Estimation in additive models with fixed censored responses

Hailin Huang; Yanlin Tang; Yuanzhang Li; Hua Liang

doi:10.1080/10485252.2018.1537441

Estimation in additive models with fixed censored responses

Hailin Huang
, Yanlin Tang
, Yuanzhang Li
, Hua Liang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We propose a new estimation method to estimate the nonparametric functions in additive models, where the response is subject to fixed censoring. Under some regularity conditions, we show that the proposed estimator is uniformly consistent with certain convergence rates. The simulation study shows that the proposed estimator performs well in finite sample sizes. We also analyze a dataset from an HIV study for an illustration.

Original language	English
Pages (from-to)	131-143
Number of pages	13
Journal	Journal of Nonparametric Statistics
Volume	31
Issue number	1
DOIs	https://doi.org/10.1080/10485252.2018.1537441
State	Published - 2 Jan 2019
Externally published	Yes

Keywords

Curse of dimensionality
Tobit model
nonparametric censored regression
randomly censored
series estimator

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1080/10485252.2018.1537441

Cite this

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abstract = "We propose a new estimation method to estimate the nonparametric functions in additive models, where the response is subject to fixed censoring. Under some regularity conditions, we show that the proposed estimator is uniformly consistent with certain convergence rates. The simulation study shows that the proposed estimator performs well in finite sample sizes. We also analyze a dataset from an HIV study for an illustration.",

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AU - Huang, Hailin

AU - Tang, Yanlin

AU - Li, Yuanzhang

AU - Liang, Hua

PY - 2019/1/2

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N2 - We propose a new estimation method to estimate the nonparametric functions in additive models, where the response is subject to fixed censoring. Under some regularity conditions, we show that the proposed estimator is uniformly consistent with certain convergence rates. The simulation study shows that the proposed estimator performs well in finite sample sizes. We also analyze a dataset from an HIV study for an illustration.

AB - We propose a new estimation method to estimate the nonparametric functions in additive models, where the response is subject to fixed censoring. Under some regularity conditions, we show that the proposed estimator is uniformly consistent with certain convergence rates. The simulation study shows that the proposed estimator performs well in finite sample sizes. We also analyze a dataset from an HIV study for an illustration.

KW - Curse of dimensionality

KW - Tobit model

KW - nonparametric censored regression

KW - randomly censored

KW - series estimator

UR - https://www.scopus.com/pages/publications/85055532122

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DO - 10.1080/10485252.2018.1537441

M3 - 文章

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SN - 1048-5252

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SP - 131

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JO - Journal of Nonparametric Statistics

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ER -

Estimation in additive models with fixed censored responses

Abstract

Keywords

UN SDGs

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