究は、シリアル依存性テストと不確実性推定のためのツールを提供します。
- この研究は、複雑な時系列データを分析するための統計的手法を進歩させます。
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