Loess: a nonparametric, graphical tool for depicting relationships between variables

By William G. Jacoby. 2000

626 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/261a930e2a889f6db35b01d06d51c78b3adc8172

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Citations in the corpus

A dose of nature: Tree cover, stress reduction, and gender differences (2014, methodology): “LOESS is a simple but powerful method for fitting smooth curves to quantitative data (Jacoby, 2000).”
Meta-analysis and dose-response analysis of high temperature effects on rice yield and quality (2017, background): “The theory and step-by-step protocol for loess fitting can be found in Cleveland et al. (1988) and Jacoby (2000), respectively.”
Urban nature in a time of crisis: recreational use of green space increases during the COVID-19 outbreak in Oslo, Norway (2020, methodology): “We performed Loess regressions (Jacoby 2000) of recreational activity changes on environmental covariates to explore non-linear responses.”
A taxonomic revision of Phytophthora Clade 5 including two new species, Phytophthora agathidicida and P. cocois (2015, methodology): “Optimum temperatures were calculated by taking the maximum point on a loess curve (“local regression”, Jacoby 2000) fitted to the data.”
Ideology, motivated reasoning, and cognitive reflection (2013, methodology): “This technique, which traces the profile of the data in detail without making any particular assumptions about their distribution, is an appropriate device both for graphical summary assessments of the raw data and for determining the appropriate form (linear or curvilinear) of the statistical model”
ASSESSING THE JOB POLARIZATION EXPLANATION OF GROWING WAGE INEQUALITY (2013, background): “Jacoby (2000) notes that a summary fit statistic of this type “cannot, strictly speaking, be interpreted as variance explained because the loess fitting procedure does not partition the total sum of squares in Y neatly into additive components representing the sums of squares in the fitted values…”