Local polynomial modelling and its applications

By Jianqing Fan, Irène Gijbels. 1994

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Citations in the corpus

Local Linear Forests (2018, methodology): “Our work is motivated by the literature on local linear regression and maximum likelihood estimation (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, background): “…however, once we move to more general settings, the weighting-based perspective proves substantially more effective, and also brings forests closer to the literature on local maximum likelihood estimation (Fan and Gijbels, 1996; Loader, 1999; Newey, 1994a; Stone, 1977; Tibshirani and Hastie, 1987).”
Sufficient Forecasting Using Factor Models (2015, methodology): “Given these estimated low-dimensional indices, we may employ one of the well-developed nonparametric regression techniques to estimate h ( · ) and make the forecast, including local polynomial modeling, regression splines, additive modeling, and many others (Green and Silverman, 1993; Fan and Gijbel”
Bandwidth Selection in Nonparametric Kernel Testing (2008, methodology): “For the case of d ≥ 4, various dimension-reduction estimation and specification methods have been discussed extensively in several monographs (e.g., Fan and Gijbels 1996; Hart 1997; Fan and Yao 2003; Li and Racine 2007).”
Nonparametric Estimation of an Additive Quantile Regression Model (2004, methodology): “using a simple rule of thumb described in Fan and Gijbels (1996, p.202). The kernel K is taken to be the normal density function. Varying δn from 0.75δn to 1.25δn did not change estimation results significantly. All the computation in this paper is carried out in R using libraries ‘splines’ (to gene”
Trending time-varying coefficient time series models with serially correlated errors (2007, methodology): “There are vast studies of comparing different nonparametric smoothing methods in literature; see Ha¨ rdle (1990), Fan and Gijbels (1996) , and Pagan and Ullah (1999).”
On deconvolution with repeated measurements (2008, background): “See also the work of Horowitz and Markatou (1996) on error estimation from panel data, and the extensive literature, accessible through the work of Newey and Powell (2003), on inference in the context of instrumental variables.”
Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency (2018, result): “At a conceptual level, our small bandwidth approach is very similar to the “dimension asymptotics”approach taken in the seminal work of Mammen (1989) and, although the technical details are rather different, some of our main conclusions are similar to his. For a more detailed explanation of the conn”
Uniform in bandwidth consistency of kernel-type function estimators (2005, methodology): “MR1744994 [12] Fan, J. and Gijbels, I. (1996).”
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images (2007, methodology): “, [10]) is a technique which is applied for nonparametric estimation using a polynomial data fit in a sliding window.”
Statistical Methods with Varying Coefficient Models. (2008, background): “See Section 4.3 of Fan and Gijbels (1996) on how to compute the estimated standard errors for the univariate local polynomial regression.”
A class of spatial econometric methods in the empirical analysis of clusters of firms in the space (2008, methodology): “For the estimation we considered the following quadratic kernel (see Hastie and Tibshirani 1990; Fan and Gijbels 1996)”
Conditional variance estimation in heteroscedastic regression models (2009, background): “There exists an extensive study on estimating the nonparametric regression func- tion m(x); see for example Fan and Gijbels (1996).”
Non‐parametric methods for doubly robust estimation of continuous treatment effects (2015, background): “For overviews of kernel smoothing see, for example, Fan and Gijbels (1996), Wasserman (2006) and Li and Racine (2007).”
Functional Data Analysis for Sparse Longitudinal Data (2005, methodology): “We use local linear smoothers (Fan and Gijbels 1996) for function and surface estimation, fitting local lines in one dimension and local planes in two dimensions by weighted least squares.”
Detecting selection bias, using propensity score matching, and estimating treatment effects: an application to the private returns to a master’s degree (2007, methodology): “[For a detailed discussion of the distinction between kernel and local linear regression methods, see Fan and Gijbels (1996).] All of the matching techniques described above help to ensure that the distribution of the pre-treatment characteristics of individuals in the treatment and the control grou”