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Annals of the Institute of Statistical Mathematics : Volume 69

Annals of the Institute of Statistical Mathematics : Volume 68

Annals of the Institute of Statistical Mathematics : Volume 67

Annals of the Institute of Statistical Mathematics : Volume 66

Annals of the Institute of Statistical Mathematics : Volume 65

Annals of the Institute of Statistical Mathematics : Volume 65, Issue 5, October 2013

Annals of the Institute of Statistical Mathematics : Volume 65, Issue 4, August 2013

Annals of the Institute of Statistical Mathematics : Volume 65, Issue 3, June 2013

Annals of the Institute of Statistical Mathematics : Volume 65, Issue 2, April 2013

Minimum density power divergence estimator for diffusion processes

Partial linear single index models with distortion measurement errors

The algebra of reversible Markov chains

Model selection via standard error adjusted adaptive lasso

Testing statistical hypotheses based on the density power divergence

Degenerate $$U$$ - and $$V$$ -statistics under ergodicity: asymptotics, bootstrap and applications in statistics

Asymptotic Palm likelihood theory for stationary point processes

Annals of the Institute of Statistical Mathematics : Volume 65, Issue 1, February 2013

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Partial linear single index models with distortion measurement errors

Content Provider	Springer Nature Link
Author	Zhang, Jun Yu, Yao Zhu, Li Xing Liang, Hua
Copyright Year	2012
Abstract	We study partial linear single index models when the response and the covariates in the parametric part are measured with errors and distorted by unknown functions of commonly observable confounding variables, and propose a semiparametric covariate-adjusted estimation procedure. We apply the minimum average variance estimation method to estimate the parameters of interest. This is different from all existing covariate-adjusted methods in the literature. Asymptotic properties of the proposed estimators are established. Moreover, we also study variable selection by adopting the coordinate-independent sparse estimation to select all relevant but distorted covariates in the parametric part. We show that the resulting sparse estimators can exclude all irrelevant covariates with probability approaching one. A simulation study is conducted to evaluate the performance of the proposed methods and a real data set is analyzed for illustration.
Starting Page	237
Ending Page	267
Page Count	31
File Format	PDF
ISSN	00203157
Journal	Annals of the Institute of Statistical Mathematics
Volume Number	65
Issue Number	2
e-ISSN	15729052
Language	English
Publisher	Springer Japan
Publisher Date	2012-07-20
Publisher Place	Japan
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Coordinate-independent sparse estimation (CISE) Covariate adjusted Dimension reduction Distorting function Minimum average variance estimation (MAVE) Measurement errors Single index Sparse principle component (SPC) Statistics Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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Intrinsic minimum average variance estimation for sufficient dimension reduction with symmetric positive definite matrices and beyond

Robust variable selection through MAVE

Variance component estimation for partial errors-in-variables models

Coordinate-independent sparse sufficient dimension reduction and variable selection

Minimum Average Deviance Estimation for Sufficient Dimension Reduction

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Smoothed functional average variance estimation for dimension reduction

Sufficient dimension reduction based on an ensemble of minimum average variance estimators

Quantile regression and variable selection of partial linear single-index model

Partial linear single index models with distortion measurement errors

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