Please use this identifier to cite or link to this item:
Title: Sample size determination for high dimensional parameter estimation with application to biomarker identification
Authors: Jiang, B 
Li, J
Keywords: Bernstein inequality
Bonferroni inequality
Sample size calculation
Training sample
Issue Date: 2018
Publisher: North-Holland
Source: Computational statistics and data analysis, 2018, v. 118, p. 54-65 How to cite?
Journal: Computational statistics and data analysis 
Abstract: We consider sample size calculation to obtain sufficient estimation precision and control the length of confidence intervals under high dimensional assumptions. In particular, we intend to provide more general results for sample size determination when a large number of parameter values need to be computed for a fixed sample. We consider three design approaches: normal approximation, inequality method and regression method. These approaches are applied to sample size calculation in estimating the Net Reclassification Improvement (NRI) and the Integrated Discrimination Improvement (IDI) for a diagnostic or screening study. Two medical examples are also provided as illustration. Our results suggest the regression method in general can yield a much smaller sample size than other methods.
ISSN: 0167-9473
EISSN: 0167-9473
DOI: 10.1016/j.csda.2017.08.010
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Apr 23, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.