کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4949271 1440043 2017 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A wild bootstrap approach for nonparametric repeated measurements
ترجمه فارسی عنوان
یک روش بوت استرپ وحشی برای اندازه گیری های تکراری غیر پارامتری
کلمات کلیدی
داده های طولی، اشکال درجه دو روش های مبتنی بر رتبه اقدامات تکراری، بوت استرپ وحشی بوت استرپ خوشه وحشی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Repeated measures and split plot plans are often the preferred design of choice when planning experiments in life and social sciences. They are typically analyzed by mean-based methods from MANOVA or linear mixed models, requiring certain assumptions on the underlying parametric distribution. However, if count, ordinal or score data are present, these techniques show their limits since means are no adequate measure of deviations between groups. Here, nonparametric rank-based methods are preferred for making statistical inference. The common nonparametric procedures such as the Wald- or ANOVA-type tests, however, have drawbacks since they usually require large sample sizes for accurate test decisions. The aim is to enhance the small sample properties of these test statistics by means of a specific nonparametric bootstrap procedure while preserving their general applicability for all kinds of data in factorial repeated measures and split plot designs. In particular, it is shown that a specific wild bootstrap procedure inherits the large sample properties of the Wald- and ANOVA-type statistics while considerably improving their small sample behavior. The new method is motivated by and applied to a practical data example in a repeated measures design with score data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 113, September 2017, Pages 38-52
نویسندگان
, , ,