کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946594 1439408 2017 33 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Graph-based composite local Bregman divergences on discrete sample spaces
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Graph-based composite local Bregman divergences on discrete sample spaces
چکیده انگلیسی
This paper develops a general framework of statistical inference on discrete sample spaces, on which a neighborhood system is defined by an undirected graph. The scoring rule is a measure of the goodness of fit for the model to observed samples, and we employ its localized version, local scoring rules, which does not require the normalization constant. We show that the local scoring rule is closely related to a discrepancy measure called composite local Bregman divergence. Then, we investigate the statistical consistency of local scoring rules in terms of the graphical structure of the sample space. Moreover, we propose a robust and computationally efficient estimator based on our framework. In numerical experiments, we investigate the relation between the neighborhood system and estimation accuracy. Also, we numerically evaluate the robustness of localized estimators.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 95, November 2017, Pages 44-56
نویسندگان
, ,