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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.


IMPACT FACTOR increased in 2015: 1.265
5-year IMPACT FACTOR: 1.423
Rank 42 out of 123 in category Statistics & Probability in the 2015 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554
Impact per Publication (IPP) 2015: 1.061

Mathematical Citation Quotient (MCQ) 2015: 0.06

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1544-6115
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Autocorrelated Logistic Ridge Regression for Prediction Based on Proteomics Spectra

Jelle J Goeman1

1Leiden University Medical Center

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 7, Issue 2, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1344, February 2008

Publication History

Published Online:
2008-02-21

This paper presents autocorrelated logistic ridge regression, an extension of logistic ridge regression for ordered covariates that is based on the assumption that adjacent covariates have similar regression coefficients. The method is applied to the analysis of proteomics mass spectra.

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[1]
Yan-Fu Li, Min Xie, and Thong-Ngee Goh
Journal of Systems and Software, 2010, Volume 83, Number 11, Page 2332

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