کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
397322 | 1438452 | 2014 | 13 صفحه PDF | دانلود رایگان |
• The construction of a belief function from the likelihood function is justified.
• This approach can be extended to statistical inference from low-quality data.
• The cases of partially relevant and uncertain data are considered.
Given a parametric statistical model, evidential methods of statistical inference aim at constructing a belief function on the parameter space from observations. The two main approaches are Dempster's method, which regards the observed variable as a function of the parameter and an auxiliary variable with known probability distribution, and the likelihood-based approach, which considers the relative likelihood as the contour function of a consonant belief function. In this paper, we revisit the latter approach and prove that it can be derived from three basic principles: the likelihood principle, compatibility with Bayes' rule and the minimal commitment principle. We then show how this method can be extended to handle low-quality data. Two cases are considered: observations that are only partially relevant to the population of interest, and data acquired through an imperfect observation process.
Journal: International Journal of Approximate Reasoning - Volume 55, Issue 7, October 2014, Pages 1535-1547