Jun 29, 2020 · In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables.
Jul 15, 2024 · In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of ...
Nonparametric partitioning-based least squares regression estimation is an important method for estimating conditional expectation functions in statistics, ...
In this special case, the coefficients of the regression of y on X = [X1,X2] can be obtained from the separate regressions of y on X1 and y on X2.
Dec 31, 2023 · In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables.
Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, ...
Nov 12, 2019 · In this paper we propose a variant of the linear least squares model that allows practitioners to partition the input features into groups of ...
Partitioning of the sum of squared deviations into various components allows the overall variability in a dataset to be ascribed to different types or sources ...
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Abstract: Recursive least squares (RLS) is undoubtedly the most widely used procedure in the field of recursive parameter estimation.
Partitioned linear regression is a technique used to subdivide the independent variables in two groups and estimate their coefficients in two separate steps.