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Identifiability of single-index models and additive-index models
Biometrika, 2007SUMMARY We provide a proof for the identifiability for both single-index models and partially linear single index models assuming only the continuity of the regression function, a condition much weaker than the differentiability conditions assumed in the existing literature.
Wei Lin, K. B. Kulasekera
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Biometrika, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Naik, Prasad A., Tsai, Chih-Ling
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Naik, Prasad A., Tsai, Chih-Ling
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On bootstrap consistency of MAVE for single index models
Computational Statistics & Data Analysis, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hong-Fan Zhang +2 more
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IEEE Transactions on Knowledge and Data Engineering, 2021
Agus Sudjianto +2 more
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Agus Sudjianto +2 more
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1998
One of the most important tasks of applied econometrics and statistics is estimating a conditional mean function. For example, one may want to estimate the mean annual earnings of workers in a certain population as a function of observable characteristics such as level of education and experience in the workforce.
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One of the most important tasks of applied econometrics and statistics is estimating a conditional mean function. For example, one may want to estimate the mean annual earnings of workers in a certain population as a function of observable characteristics such as level of education and experience in the workforce.
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Nonlinear regression models with single‐index heteroscedasticity
Statistica Neerlandica, 2019We consider nonlinear heteroscedastic single‐index models where the mean function is a parametric nonlinear model and the variance function depends on a single‐index structure. We develop an efficient estimation method for the parameters in the mean function by using the weighted least squares estimation, and we propose a “delete‐one‐component ...
Jun Zhang +3 more
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Quantile regression of partially linear single-index model with missing observations
Statistics, 2021Han-Ying Liang, Yu Shen
exaly

