Local Likelihood Estimation

By Robert Tibshirani, Trevor J. Hastie. 1987

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, background): “Generalized random forests can be thought of as a generalization of local maximum likelihood, introduced by Tibshirani and Hastie [1987], but where kernel weighting functions are used to weight nearby observations more heavily than observations distant from a particular test point. A weakness of for”
Local Linear Forests (2018, methodology): “, kernel regression, k-NN, or matching for causal inference), it is 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; Heckm”
Generalized random forests (2016, methodology): “The idea of local maximum likelihood (and local generalized method of moments) estimation has a long history, including Fan, Farmen and Gijbels (1998), Newey (1994a), Staniswalis (1989), Stone (1977), Tibshirani and Hastie (1987) and Lewbel (2007).”
Non-Parametric Regression for Binary Dependent Variables (2006, methodology): “For higherdimensional X, Tibshirani and Hastie (1987) suggest non-parametric additive modelling for the regressors X, which, however, restricts interactions of the regressors.12 Only in the context of local least squares, an example for non-parametric regression with a binary dependent variable Y…”
Local likelihood estimation of truncated regression and its partial derivatives: theory and application (2008, methodology): “In particular, we adapt the theory of local maximum likelihood estimation (e.g., see Tibshirani and Hastie, 1987, Fan and Gijbels, 1996, Fan et al., 1996, and Kumbhakar et al., 2006) to the truncated regression case.”
Propagation-Separation Approach for Local Likelihood Estimation (2006, methodology): “…based on the assumption that the function θ is smooth leading to its local linear (polynomial) approximation within a ball of some small radius h centered in the point of estimation, see e.g. Tibshirani and Hastie (1987), Hastie and Tibshirani (1993), Fan, Farmen and Gijbels (1998), Carroll et.al.”
Relative risk trees for censored survival data. (1992, background): “The linear function is typically replaced by an additive function (Tibshirani and Hastie, 1987; O'Sullivan, 1988; Gentleman and Crowley, 1991).”
Likelihood-Based Local Linear Estimation of the Conditional Variance Function (2004, background): “The basic idea of local likelihood estimation goes back to Tibshirani and Hastie (1987).”
Analysis of randomized comparative clinical trial data for personalized treatment selections. (2011, methodology): “based on a local likelihood approach (Tibshirani and Hastie, 1987; Fan and Gijbels, 1996), where μk(v) = E(Y 0 (k)|U 0 ∈ v), k = 0, 1, and the expectation is taken with respect to the data and (Y 0 (k),U 0).”
A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity (2002, methodology): “GWR has been developed mainly along lines that parallel developments in the literature on smoothing methods, in particular local likelihood estimation, kernel regression, and locally weighted regression (see, inter alia, Tibshirani and Hastie, 1987; Cleveland and Devlin, 1988).”
The joint estimation of bank-level market power and efficiency (2009, methodology): “We base our approach on the Cournot-type theoretical framework of Uchida and Tsutsui (2005), which has also been employed by Brissimis et al.”
Local maximum likelihood estimation and inference (1998, methodology): “B (1998) 60, Part 3, pp. 591^608 The idea of using local ®tting for likelihood-based regression models was applied by Tibshirani and Hastie (1987) to the class of generalized linear models (see Nelder and Wedderburn (1972)) and to the proportional hazards model of Cox (1972).”