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Partial Least Squares Path Modeling Basic Concepts, Methodological Issues and Applications
Now in its second edition, this edited book presents recent progress and techniques in partial least squares path modeling (PLS-PM), and provides a...
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Least Squares: Regression and ANOVA
Some fundamental concepts relating to linear models are introduced. Least squares estimation is discussed as a method for computing estimates of...
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Least Squares
In Sect. 10.6 we showed that the least squares (LS) method originates from the maximum likelihood...
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On Weighted Least Squares Estimators for Chirp Like Model
In this paper we have considered the chirp like model which has been recently introduced, and it has a very close resemblance with a chirp model. We...
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Weighted least squares for archetypal analysis with missing data
Archetypal analysis expresses observations in terms of a limited number of archetypes, defined as convex combinations of observed units. Such...
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Least squares estimation for the Ornstein–Uhlenbeck process with small Hermite noise
We consider the problem of the drift parameter estimation for a non-Gaussian long memory Ornstein–Uhlenbeck process driven by a Hermite process. To...
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Robust optimal subsampling based on weighted asymmetric least squares
With the development of contemporary science, a large amount of generated data includes heterogeneity and outliers in the response and/or covariates....
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On a projection least squares estimator for jump diffusion processes
This paper deals with a projection least squares estimator of the drift function of a jump diffusion process X computed from multiple independent...
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Large deviations for randomly weighted least squares estimator in a nonlinear regression model
In this work, we introduce the random weighting method to the nonlinear regression model and study the asymptotic properties for the randomly...
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Least Squares Estimators and Residuals Analysis
The main objective of this chapter is to introduce tools for analyzing the impact of the noise acting on the data sets, on the one hand, on the least...
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Trend and Seasonality Model Learning with Least Squares
In this chapter, a specific attention is paid to the determination of parametric models of the time series deterministic components: the trend and...
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Group least squares regression for linear models with strongly correlated predictor variables
Traditionally, the main focus of the least squares regression is to study the effects of individual predictor variables, but strongly correlated...
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Least squares estimation for a class of uncertain Vasicek model and its application to interest rates
This paper addresses statistical inference in uncertain differential equations, focusing on parameter estimation for a class of uncertain Vasicek...
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Least-squares estimators based on the Adams method for stochastic differential equations with small Lévy noise
We consider stochastic differential equations (SDEs) driven by small Lévy noise with some unknown parameters and propose a new type of least-squares...
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Alternative fixed-effects panel model using weighted asymmetric least squares regression
A fixed-effects model estimates the regressor effects on the mean of the response, which is inadequate to account for heteroscedasticity. In this...
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Software Packages for Partial Least Squares Structural Equation Modeling: An Updated Review
As a result of its ability to deal with situations that are difficult to address using other SEM methods, the partial least squares (PLS) approach to...
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Sparsifying the least-squares approach to PCA: comparison of lasso and cardinality constraint
Sparse PCA methods are used to overcome the difficulty of interpreting the solution obtained from PCA. However, constraining PCA to obtain sparse...
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Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements
This chapter aims to provide a brief overview of the three primary structural equation modeling approaches, which include partial least squares-path...
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Least-squares bilinear clustering of three-way data
A least-squares bilinear clustering framework for modelling three-way data, where each observation consists of an ordinary two-way matrix, is...
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Least-Squares Wavelet Analysis of Rainfalls and Landslide Displacement Time Series Derived by PS-InSAR
Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) data is a crucial step for monitoring the displacement of the Earth’s...