Threshold Value Estimation Using Adaptive Two-Stage Plans in R

Shawn Mankad, George Michailidis and Moulinath Banerjee

Journal of Statistical Software, 2015, vol. 067, issue i03

Abstract: This paper introduces the R package twostageTE for estimation of an inverse regression function at a given point when one can sample an explanatory covariate at different values and measure the corresponding responses. The package implements a number of nonparametric methods for budget constrained threshold value estimation. Specifically, it contains methods for classical one-stage designs and also adaptive two-stage designs, which have been shown to yield more efficient and accurate results. A major advantage of the methods in package twostageTE is that threshold value estimation is performed without penalization or kernel smoothing, and hence, avoids the well-known problems of choosing the corresponding tuning parameter (regularization, bandwidth). The user can easily perform a two-stage analysis with twostageTE by (i) identifying the second stage sampling region from an initial sample, and (ii) computing various types of confidence intervals to ensure a robust analysis. The package twostageTE is illustrated through simulated examples.

Date: 2015-10-07
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:067:i03

DOI: 10.18637/jss.v067.i03

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