Semiparametric likelihood-based inference for biased and truncated data when the total sample size is known

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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.

Keywords: Auxiliary information; Sampling bias; Semiparametric likelihood ratio; Truncation

Document Type: Original Article


Affiliations: 1: University of California, Los Angeles, USA, 2: University of Maryland, College Park, USA

Publication date: January 1, 1998

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