WALD的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Handbook of Scan Statistics

為了解決WALD的問題，作者 這樣論述：

Joseph Glaz has been recently appointed (effective July 1, 2011) as head of the Department of Statistics, University of Connecticut. He has co-authored 6 books; 4 of which published with either Birkhauser Boston or Springer New York. Glaz is the current editor-in-chief of the following Springer jour

nal: Methodology and Computing in Applied Probability. Honors and Awards include: election to the Connecticut Academy of Arts and Sciences (2011), elected fellow of the Institute of Mathematical Sciences (2009), AAUP Excellence in Research Award (2006), Abraham Wald Prize in Sequential Analysis (200

6), elected fellow of the American Statistical Assoc. (2000), elected member of the International Statistical Institute (1999).Most importantly, Joe Glaz has worked closely with Joseph Naus, the father of scan statistics, since this area of statistics was conceived around 1999.Markos Koutras is curr

ently a full professor in the department of Statistics and Actuarial Science at the University of Piraeus in Greece and served as department chair 2003-2007. He received his PhD in mathematics from the University of Athens in 1983. Koutras serves as a highly active member of the editorial board of t

he Springer journal: Methodology and Computing in Applied Probability and has served on the organizing committee for several international conferences. He has served as referee/reviewer on at least 15 journals and book series. Prof. Koutras had been appointed institutional scientific director of the

modernization of the central library of the University of Piraeus and director of the Graduate Program in Applied Statistics (2001-2004; 2008-2011) at Piraeus.

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勞工高尿酸血症相關因子與機器學習預測評估

為了解決WALD的問題，作者邱昰桓 這樣論述：

研究背景：機器學習是一種不需顧慮變項間交互作用的一種疾病預測方法。而高尿酸血症是各種慢性疾病的生物指標之一，然而在2011年起我國政府所提供的常規健康檢查卻取消了尿酸的檢測。研究目的：透過機器學習的方法鑑別高尿酸血症的相關因子，並建立用於健康檢查的篩檢預測工具。研究方法：本研究數據來自於台灣北部某醫院的勞工健康檢查，使用迴歸分析找尋相關因子，並藉由迴歸係數估計因子對於尿酸的影響程度。機器學習部分則是利用10種之分類器，分別是羅吉斯迴歸、引導聚集算法、隨機森林、支持向量機、K-近鄰演算法、高斯單純貝氏分類器、梯度提升技術、自適應增強學習、極限梯度提升與多層感知器等十種分類器建立模型，並利用混淆

矩陣對模型的性能進行評估和比較，也使用Shapley additive explanation value (SHAP) 選擇重要特徵值。研究結果：3,667名參與者中有897 人患高尿酸血症，尿酸異常相關因子為年齡、性別、代謝症候群嚴重度指標、身體質量指數與肌酸酐。機器學習過程使用 4,319 筆經處理過的數據，利用SHAP值進行重要特徵值選擇，對尿酸異常的影響依序為肌酸酐與代謝症候群嚴重程度指標等。最佳模型為羅吉斯迴歸和梯度提升技術，兩者的模型的曲線下面積均為 0.78，若只使用排序前兩個主要特徵再次模型訓縣，其預測能力與原始模型相似。研究結論：高尿酸血症主要影響因子為肌酸酐及代謝症候群嚴

重度指標，我們可利用這兩個變項在健康檢查當中進行初步的高尿酸血症檢。本研究提供一種低成本且即時性的工具進行勞工高尿酸血症的預測，擴大研究對象、預測變數與進行前瞻性設計，並進一步提升模型鑑別能力。

Handbook of Scan Statistics

為了解決WALD的問題，作者 這樣論述：

Joseph Glaz has been recently appointed (effective July 1, 2011) as head of the Department of Statistics, University of Connecticut. He has co-authored 6 books; 4 of which published with either Birkhauser Boston or Springer New York. Glaz is the current editor-in-chief of the following Springer jour

nal: Methodology and Computing in Applied Probability. Honors and Awards include: election to the Connecticut Academy of Arts and Sciences (2011), elected fellow of the Institute of Mathematical Sciences (2009), AAUP Excellence in Research Award (2006), Abraham Wald Prize in Sequential Analysis (200

6), elected fellow of the American Statistical Assoc. (2000), elected member of the International Statistical Institute (1999).Most importantly, Joe Glaz has worked closely with Joseph Naus, the father of scan statistics, since this area of statistics was conceived around 1999.Markos Koutras is curr

ently a full professor in the department of Statistics and Actuarial Science at the University of Piraeus in Greece and served as department chair 2003-2007. He received his PhD in mathematics from the University of Athens in 1983. Koutras serves as a highly active member of the editorial board of t

he Springer journal: Methodology and Computing in Applied Probability and has served on the organizing committee for several international conferences. He has served as referee/reviewer on at least 15 journals and book series. Prof. Koutras had been appointed institutional scientific director of the

modernization of the central library of the University of Piraeus and director of the Graduate Program in Applied Statistics (2001-2004; 2008-2011) at Piraeus.

利率期限結構衡量貨幣政策對總體經濟之影響

為了解決WALD的問題，作者林侹丞 這樣論述：

本文以台灣地區2005年至2010年為研究期間，採Bu et al.(2020)之利率期限結構方法取得貨幣政策衝擊之估計量而後使用向量自回歸模型之衝擊反應函數，以探討通貨膨脹率、工業生產指數增長率、加權股價指數增長率以及貨幣政策衝擊間之動態影響關係。於結果可知，在該研究時段內經由因果檢定可得出，加權股價指數增長率顯著的影響工業生產指數增長率、通貨膨脹率以及貨幣政策衝擊，而後於向量自回歸模型中發現，貨幣政策對總體經濟變量的衝擊幅度不大。