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Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity (O(N2)) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful.

Adaptive filters --- Least squares. --- Method of least squares --- Squares, Least --- Curve fitting --- Geodesy --- Mathematical statistics --- Mathematics --- Probabilities --- Triangulation --- Filters, Adaptive --- Electric filters --- Design and construction.

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Ten chapters discuss key aspects of advanced PLS analysis and its practical applications, covering new guidelines and improvements in the use of PLS-PM as well as various individual topics.

Least squares. --- Structural equation modeling. --- SEM (Structural equation modeling) --- Multivariate analysis --- Factor analysis --- Regression analysis --- Path analysis (Statistics) --- Method of least squares --- Squares, Least --- Curve fitting --- Geodesy --- Mathematical statistics --- Mathematics --- Probabilities --- Triangulation --- Hospitality industry --- Research --- E-books --- Service industries

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Error analysis (Mathematics) --- Least squares --- Academic collection --- #TELE:SISTA --- Method of least squares --- Squares, Least --- Curve fitting --- Geodesy --- Mathematical statistics --- Mathematics --- Probabilities --- Triangulation --- Errors, Theory of --- Instrumental variables (Statistics) --- Mathematical statistics --- Numerical analysis --- Statistics --- Congresses --- Congresses

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Mathematical statistics --- Least squares --- Linear models (Statistics) --- Proof theory --- Mathematical statistics. --- Least squares. --- Proof theory. --- Logic, Symbolic and mathematical --- Models, Linear (Statistics) --- Mathematical models --- Statistics --- Method of least squares --- Squares, Least --- Curve fitting --- Geodesy --- Mathematics --- Probabilities --- Triangulation --- Statistical inference --- Statistics, Mathematical --- Sampling (Statistics) --- Statistical methods

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Operational research. Game theory --- Moindres carrés --- Least squares --- Academic collection --- #TELE:SISTA --- 519.6 --- Method of least squares --- Squares, Least --- Curve fitting --- Geodesy --- Mathematical statistics --- Mathematics --- Probabilities --- Triangulation --- Computational mathematics. Numerical analysis. Computer programming --- Least squares. --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- Moindres carrés