Local Regression and Likelihood

By Guohua Pan, Catherine Loader. 1999

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Citations in the corpus

Local Linear Forests (2018, methodology): “…helpful to use a local regression adjustment to correct for potential misalignment between a test point and its neighborhood (Stone 1977; Tibshirani and Hastie 1987; Cleveland and Devlin 1988; Newey 1994; Fan and Gijbels 1996; Heckman, Ichimura, and Todd 1998; Loader 1999; Abadie and Imbens 2011).”
Generalized random forests (2016, methodology): “This problem has also been considered in detail by Meinshausen (2006), who proposed a consistent forest-based quantile regression algorithm; his method also fits into the paradigm of solving estimating equations (5) using random forest weights (6).”
Memory-based Parameter Adaptation (2018, methodology): “Finally, we mention that our work is closely related to the local regression and adaptive coefﬁcient models literature, see Loader (2006) and references therein.”
Probit Transformation for Nonparametric Kernel Estimation of the Copula Density (2014, methodology): “Then, h Q can be selected via cross-validation (Loader, 1999, Section 5.3.3) as where, as usual in cross-validation procedures, the observations but ˆ Q i .”
Statistical Analysis of a Telephone Call Center (2005, unknown): “In practice we used LOCFIT (Loader, 1999), though other techniques, such as kernel procedures or smoothing splines, would yield similar fits. In particular, we have experimented with HEFT (Kooperberg, Stone and Truong, 1995), as can be seen in Mandelbaum et al. (2000). The smoothing bandwidth was ch”
Flexible Copula Density Estimation with Penalized Hierarchical B‐splines (2013, methodology): “Copulas have experienced general interest in the last years, primarily in the area of finance; see, for instance, McNeil et al. (2009), although the idea has been applied in other contexts as well; see, for example, Bogaerts & Lesaffre (2008) or Song et al. (2009) for biostatistical applications or ”
Testing the Accuracy of Regression Discontinuity Analysis Using Experimental Benchmarks (2009, background): “Ludwig and Miller (2007) and Imbens and Lemieux (2008) have proposed a ‘‘leave one out’’ cross-validation procedure aimed specifically at estimating the regression function at the cutoff. To see howwell a local linear regression with bandwidth h fits the data, they run a local linear regression for ”
Sampling Properties of the Spectrum and Coherency of Sequences of Action Potentials (2000, background): “Choice of bandwidth is a subject on which a considerable literature exists and which is beyond the scope of this article (Loader, 1999).”
The Generalized Cross Entropy Method, with Applications to Probability Density Estimation (2011, methodology): “More importantly, as pointed out in [36], to derive the asymptotic rates of convergence of the estimator one makes incompatible assumptions about the smoothness of the underlying unknown density.”
Correlated Label Propagation with Application to Multi-label Learning (2006, methodology): “A number of machine learning methods have been developed for label propagation, including kernel-based K Nearest Neighbor (kNN) [14], Gaussian processes [23, 4], harmonic functions [29], and Green functions [27].”
Spatial and temporal variability in somatic growth of green sea turtles (Chelonia mydas) resident in the Hawaiian Archipelago (2004, methodology): “These local regression models were fitted using the locfit() library (Loader 1999) for either SPLUS or R.”
Locally weighted polynomial regression: Parameter choice and application to forecasts of the Great Salt Lake (2006, methodology): “Recently [e.g., Loader, 1999; Hastie and Loader, 1993; Fan and Gijbels, 1992; Müller, 1987; Lejeune, 1985], these methods have been recognized as very useful generalizations of kernel regression (weighted moving averages).”
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation (2016, background): “samples is missing in the theoretical statistics literature (despite local log-likelihood methods for regression and density estimation being standard textbook fare [29, 14]).”
Kernel bandwidth optimization in spike rate estimation (2009, methodology): “The established density estimation methods examined for comparison are the histogram (Shimazaki and Shinomoto 2007), Abramson’s adaptive kernel (Abramson 1982), Locﬁt (Loader 1999b), and Bayesian adaptive regression splines (BARS) (DiMatteo et al. 2001; Kass et al. 2003) methods, whose details are s”
Local linear spatial quantile regression (2009, background): “The basic idea of local linear fitting (see [16,17,34,54]) consists in approximating in a neighbourhood of x the unknown quantile regression function qp(z) by a linear function: qp(z) ≈ qp(x) + (q̇p(x))′(z − x) =: a0 + a′1(z − x).”