کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10139290 | 1645952 | 2019 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Generalized additive partial linear models for analyzing correlated data
ترجمه فارسی عنوان
مدل های خطی جزئی اضافه شده برای تجزیه و تحلیل داده های همبسته
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Statistical procedures are proposed in generalized additive partial linear models (GAPLM) for analyzing correlated data. A reweighed iterative process based on the backfitting algorithm is derived for the parameter estimation from a penalized GEE. Discussions on the inferential aspects of GAPLM, particularly on the asymptotic properties of the former estimators as well as on the effective degrees of freedom derivation, are given. Diagnostic methods, such as leverage measures, residual analysis and local influence graphs, under different perturbation schemes, are proposed. A small simulation study is performed to assess the empirical distribution of the parametric and nonparametric estimators as well as of some proposed residuals. Finally, a motivating data set is analyzed by the methodology developed through the paper.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 129, January 2019, Pages 47-60
Journal: Computational Statistics & Data Analysis - Volume 129, January 2019, Pages 47-60
نویسندگان
Roberto F. Manghi, Francisco José A. Cysneiros, Gilberto A. Paula,