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Confidence Intervals for Nonparametric Regression Functions with Missing Data.

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Titel: Confidence Intervals for Nonparametric Regression Functions with Missing Data.
Autoren: Qin, Yongsong, Qiu, Tao, Lei, Qingzhu
Quelle: Communications in Statistics: Theory & Methods; Oct2014, Vol. 43 Issue 19, p4123-4142, 20p
Schlagwörter: CONFIDENCE intervals, NONPARAMETRIC estimation, REGRESSION analysis, MATHEMATICAL functions, MISSING data (Statistics), EMPIRICAL research
Abstract: Suppose that we have a nonparametric regression modelY=m(X) + ε withX∈Rp, whereXis a random design variable and is observed completely, andYis the response variable and someY-values are missing at random. Based on the “complete” data sets forYafter nonaprametric regression imputation and inverse probability weighted imputation, two estimators of the regression functionm(x0) for fixedx0∈Rpare proposed. Asymptotic normality of two estimators is established, which is used to construct normal approximation-based confidence intervals form(x0). We also construct an empirical likelihood (EL) statistic form(x0) with limiting distribution of χ21, which is used to construct an EL confidence interval form(x0). [ABSTRACT FROM PUBLISHER]
Copyright of Communications in Statistics: Theory & Methods is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Confidence Intervals for Nonparametric Regression Functions with Missing Data.
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  Data: <searchLink fieldCode="AR" term="%22Qin%2C+Yongsong%22">Qin, Yongsong</searchLink><br /><searchLink fieldCode="AR" term="%22Qiu%2C+Tao%22">Qiu, Tao</searchLink><br /><searchLink fieldCode="AR" term="%22Lei%2C+Qingzhu%22">Lei, Qingzhu</searchLink>
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  Data: Communications in Statistics: Theory & Methods; Oct2014, Vol. 43 Issue 19, p4123-4142, 20p
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  Data: <searchLink fieldCode="DE" term="%22CONFIDENCE+intervals%22">CONFIDENCE intervals</searchLink><br /><searchLink fieldCode="DE" term="%22NONPARAMETRIC+estimation%22">NONPARAMETRIC estimation</searchLink><br /><searchLink fieldCode="DE" term="%22REGRESSION+analysis%22">REGRESSION analysis</searchLink><br /><searchLink fieldCode="DE" term="%22MATHEMATICAL+functions%22">MATHEMATICAL functions</searchLink><br /><searchLink fieldCode="DE" term="%22MISSING+data+%28Statistics%29%22">MISSING data (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22EMPIRICAL+research%22">EMPIRICAL research</searchLink>
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  Data: Suppose that we have a nonparametric regression modelY=m(X) + ε withX∈Rp, whereXis a random design variable and is observed completely, andYis the response variable and someY-values are missing at random. Based on the “complete” data sets forYafter nonaprametric regression imputation and inverse probability weighted imputation, two estimators of the regression functionm(x0) for fixedx0∈Rpare proposed. Asymptotic normality of two estimators is established, which is used to construct normal approximation-based confidence intervals form(x0). We also construct an empirical likelihood (EL) statistic form(x0) with limiting distribution of χ21, which is used to construct an EL confidence interval form(x0). [ABSTRACT FROM PUBLISHER]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Communications in Statistics: Theory & Methods is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/03610926.2012.705210
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 4123
    Subjects:
      – SubjectFull: CONFIDENCE intervals
        Type: general
      – SubjectFull: NONPARAMETRIC estimation
        Type: general
      – SubjectFull: REGRESSION analysis
        Type: general
      – SubjectFull: MATHEMATICAL functions
        Type: general
      – SubjectFull: MISSING data (Statistics)
        Type: general
      – SubjectFull: EMPIRICAL research
        Type: general
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      – TitleFull: Confidence Intervals for Nonparametric Regression Functions with Missing Data.
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              M: 10
              Text: Oct2014
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              Y: 2014
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