Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Khung dự đoán trong mô hình độ trễ phân phối với hàm mục tiêu: ứng dụng cho dữ liệu biến đổi khí hậu toàn cầu
Tóm tắt
Do bản chất của mô hình độ trễ phân phối, các nhà nghiên cứu có thể gặp phải vấn đề đa cộng tuyến trong mô hình này. Các kỹ thuật ước lượng có thiên lệch, trong đó có ước lượng Almon ridge, được xem xét như một sự thay thế cho ước lượng Almon với mục tiêu phục hồi đa cộng tuyến. Mặc dù hiệu suất ước lượng thường được xem xét, nhưng hiệu suất dự đoán hiếm khi được xử lý trong mô hình độ trễ phân phối. Mục tiêu chính của bài báo này là điều tra hiệu suất dự đoán của mô hình độ trễ phân phối thông qua hàm mục tiêu. Trong bối cảnh này, chúng tôi sử dụng ước lượng Almon ridge để xác định một biến dự đoán mới có tính chống chịu cao hơn với đa cộng tuyến. Để phân tích một cách toàn diện về vấn đề dự đoán trong mô hình độ trễ phân phối, chúng tôi tập trung vào các kết quả lý thuyết và so sánh. Sau đó, vấn đề xác định các tham số tối ưu được xem xét thông qua việc giảm thiểu sai số bình phương dự đoán. Phân tích số liệu dựa trên dữ liệu biến đổi khí hậu toàn cầu được xem xét để xác thực các kết quả lý thuyết của chúng tôi. Hơn nữa, một thí nghiệm Monte Carlo được thực hiện để đánh giá khả năng dự đoán của các ước lượng.
Từ khóa
#mô hình độ trễ phân phối #đa cộng tuyến #ước lượng Almon ridge #hiệu suất dự đoán #hàm mục tiêu #sai số bình phương dự đoánTài liệu tham khảo
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