Omnivariant Generalized Least Squares regression: Theory, geochronological applications, and making the case for reconciled ?47 calibrations
Omnivariant Generalized Least Squares regression: Theory, geochronological applications, and making the case for reconciled ?47 calibrations
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全变量广义最小二乘回归:理论、地质年代学应用以及协调 47 校准的案例
DOI:
10.1016/j.chemgeo.2023.121881
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发表时间:
2024
期刊:
影响因子:
3.9
通讯作者:
Daëron M
中科院分区:
文献类型:
--
作者:
Daëron M
Least-squares regression methods are mathematically powerful, conceptually and computationally simple, and widely used in many fields. However, none of the commonly-used flavors of least-squares regression, such as York regression or Generalized Least Squares (GLS), take into account the full set of covariances between all observed (x,y) values. Here we describe the Omnivariant Generalized Least Squares (OGLS) method to fit a model of the formy=f(x), accounting for the full error correlation structure of the (x,y) data, based on a first-order linear propagation of the uncertainties in all variables into errors inyresiduals, followed by minimizing the vector ofyresiduals with respect to the Mahalanobis norm defined by its covariance matrix. This approach may be described as a generalization of both York regression and GLS. It is mathematically exact for straight-line fits, and is also suitable for many non-linear models. Here we describe the principles of OGLS regression and discuss its properties, caveats, and practical use, and provide two consistent open-source implementations in Python and R. To illustrate how various fields of geochronology and stable-isotope geochemistry may benefit from this new method, we discuss how OGLS may specifically apply to40Ar/39Ar dating and how it provides robust mathematical evidence that Δ47carbonate calibrations in the recently defined I-CDES metrological scale are statistically indistinguishable, effectively solving long-standing methodological discrepancies.