Statistical-analysis of Spectra From Electron-spectroscopy for Chemical-analysis
(1994) Royal Statistical Society. Journal. Series D: The Statistician — Vol. 43, n° 1, p. 111-127 (1994)
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- Abstract
- Electron spectroscopy for chemical analysis is a key technique in the study of modified material surfaces. Analysis of the resulting spectra consists in decomposing multiple peaks into subpeaks, whose functional form is known up to a few parameters. Statistical inference consists in estimating these parameters and derived quantities, such as peak area ratios, and in assessing the accuracies of these estimates. Purely likelihood-based peak decomposition is notorious for problems of identifiability. In practice, however, additional knowledge exists about some of the parameters and can, when incorporated in the model as an informative prior, produce unique decompositions. Modem tools for Bayesian statistics, such as profile diagnostics, Laplacian approximations of marginals and Markov chain algorithms for sampling from the posterior, can then be used to obtain inference reaching beyond point estimates and approximate standard errors.
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Ritter, C. (1994). Statistical-analysis of Spectra From Electron-spectroscopy for Chemical-analysis. Royal Statistical Society. Journal. Series D: The Statistician, 43(1), 111-127. https://doi.org/10.2307/2348937 (Original work published 1994)