Local Regression Models

By Oliver B. Linton. 2010

953 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

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Citations in the corpus

"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, background): “We also show the ﬁtted LOESS curves (Cleveland, Grosse, and Shyu 1992).”
Ecosystem variability along the estuarine salinity gradient: Examples from long‐term study of San Francisco Bay (2017, methodology): “Smooth lines in plots are local regression fits (Cleveland et al. 1992) using the loess function in R (R Core Team 2014) with span 5 0.7 and degree 5 1, i.e., locally linear fits.”
The solution path of the generalized lasso (2010, methodology): “As another example, the inclusion of a design matrix X in the trend filtering setup provides an alternative way of fitting varying-coefficient models [8, 18].”
Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line (2010, methodology): “We used robust local polynomial regression fit in estimating function f(x) (Cleveland et al, 1992).”
Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals (2018, methodology): “The magenta line in each panel is a loess nonparametric regression smooth (Cleveland, Grosse, and Shyu 1992), using a span of 2/3 by default.”
Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling (2013, background): “…of the above issues, in recent years, the nonparametric varying/functional coefficient models have been considered by various authors, including Cleveland, Grosse, and Shyu (1992), Chen and Tsay (1993), Hastie and Tibshirani (1993), Fan and Zhang (1999), and Cai, Fan, and Yao (2000), among…”
Non‐stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil (2009, methodology): “The resulting monthly values were then interpolated for each calendar day using the Loess technique (Cleveland et al., 1992; Hipel and McLeod, 2005).”
Physiological influences can outweigh environmental signals in otolith microchemistry research (2014, methodology): “LOESS fits a polynomial model using a weighted least squares linear regression and a variable ‘span’ or window size (Cleveland et al. 1992).”
A variable selection approach to monotonic regression with Bernstein polynomials (2011, methodology): “The first is the local regression method as implemented by the loess function [13].”
Issues in Spatial Data Analysis (2010, methodology): “…estimates and standard errors, and degrees of freedom are preserved by confining nonparametric 10The conditionally parametric model is considered in detail by Cleveland, Grosse, and Shyu (1992) and Cleveland (1994), while still more general versions are analyzed in Hastie and Tibshirani (1993).”
Estimation And Hypothesis Testing For Nonparametric Hedonic House Price Functions (2010, neutral): “The model is considered in detail by Cleveland, Grosse, and Shyu (1992) and Cleveland (1994).”
Local enhancement in a seabird: reaction distances and foraging consequence of predator aggregations (2014, background): “The black lines show a local polynomial regression fitting of degree 2 (using loess algorithm implemented in R software by Cleveland et al. 1992) calculated with a span of 75% and the dashed gray lines show standard deviation of the regression.”