逐次最小二乗法（RLS: Recursive Least Squares）ーパラメータ同定に用いられる時系列データの回帰手法！ー

最小二乗法を用いた回帰分析ー②重回帰分析と非線形回帰分析ー【Pythonプログラム付】

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4.6.10. In many least squares applications, the underlying data matrix Am × n does not have independent columns—i.e., rank(A) ∈ℛ(A^T), and if x = is any least squares solution (i.e., A^T Ax = A^T b), prove that the estimate defined byŷ = t^T x = ∑i=1^n ti âiis unique in the sense that ŷ is independent of which least squares solution x is used.

Problem Solving: Least Squares Approximation | Linear Algebra | Mathematics | MIT OpenCourseWare

Lecture 9: Four Ways to Solve Least Squares Problems | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Mathematics | MIT OpenCourseWare