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Semiparametric likelihood‐based inference for biased and truncated data when the total sample size is known
Author(s) -
Li Gang,
Qin Jing
Publication year - 1998
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00122
Subject(s) - inference , sample size determination , statistics , statistical inference , confidence interval , sample (material) , mathematics , econometrics , interval (graph theory) , computer science , artificial intelligence , chemistry , chromatography , combinatorics
Biased and truncated data arise in many practical areas. Many efficient statistical methods have been studied in the literature. This paper discusses likelihood‐based inferences for the two types of data in the presence of auxiliary information of known total sample size. It is shown that this information improves inference about the underlying distribution and its parameters in which we are interested. A semiparametric likelihood ratio confidence interval technique is employed. Also some simulation results are reported.