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Unbiased Least-Squares Modelling [PDF]
Mathematics, 2020 In this paper we analyze the bias in a general linear least-squares parameter estimation problem, when it is caused by deterministic variables that have not been included in the model.
Marta Gatto, Fabio Marcuzzi
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Marginal Screening for Partial Least Squares Regression
IEEE Access, 2017 Partial least squares (PLS) regression is a versatile modeling approach for high-dimensional data analysis. Recently, PLS-based variable selection has attracted great attention due to high-throughput data reduction and modeling interpretability.
Naifei Zhao, Qingsong Xu, Hong Wang
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Quantum Regularized Least Squares [PDF]
Quantum, 2023 Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics.
Shantanav Chakraborty +2 more
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IEEE Access, 2021 Two M-decomposed based identification algorithms are proposed for large-scale systems in this study. Since the least squares algorithms involve matrix inversion calculation, they can be inefficient for large-scale systems whose information matrices are ...
Yuejiang Ji, Lixin Lv
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Projected Least-Squares Quantum Process Tomography [PDF]
Quantum, 2022 We propose and investigate a new method of quantum process tomography (QPT) which we call projected least squares (PLS). In short, PLS consists of first computing the least-squares estimator of the Choi matrix of an unknown channel, and subsequently ...
Trystan Surawy-Stepney +3 more
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Comparison between the Conventional Partial Least Squares (Pls) and the Robust Partial Least Squares (Rpls-Sem) Through Winsorization Approach [PDF]
Journal of Information Technology Management, 2022 This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used
GholamReza Zandi +3 more
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Partitioned least squares [PDF]
Machine Learning, 2019 AbstractLinear least squares is one of the most widely used regression methods in many fields. The simplicity of the model allows this method to be used when data is scarce and allows practitioners to gather some insight into the problem by inspecting the values of the learnt parameters.
Roberto Esposito +2 more
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Approximate least squares [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Preprint of the paper submitted to IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ...
Lunglmayr, Michael +2 more
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GM(1,1;λ) with Constrained Linear Least Squares
Axioms, 2021 The only parameters of the original GM(1,1) that are generally estimated by the ordinary least squares method are the development coefficient a and the grey input b.
Ming-Feng Yeh, Ming-Hung Chang
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Image magnification by least squares surfaces [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2017 Image magnification is one of the current issues of image processing in which keeping the quality and structure of images is the main concern. In im- age magnification, it is necessary to insert information in extra pixels.
Ali Mohammad Esmaili Zaini +2 more
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