Nonparametric and Semiparametric Models

By Robert A. Lordo. 2005

614 citations recorded by the source corpus.

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In Statistics and Probability

Citations in the corpus

Convex-constrained Sparse Additive Modeling and Its Extensions (2017, methodology): “Following [HSMW04] and [RLLW08], 10 original covariates are chosen to be included in the study: crim, indus, nox, rm, age, dis, tax, ptratio, b, lstat.”
Sparse additive models (2007, methodology): “Our ﬁrst example is from (H¨ardle et al., 2004).”
High-dimensional additive modeling (2008, neutral): “This example is similar to Example 1 in [26] and [15].”
Uncertainty identification by the maximum likelihood method (2005, methodology): “[17], where the bandwidth matrix is given by H 1⁄4 M 1=ðdþ4ÞR1=2 y , (17) where d 1⁄4 minðn;mÞ is the number of dof in the response.”
Model learning for robot control: a survey (2011, methodology): “A straightfoward way to model the function f in Equation (6) is to assume a parametric structure, such as linear or polynomial models or multilayer perceptron neural networks, and, subsequently, fit the model parameters using training data [50,48,51].”
The Curse of Highly Variable Functions for Local Kernel Machines (2005, background): “For a wide class of kernel regression estimators, the unconditional variance and squared bias can be shown to be written as follows (Härdle et al., 2004):”
Focused information criterion and model averaging for generalized additive partial linear models (2011, background): “…since it is only defined implicitly as the limit of a complicated iterative algorithm, while the latter suffers from the curse of dimensionality [Härdle et al. (2004a)], which may lead to an increase in the computational burden and which also conflicts with the purpose of using a GAPLM, that…”
Restricted Ridge Estimators of the Parameters in Semiparametric Regression Model (2009, neutral): “This model has attracted a considerable attention in the past two decades (see Green and Silverman, 1994; Eubank, 1999; Hardle et al., 2000, 2004).”
Analyzing Semiparametrically the Trends in the Gender Pay Gap - The Example of Spain (2008, methodology): “For more details of the nonparametric method see Härdle et al (2004).”
Bandwidth Selection in an EM-Like Algorithm for Nonparametric Multivariate Mixtures (2011, methodology): “The nonparametric density estimation step (6) of the npEM algorithm described above is a modified version of kernel density estimation, a wellstudied topic in statistics (e.g., see Silverman, 1986; Scott, 1992; Härdle et al., 2004).”
SHREC 2007 3 D Shape Retrieval Contest (2006, neutral): “The contest is organized in conjunction with the SMI’07 conference (Shape Modeling International), where the evaluation results are presented.”
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation (2007, methodology): “Since KLIEP is based on the minimization of the Kullback-Leibler divergence, its model selection can be naturally carried out through a variant of likelihood CV, which is a popular technique in density estimation [3].”
Non-linear regression models for Approximate Bayesian Computation (2008, background): “H¨ardle et al. 2004).”