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Journal ArticleDOI

A Note on Regression Methods in Calibration

E. J. Williams
- 01 Feb 1969 - 
- Vol. 11, Iss: 1, pp 189-192
TLDR
In this article, a note on regression methods in calibration is given, with a discussion of regression methods for calculating the parameters of a Calibration model with respect to a given set of parameters.
Abstract
(1969). A Note on Regression Methods in Calibration. Technometrics: Vol. 11, No. 1, pp. 189-192.

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Citations
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Journal ArticleDOI

Statistical Calibration: A Review

TL;DR: In this paper, a wide variety of approaches to both univariate and multivariate calibration are reviewed, including the classical, inverse, Bayesian and non-parametric approaches together with the approaches via tolerance regions.
Journal ArticleDOI

On the geometry of SNV and MSC

TL;DR: The spectral pre-treatments known as standard normal variate (SNV) and multiplicative scatter correction (MSC) often give very similar results, and are widely regarded as exchangeable as discussed by the authors.
Journal ArticleDOI

A primer on multivariate calibration

TL;DR: Several evolving multivariate calibration methods for analytical chemistry have been introduced in this article, and some important issues regarding their use are discussed in Section 2.2.1 and 3.1.
Journal ArticleDOI

Performance evaluation of two methods for online monitoring of linear calibration profiles

TL;DR: In this article, the authors compare the performance of two phase II monitoring schemes for linear profiles, one based on the classical calibration method monitoring the deviations from the regression line (referred to as the NIST method) and the second based on individually monitoring the parameters of the linear profile.
Journal ArticleDOI

Stature estimation and calibration: Bayesian and maximum likelihood perspectives in physical anthropology

TL;DR: It is shown that inverse calibration (regression of stature on bone length) is generally preferred when the stature distribution for a reference sample forms a reasonable prior, while classical calibration is preferred when there is reason to suspect that the estimated stature will be an extrapolation beyond the useful limits of the reference sample statures.
References
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Book

Applied Regression Analysis

TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI

Conditional Expectation and Unbiased Sequential Estimation

TL;DR: In this paper, it was shown that whenever there is a sufficient statistic and an unbiased estimate, not a function of $u$ only, for a parameter $\theta$, the function $E(t \mid u)$, which is a function function of u only, is an unbiased estimator with a variance smaller than that of $t.
Journal ArticleDOI

Classical and Inverse Regression Methods of Calibration

TL;DR: In this article, the classical and Inverse least squares methods of linear calibration are compared by Monte Carlo methods and the Inverse approach is found to be superior to the classical approach from a mean squared error point of view.
Journal ArticleDOI

The Probability Integral