東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Mixed-effects regression models in l...

Speelman, Dirk.

Linked to FindBook

Google Book

Amazon

博客來

Mixed-effects regression models in linguistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mixed-effects regression models in linguistics/ edited by Dirk Speelman, Kris Heylen, Dirk Geeraerts.
other author:	Speelman, Dirk.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	vii, 146 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Mixed Models with Emphasis on Large Data Sets -- Chapter 3. The L2 Impact on Learning L3 Dutch: The L2 Distance Effect Job -- Chapter 4. Autocorrelated Errors in Experimental Data in the Language Sciences: Some Solutions Offered by Generalized Additive Mixed Models -- Chapter 5. Border Effects Among Catalan Dialects -- Chapter 6. Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion -- Chapter 7. (Non)metonymic Expressions for Government in Chinese: A Mixed-Effects Logistic Regression Analysis.
Contained By:	Springer eBooks
Subject:	Linguistics - Statistical methods. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-69830-4
ISBN:	9783319698304

Mixed-effects regression models in linguistics
Mixed-effects regression models in linguistics[electronic resource] /edited by Dirk Speelman, Kris Heylen, Dirk Geeraerts. - Cham :Springer International Publishing :2018. - vii, 146 p. :ill. (some col.), digital ;24 cm. - Quantitative methods in the humanities and social sciences,2199-0956. - Quantitative methods in the humanities and social sciences..

Chapter 1. Introduction -- Chapter 2. Mixed Models with Emphasis on Large Data Sets -- Chapter 3. The L2 Impact on Learning L3 Dutch: The L2 Distance Effect Job -- Chapter 4. Autocorrelated Errors in Experimental Data in the Language Sciences: Some Solutions Offered by Generalized Additive Mixed Models -- Chapter 5. Border Effects Among Catalan Dialects -- Chapter 6. Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion -- Chapter 7. (Non)metonymic Expressions for Government in Chinese: A Mixed-Effects Logistic Regression Analysis.

When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.

ISBN: 9783319698304

Standard No.: 10.1007/978-3-319-69830-4doiSubjects--Topical Terms:

815506
Linguistics
--Statistical methods.

LC Class. No.: P138.5

Dewey Class. No.: 410.151

Mixed-effects regression models in linguistics
LDR:02979nmm a2200301 a 4500 001 2134082
003 DE-He213
005 20180207150927.0
006 m d
007 cr nn 008maaau
008 181005s2018 gw s 0 eng d
020 $a 9783319698304 $q (electronic bk.)
020 $a 9783319698281 $q (paper)
024 7 $a 10.1007/978-3-319-69830-4 $2 doi
035 $a 978-3-319-69830-4
040 $a GP $c GP
041 0 $a eng
050 4 $a P138.5
082 0 4 $a 410.151 $2 23
090 $a P138.5 $b .M685 2018
245 0 0 $a Mixed-effects regression models in linguistics $h [electronic resource] / $c edited by Dirk Speelman, Kris Heylen, Dirk Geeraerts.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a vii, 146 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Quantitative methods in the humanities and social sciences, $x 2199-0956
505 0 $a Chapter 1. Introduction -- Chapter 2. Mixed Models with Emphasis on Large Data Sets -- Chapter 3. The L2 Impact on Learning L3 Dutch: The L2 Distance Effect Job -- Chapter 4. Autocorrelated Errors in Experimental Data in the Language Sciences: Some Solutions Offered by Generalized Additive Mixed Models -- Chapter 5. Border Effects Among Catalan Dialects -- Chapter 6. Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion -- Chapter 7. (Non)metonymic Expressions for Government in Chinese: A Mixed-Effects Logistic Regression Analysis.
520 $a When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.
650 0 $a Linguistics $x Statistical methods. $3 815506
650 0 $a Regression analysis. $3 529831
650 1 4 $a Statistics. $3 517247
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 894294
650 2 4 $a Semantics. $3 520060
650 2 4 $a Syntax. $3 897045
700 1 $a Speelman, Dirk. $3 3302200
700 1 $a Heylen, Kris. $3 3302201
700 1 $a Geeraerts, Dirk. $3 3302202
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Quantitative methods in the humanities and social sciences. $3 2072013
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-69830-4
950 $a Mathematics and Statistics (Springer-11649)