Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models

R. Harald Baayen; Jacolien van Rij; Cécile de Cat; Simon Wood

doi:10.1007/978-3-319-69830-4

Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models

R. Harald Baayen, Jacolien van Rij, Cécile de Cat, Simon Wood

Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Original language	English
Title of host publication	Mixed-Effects Regression Models in Linguistics
Editors	Dirk Speelman, Kris Heylen, Dirk Geeraerts
Publisher	Springer International Publishing AG
Chapter	4
Pages	49-69
ISBN (Electronic)	978-3-319-69830-4
ISBN (Print)	978-3-319-69828-1
DOIs	https://doi.org/10.1007/978-3-319-69830-4
Publication status	Published - 2018

Publication series

Name	Quantitative Methods in the Humanities and Social Sciences
Publisher	Springer International Publishing AG
ISSN (Electronic)	2199-0956

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10.1007/978-3-319-69830-4

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Baayen, R. H., van Rij, J., de Cat, C., & Wood, S. (2018). Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models. In D. Speelman, K. Heylen, & D. Geeraerts (Eds.), Mixed-Effects Regression Models in Linguistics (pp. 49-69). (Quantitative Methods in the Humanities and Social Sciences). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-69830-4

Baayen, R. Harald ; van Rij, Jacolien ; de Cat, Cécile et al. / Autocorrelated errors in experimental data in the language sciences : Some solutions offered by Generalized Additive Mixed Models. Mixed-Effects Regression Models in Linguistics. editor / Dirk Speelman ; Kris Heylen ; Dirk Geeraerts. Springer International Publishing AG, 2018. pp. 49-69 (Quantitative Methods in the Humanities and Social Sciences).

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title = "Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models",

author = "Baayen, {R. Harald} and {van Rij}, Jacolien and {de Cat}, C{\'e}cile and Simon Wood",

year = "2018",

doi = "10.1007/978-3-319-69830-4",

language = "English",

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Baayen, RH, van Rij, J, de Cat, C & Wood, S 2018, Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models. in D Speelman, K Heylen & D Geeraerts (eds), Mixed-Effects Regression Models in Linguistics. Quantitative Methods in the Humanities and Social Sciences, Springer International Publishing AG, pp. 49-69. https://doi.org/10.1007/978-3-319-69830-4

Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models. / Baayen, R. Harald; van Rij, Jacolien; de Cat, Cécile et al.
Mixed-Effects Regression Models in Linguistics. ed. / Dirk Speelman; Kris Heylen; Dirk Geeraerts. Springer International Publishing AG, 2018. p. 49-69 (Quantitative Methods in the Humanities and Social Sciences).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Autocorrelated errors in experimental data in the language sciences

T2 - Some solutions offered by Generalized Additive Mixed Models

AU - Baayen, R. Harald

AU - van Rij, Jacolien

AU - de Cat, Cécile

AU - Wood, Simon

PY - 2018

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DO - 10.1007/978-3-319-69830-4

M3 - Chapter

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T3 - Quantitative Methods in the Humanities and Social Sciences

SP - 49

EP - 69

BT - Mixed-Effects Regression Models in Linguistics

A2 - Speelman, Dirk

A2 - Heylen, Kris

A2 - Geeraerts, Dirk

PB - Springer International Publishing AG

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Baayen RH, van Rij J, de Cat C, Wood S. Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models. In Speelman D, Heylen K, Geeraerts D, editors, Mixed-Effects Regression Models in Linguistics. Springer International Publishing AG. 2018. p. 49-69. (Quantitative Methods in the Humanities and Social Sciences). doi: 10.1007/978-3-319-69830-4