Related Topics in Partially Linear Models. Semi-parametric Regression, Measurement Errors, Missing Data, Single-index Models, Regression Calibration

Related Topics in Partially Linear Models. Semi-parametric Regression, Measurement Errors, Missing Data, Single-index Models, Regression Calibration

Hua Liang

     

бумажная книга



Издательство: Книга по требованию
Дата выхода: июль 2011
ISBN: 978-3-6390-7239-6
Объём: 100 страниц
Масса: 172 г
Размеры(В x Ш x Т), см: 23 x 16 x 1

Various effects have been made to remedy the curse of dimensionality for high-dimensional data. Partially linear models, as an effective dimensional reduction technique, have been intensively studied in literature. We develop methodology for the estimation of regression parameters in partially linear models when the covariates are measured with errors or may be missing. We are particularly concerned with two cases where we observe a surrogate of the covariate. The second case focuses on the linear covariate being incompletely observable. We give the corresponding solutions for the above problems. The first solution employs the technique of correcting for attenuation. The second is proposed using inverse weight probability. The resulting estimators are proven to be asymptotically normal. The model is used to analyze a data set from the Framingham Heart Study for the purpose of illustrating the methods. We also investigate the semiparametric partially linear single index errors-in-variables models, for which two classes of estimators are proposed, and the corresponding theoretical properties are derived and compared.

Данное издание не является оригинальным. Книга печатается по технологии принт-он-деманд после получения заказа.

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