کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
417181 681464 2008 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data
چکیده انگلیسی

An Approximate Bayesian Bootstrap (ABB) offers advantages in incorporating appropriate uncertainty when imputing missing data, but most implementations of the ABB have lacked the ability to handle nonignorable missing data where the probability of missingness depends on unobserved values. This paper outlines a strategy for using an ABB to multiply impute nonignorable missing data. The method allows the user to draw inferences and perform sensitivity analyses when the missing data mechanism cannot automatically be assumed to be ignorable. Results from imputing missing values in a longitudinal depression treatment trial as well as a simulation study are presented to demonstrate the method’s performance. We show that a procedure that uses a different type of ABB for each imputed data set accounts for appropriate uncertainty and provides nominal coverage.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 53, Issue 2, 15 December 2008, Pages 405–415
نویسندگان
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