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- W3179848181 abstract "Regression method has been widely used to explore relationship between dependent and independent variables. In practice, data issues such as censoring and missing data often exist. When the response variable is (fixed) censored, Tobit regression models have been widely employed to explore the relationship between the response variable and covariates. In this paper, we extend conventional parametric Tobit models to a novel semi-parametric regression model by replacing the linear components in Tobit models with nonparametric additive components, which we refer as Tobit additive models, and propose a likelihood based estimation method for Tobit additive models. %The proposed estimation method is computational efficient and easy to implement. Numerical experiments are conducted to evaluate the finite sample performance. The estimation method works well in finite sample experiments, even when sample size is relative small." @default.
- W3179848181 created "2021-07-19" @default.
- W3179848181 creator A5029952336 @default.
- W3179848181 date "2021-07-03" @default.
- W3179848181 modified "2023-09-27" @default.
- W3179848181 title "Novel Semi-parametric Tobit Additive Regression Models" @default.
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- W3179848181 doi "https://doi.org/10.48550/arxiv.2107.01497" @default.
- W3179848181 hasPublicationYear "2021" @default.
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