Nonparametric-likelihood inference based on cost-effectively-sampled-data

Albert Vexler, Shuling Liu and Enrique F. Schisterman

Journal of Applied Statistics, 2011, vol. 38, issue 4, 769-783

Abstract: Costs associated with the evaluation of biomarkers can restrict the number of relevant biological samples to be measured. This common problem has been dealt with extensively in the epidemiologic and biostatistical literature that proposes to apply different cost-efficient procedures, including pooling and random sampling strategies. The pooling design has been widely addressed as a very efficient sampling method under certain parametric assumptions regarding data distribution. When cost is not a main factor in the evaluation of biomarkers but measurement is subject to a limit of detection, a common instrument limitation on the measurement process, the pooling design can partially overcome this instrumental limitation. In certain situations, the pooling design can provide data that is less informative than a simple random sample; however this is not always the case. Pooled-data-based nonparametric inferences have not been well addressed in the literature. In this article, a distribution-free method based on the empirical likelihood technique is proposed to substitute the traditional parametric-likelihood approach, providing the true coverage, confidence interval estimation and powerful tests based on data obtained after the cost-efficient designs. We also consider several nonparametric tests to compare with the proposed procedure. We examine the proposed methodology via a broad Monte Carlo study and a real data example.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/02664761003692290 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:38:y:2011:i:4:p:769-783

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664761003692290

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().