Goodness of fit tests for parametric regression models

By 조재현. 2004

52 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5973302f1399abccc1b0af860876ebccfedc1d6e

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Citations in the corpus

On Testing Independence and Goodness-of-fit in Linear Models (2013, methodology): “The other model, Model 2, is adapted from [FH01] (see Example 4 of their paper) and can be written as where X = ( X 1 , X 2 , X 3 , X 4 ) ⊤ is the covariate vector.”
New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis (2004, methodology): “To avoid the forementioned two problems, unbounded loss of efficiency and nonnegligible biases due to approximations, and to maintain the simplicity of Lin and Ying’s approach, we propose the following simple method from the partial linear model (Fan and Huang 2001).”
Nonparametric inference with generalized likelihood ratio tests (2007, background): “It was shown by Fan (1996) that under the null hypothesis (2.14), P (TAN x) → exp(− exp(−x)), as n →∞, and by Fan and Huang (2001) and Fan, Zhang and Zhang (2001) that the adaptive Neyman test can detect adaptively, in the sense of Spokoiny (1996), the alternatives with the optimal rate (n−2 log…”
A Test for model specification of diffusion processes (2007, background): “The adaptive result is more explicitly given in Fan and Huang [17].”
Profile likelihood inferences on semiparametric varying-coefficient partially linear models (2005, methodology): “This kind of idea appears independently in the work of Yatchew (1997), who used ª0 ¼ ª1 ¼ 0, and Fan and Huang (2001), who used the linear terms to gain a better approximation.”
Nonparametric Inferences for Additive Models (2005, methodology): “Nonparametric Inferences for Additive Models”