Email this article 
Hide
Show all
| 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:: 1458. Submitted: 2013-04-24. Published: 2015-10-07. |
|||||
| Paper: |
Threshold Value Estimation Using Adaptive Two-Stage Plans in R
Download PDF
(Downloads: 537)
|
||||
| Supplements: |
| ||||
| DOI: |
10.18637/jss.v067.i03
|
This work is licensed under the licenses 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. |