Kernel smoothing density estimation when group membership is subject to missing

Density function is a fundamental concept in data analysis. Non-parametric methods including kernel smoothing estimate are available if the data is completely observed. However, in studies such as diagnostic studies following a two-stage design the membership of some of the subjects may be missing....

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Bibliographic Details
Published inJournal of statistical planning and inference Vol. 142; no. 3; pp. 685 - 694
Main Authors Tang, Wan, He, Hua, Gunzler, Douglas
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 01.03.2012
Subjects
Density
Kernel smoothing
Membership missing
Kernel smoothing
Density
Membership missing
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Summary:Density function is a fundamental concept in data analysis. Non-parametric methods including kernel smoothing estimate are available if the data is completely observed. However, in studies such as diagnostic studies following a two-stage design the membership of some of the subjects may be missing. Simply ignoring those subjects with unknown membership is valid only in the MCAR situation. In this paper, we consider kernel smoothing estimate of the density functions, using the inverse probability approaches to address the missing values. We illustrate the approaches with simulation studies and real study data in mental health.
Bibliography:ObjectType-Article-1
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ISSN:0378-3758
1873-1171
DOI:10.1016/j.jspi.2011.09.009