Springer Verlag;Berlin/Heidelberg: Springer Berlin Heidelberg
摘要:
摘要： Doubly-truncated data often appear in lifetime data analysis, where samples are collected under certain time constraints. Nonparametric methods for doubly-truncated data have been studied well in the literature. Alternatively, this paper considers parametric inference when samples are subject to double-truncation. Efron and Petrosian (J Am Stat Assoc 94:824–834, 1999 ) proposed to fit a parametric family, called the special exponential family, with doubly-truncated data. However, non-trivial technical aspects, such as parameter space, support of the density, and computational algorithms, have not been discussed in the literature. This paper fills this gap by providing the technical aspects, including adequate choices of parameter space as well as support, and reliable computational algorithms. Simulations are conducted to verify the suggested techniques, and real data are used for illustration. 其他題名： Comput Stat 出版者： Berlin/Heidelberg: Springer Berlin Heidelberg 出版日期： 2015-12-01 出處： Computational statistics, 2015-12, Vol.30 (4), p.1199-1229 資源來源： SpringerLink Journals 版權： Springer-Verlag Berlin Heidelberg 2015 識別號： ISSN: 0943-4062 識別號： EISSN: 1613-9658 識別號： DOI: 10.1007/s00180-015-0564-z
