The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Statistical View of Some Chemometrics Regression Tools
Abstract
Chemometrics is a field of chemistry that studies the application of statistical methods to chemical data analysis. In addition to borrowing many techniques from the statistics and engineering literatures, chemometrics itself has given rise to several new data-analytical methods. This article examines two methods commonly used in chemometrics for predictive modeling—partial least squares and principal components regression—from a statistical perspective. The goal is to try to understand their apparent successes and in what situations they can be expected to work well and to compare them with other statistical methods intended for those situations. These methods include ordinary least squares, variable subset selection, and ridge regression.
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Cites
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“Models were developed using both well-known and innovative methods including partial least-squares (PLS) (Ståhle and Wold 1987; Wold et al. 2001), partial least-squares discriminant analysis (PLS-DA) (Frank and Friedman 1993; Nouwen et al. 1997), decision forest (DF) (Hong et al. 2005, 2004; Tong et al. 2003; Xie et a…”
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Metadata
- Authors
- Frank, L. E., & Friedman, J. H.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1993-05
- Volume
- 35
- Issue
- 2
- Pages
- 109-135
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W2079775628
- Cited by
- 2,213
- References
- 3
- FWCI
- 22.40
- Type
- journal article
- Language
- en
- Open access
- closed
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