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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 steadystate mean square error (MSE) than LMS. However, their high computational complexity (O(N2)) makes them unsuitable for many realtime applications. A wellknown 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 leastsquares 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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Mathematical statistics  Least squares.  Error analysis (Mathematics)  Least squares  #TELE:SISTA  Method of least squares  Squares, Least  Curve fitting  Geodesy  Mathematics  Probabilities  Triangulation  Errors, Theory of  Instrumental variables (Statistics)  Numerical analysis  Statistics
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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 PLSPM 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  Ebooks  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)  Numerical analysis  Statistics  Congresses
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Running Regressions introduces firstyear social science undergraduates, particularly those studying economics and business, to the practical aspects of simple regression analysis, without adopting an esoteric, mathematical approach. It shows that statistical analysis can be simultaneously straightforward, useful and interesting, and can deal with topical, realworld issues. Each chapter introduces an economic theory or idea by relating it to an issue of topical interest, and explains how data and econometric analysis can be used to test it. The book can be used as a selfstanding text or to supplement conventional econometric texts. It is also ideally suited as a guide to essays and project work.
Econometrics.  Least squares.  Regression analysis.  Analysis, Regression  Linear regression  Regression modeling  Multivariate analysis  Structural equation modeling  Method of least squares  Squares, Least  Curve fitting  Geodesy  Mathematical statistics  Mathematics  Probabilities  Triangulation  Economics, Mathematical  Statistics
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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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Mathematical statistics  Échantillonnage  Sampling  519.235  Least squares  Regression analysis  #TELE:SISTA  Analysis, Regression  Linear regression  Regression modeling  Multivariate analysis  Structural equation modeling  Method of least squares  Squares, Least  Curve fitting  Geodesy  Mathematics  Probabilities  Triangulation  Statistics of dependent variables. Contingency tables  Least squares.  Regression analysis.  519.235 Statistics of dependent variables. Contingency tables
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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
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Numerical analysis  Mathematical statistics  Equations, Simultaneous  Least squares  Moindres carrés  Numerical solutions  Least squares.  Numerical solutions.  519.65  Least squares  #TELE:SISTA  Method of least squares  Squares, Least  Curve fitting  Geodesy  Mathematics  Probabilities  Triangulation  Simultaneous equations  Approximation. Interpolation  519.65 Approximation. Interpolation  Moindres carrés  Numerical calculations  Calculs numériques  Moindres carrés.  Calculs numériques.  Analyse numérique.  Algèbre linéaire.  Algebras, Linear
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New Perspectives in Partial Least Squares and Related Methods shares original, peerreviewed research from presentations during the 2012 partial least squares methods meeting (PLS 2012). This was the 7th meeting in the series of PLS conferences and the first to take place in the USA. PLS is an abbreviation for Partial Least Squares and is also sometimes expanded as projection to latent structures. This is an approach for modeling relations between data matrices of different types of variables measured on the same set of objects. The twentytwo papers in this volume, which include three invited contributions from our keynote speakers, provide a comprehensive overview of the current state of the most advanced research related to PLS and related methods. Prominent scientists from around the world took part in PLS 2012 and their contributions covered the multiple dimensions of the partial least squaresbased methods. These exciting theoretical developments ranged from partial least squares regression and correlation, component based path modeling to regularized regression and subspace visualization. In following the tradition of the six previous PLS meetings, these contributions also included a large variety of PLS approaches such as PLS metamodels, variable selection, sparse PLS regression, distance based PLS, significance vs. reliability, and nonlinear PLS. Finally, these contributions applied PLS methods to data originating from the traditional econometric/economic data to genomics data, brain images, information systems, epidemiology, and chemical spectroscopy. Such a broad and comprehensive volume will also encourage new uses of PLS models in work by researchers and students in many fields.
Commercial statistics.  Economics  Statistical methods.  Social sciences  Statistical methods.  Mathematics  Physical Sciences & Mathematics  Mathematical Statistics  Least squares.  Mathematics.  Math  Method of least squares  Squares, Least  Statistics.  Statistical Theory and Methods.  Statistics, general.  Science  Curve fitting  Geodesy  Mathematical statistics  Probabilities  Triangulation  Mathematical statistics.  Statistical analysis  Statistical data  Statistical methods  Statistical science  Econometrics  Statistical inference  Statistics, Mathematical  Statistics  Sampling (Statistics)  Statistics .
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