Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Shawn Mankad, George Michailidis, Moulinath Banerjee
Title: Threshold Value Estimation Using Adaptive Two-Stage Plans in R
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.

Page views:: 1622. Submitted: 2013-04-24. Published: 2015-10-07.
Paper: Threshold Value Estimation Using Adaptive Two-Stage Plans in R     Download PDF (Downloads: 623)
twostageTE_1.3.tar.gz: R source package Download (Downloads: 84; 219KB)
v67i03.R: R replication code Download (Downloads: 117; 7KB)

DOI: 10.18637/jss.v067.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.