Profile likelihood inferences on semiparametric varying-coefficient partially linear models

By Jianqing Fan, Tao Huang. 2005

746 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/96e4aa3abd4aee6818fdf07a996eb4a1cbd90f14

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Zhou Zhou
2. Yingcun Xia
3. Ying Wei
4. Byeong U. Park
5. Jinhong You
6. Dennis Kristensen
7. Qi Li
8. Jianqing Fan

In Statistics and Probability

1. Jianqing Fan
2. Chenlei Leng
3. Yingcun Xia
4. Liangjun Su
5. Hua Liang
6. Hansheng Wang
7. Byeong U. Park
8. Tao Hu

Citations in the corpus

• Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates (2009, methodology): “The proof is similar to Theorems 3.1 and 3.2 of Fan and Huang (2005).”
• A SINGLE-INDEX QUANTILE REGRESSION MODEL AND ITS ESTIMATION (2012, methodology): “The data have been analyzed by several statisticians; see, e.g., Harrison and Rubinfeld (1978), Doksum and Samarov (1995), Fan and Huang (2005), and the references therein.”
• Tuning parameter selectors for the smoothly clipped absolute deviation method. (2007, methodology): “Furthermore, it can be shown that by using results in Mack & Silverman (1982), where U is the support of u; see Fan & Huang (2005) for details.”
• Semiparametric Trending Panel Data Models with Cross-Sectional Dependence (2012, methodology): “Among these methods, the averaged profile likelihood estimation method is a commonly–used method and has been investigated by some authors in both the time series and panel data cases (see, for example, Fan and Huang 2005; Su and Ullah 2006; Atak, Linton and Xiao 2010).”
• A UNIFIED VARIABLE SELECTION APPROACH FOR VARYING COEFFICIENT MODELS (2012, methodology): “To summarize, three types of variable selection problems are of practical interests in VC models: (i) separation of varying and constant coefficients (Huang, Wu, and Zhou (2002)); Wang, Zhu, and Zhou (2009)); (ii) selection of variables with nonzero varying coefficients (Cai, Fan, and Li (2000), Qu ”
• Statistical Methods with Varying Coefficient Models. (2008, methodology): “Under the conditions in Fan and Huang (2005) , the estimatorâ 2 given in (2.13) is asymptotic mal, i.e. n 1/2 (ˆ a 2 − a 2 ) D −→ N (0, Σ), where Σ = E(Z 2 Z T 2 ) − E E(Z 2 Z T 1 |U ) E(Z 1 Z T 1 |U ) −1 × E(Z 1 Z T 2 |U ) −1 σ 2 (u).”
• ADAPTIVE SEMI-VARYING COEFFICIENT MODEL SELECTION (2012, methodology): “To study the asymptotic properties of the proposed method, standard regu- larity conditions are needed (Fan and Huang (2005)).”
• Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression (2013, methodology): “For example, Li et al. (2002) introduced a local least-square method with a kernel weight function for SPLVCM; Zhang et al. (2002) studied SPLVCM based on local polynomial method (Fan and Gijbels 1996); Lu (2008) discussed the SPLVCM in the framework of generalized linear model based on two step est”
• Non‐parametric detection and estimation of structural change (2012, positive): “(2.7) The above estimator of β (τ) is identical to the one proposed by Robinson (1989), and is similar to nonparametric estimators of varying-coefficient models in a cross-sectional setting; see, for example, Fan and Huang (2005).”
• A Multiple-Index Model and Dimension Reduction (2008, methodology): “See, for example, Grenfell, Finkenstädt, Wilson, Coulson, and Crawley (2000), Gao and Tong (2004), Samarov et al. (2005), and Maestri et al.”
• Statistical Estimation in Generalized Multiparameter Likelihood Models (2009, methodology): “Under semiparametric models for the regression mean, Fan and Huang (2005) and Lam and Fan (2008) used difference-based methods to obtain a reliable initial estimate.”
• Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling (2013, methodology): “To this goal, one can follow the idea of profile least squares or likelihood (e.g., Fan and Huang 2005; Su and Jin 2010) and extend it to our local linear GMM framework.”
• A simple approach for varying-coefficient model selection (2009, background): “See also Fan and Huang (2006) .T his model allows us to examine the possible interactions between LSTAT and other variables.”
• Mixture of Regression Models With Varying Mixing Proportions: A Semiparametric Approach (2012, methodology): “1 of Fan and Huang (2005) and will be used throughout the proofs of this section.”
• Estimation and inference in spatially varying coefficient models (2018, methodology): “Fan, J. and Huang, T. (2005). Profile likelihood inferences on semiparametric varying-coefficient partially linear models.”
• Estimation of a semiparametric varying-coefficient partially linear errors-in-variables model (2006, methodology): “When X is observable as well, we can apply the profile least squares estimation proposed by Fan and Huang [13] to estimate the parametric component and apply the local polynomial estimation to estimate the nonparametric component.”