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http://hdl.handle.net/10397/74604
Title: | Sample size determination for high dimensional parameter estimation with application to biomarker identification | Authors: | Jiang, B Li, J |
Issue Date: | Feb-2018 | Source: | Computational statistics and data analysis, Feb. 2018, v. 118, p. 54-65 | 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. | Keywords: | Bernstein inequality Bonferroni inequality IDI NRI Sample size calculation Training sample |
Publisher: | Elsevier | Journal: | Computational statistics and data analysis | EISSN: | 0167-9473 | DOI: | 10.1016/j.csda.2017.08.010 | Rights: | © 2017 Elsevier B.V. All rights reserved. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ The following publication Jiang, B., & Li, J. (2018). Sample size determination for high dimensional parameter estimation with application to biomarker identification. Computational Statistics & Data Analysis, 118, 54-65 is available at https://doi.org/10.1016/j.csda.2017.08.010 |
Appears in Collections: | Journal/Magazine Article |
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Jiang_Sample_Size_Determination.pdf | Pre-Published version | 1.14 MB | Adobe PDF | View/Open |
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