What residualizing predictors in regression analyses does (and what it does not do)

Wurm, Lee H.; Fisicaro, Sebastiano A.

doi:10.1016/j.jml.2013.12.003

Cited by 147 publications

(131 citation statements)

References 34 publications

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“…More importantly, the model also confirms that yer deletion is significantly less acceptable when it creates tri-consonantal clusters, and when the cluster originates from a complex coda. Deletion is significantly more acceptable in disyllables, when the stem vowel is [o], and when the cluster ends ⁶The minimization of collinearity via predictor centering and residualization has been criticized by Wurm & Fisicaro (2014). When we ran the regression without centering and residualization, the result was very similar to the model we report in Table 6, except that vowel did not reach significance.…”

Section: Inferential Statisticssupporting

confidence: 52%

Source-Oriented Generalizations as Grammar Inference in Russian Vowel Deletion

Becker

Gouskova

2016

Linguistic Inquiry

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Speakers learn detailed generalizations about the morphophonology of their language, and extend them to nonce words. We propose a theory of this morphophonological knowledge that partitions the lexicon into uniform and productive sublexicons. Each sublexicon has its own phonotactic grammar, which the speaker uses as an inference mechanism to determine the relative productivity of each sublexicon. We report the results of an experiment on the generalization of mid vowel deletion ("yer" deletion) in Russian, showing that speakers encode source-oriented generalizations about the shapes of words that can undergo vowel deletion, as well as product-oriented generalizations about words that result from vowel deletion. An implementation of our model learns the patterns of deletion and captures both source-oriented and productoriented generalizations.*For their insightful questions, comments, and suggestions, we wish to thank

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Section: Inferential Statisticssupporting

confidence: 52%

Source-Oriented Generalizations as Grammar Inference in Russian Vowel Deletion

Becker

Gouskova

2016

Linguistic Inquiry

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“…Researchers may want to complement exploratory regression analysis with techniques from machine learning such as random forests (Breiman, 2001;Strobl et al, 2009) or gradient boosting machines (Friedman, 2001;Chen et al, 2015) to obtain an independent assessment of variable importance that is orthogonal to the exploration of the data with regression modeling. These techniques are not plagued by issues of collinearity (Wurm and Fisicaro, 2014), and they do not require any kind of model selection (for an example, see, e.g., Baayen et al, 2016a, for application of random forests for this purpose).…”

Section: Exploratory Data Analysismentioning

confidence: 99%

The cave of shadows: Addressing the human factor with generalized additive mixed models

Baayen

Vasishth

Kliegl

et al. 2017

Journal of Memory and Language

199

201

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Generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data. This autocorrelational structure is likely to be a consequence of learning, fatigue, or the ebb and flow of attention within an experiment (the 'human factor'). Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival on constant adaptation to their environment, including the environment of an experiment. Three data sets illustrate that the human factor may interact with predictors of interest, both factorial and metric. We also show that, especially within the framework of the generalized additive model, in the nonlinear world, fitting maximally complex models that take every possible contingency into account is ill-advised as a modeling strategy. Alternative modeling strategies are discussed for both confirmatory and exploratory data analysis.

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“…Another issue is residualization. Recent study suggests that residualization, counter to the common recognition in the literature, does not change the result for the residualized variable but the variable which is residualized against (Wurm and Fisicaro, 2014). This indicates that more attention should be given to the predictor age, against which we did the residualization.…”

Section: Limitationsmentioning

confidence: 66%

Predicting tonal realizations in one Chinese dialect from another

Chen

Heuven

et al. 2016

Speech Communication

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Pronunciation dictionaries are usually expensive and time-consuming to prepare for the computational modeling of human languages, especially when the target language is under-resourced. Northern Chinese dialects are often under-resourced but used by a significant number of speakers. They share the basic sound inventories with Standard Chinese (SC). Also, their words usually share the segmental realizations and logographic written forms with the SC translation equivalents. Hence the pronunciation dictionaries of northern Chinese dialects could be easily available if we were able to predict the tonal realizations of the dialect words from the tonal information of their SC counterparts. This paper applies statistical modeling to investigate the tonal aspect of the related words between a northern dialect, i.e. Jinan Mandarin (JM), and Standard Chinese (SC). Multi-linear regression models were built with between-word pitch distance of JM words as the dependent variable and the following were included as the predictors: SC tonal relations, between-dialect tonal identity, and individual backgrounds. The results showed that tonal relations in SC and between-dialect identity, as predictors featuring the relation between the JM and SC tonal systems, are significant and robust predictors of JM tonal realizations. The speakers' sociolinguistic and cognitive backgrounds, together with the tonal merge and neutral tone information within JM, are important for the prediction of JM tonal realizations and affect the way that between-language predictors take effect.

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What residualizing predictors in regression analyses does (and what it does not do)

Cited by 147 publications

References 34 publications

Source-Oriented Generalizations as Grammar Inference in Russian Vowel Deletion

Source-Oriented Generalizations as Grammar Inference in Russian Vowel Deletion

The cave of shadows: Addressing the human factor with generalized additive mixed models

Predicting tonal realizations in one Chinese dialect from another

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