Generalized Maximally Selected Statistics. Export

@article{citeulike:2986644, abstract = {Maximally selected statistics for the estimation of simple cutpoint models are embedded into a generalized conceptual framework based on conditional inference procedures. This powerful framework contains most of the published procedures in this area as special cases, such as maximally selected chi(2) and rank statistics, but also allows for direct construction of new test procedures for less standard test problems. As an application, a novel maximally selected rank statistic is derived from this framework for a censored response partitioned with respect to two ordered categorical covariates and potential interactions. This new test is employed to search for a high-risk group of rectal cancer patients treated with a neo-adjuvant chemoradiotherapy. Moreover, a new efficient algorithm for the evaluation of the asymptotic distribution for a large class of maximally selected statistics is given enabling the fast evaluation of a large number of cutpoints.}, address = {Institut f\"{u}r Statistik, Ludwig-Maximilians-Universit\"{a}t M\"{u}nchen, Ludwigstra{\ss}e 33, D-80539 M\"{u}nchen, Germany.}, author = {Hothorn, Torsten and Zeileis, Achim}, citeulike-article-id = {2986644}, citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1541-0420.2008.00995.x}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18325074}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18325074}, day = {5}, doi = {10.1111/j.1541-0420.2008.00995.x}, issn = {0006-341X}, journal = {Biometrics}, keywords = {breakpoint, dose-response, monotonic, r}, month = {March}, posted-at = {2008-07-11 04:12:22}, priority = {2}, title = {Generalized Maximally Selected Statistics.}, url = {http://dx.doi.org/10.1111/j.1541-0420.2008.00995.x}, year = {2008} }

TY - JOUR ID - citeulike:2986644 L3 - citeulike-article-id:2986644 N2 - Maximally selected statistics for the estimation of simple cutpoint models are embedded into a generalized conceptual framework based on conditional inference procedures. This powerful framework contains most of the published procedures in this area as special cases, such as maximally selected chi(2) and rank statistics, but also allows for direct construction of new test procedures for less standard test problems. As an application, a novel maximally selected rank statistic is derived from this framework for a censored response partitioned with respect to two ordered categorical covariates and potential interactions. This new test is employed to search for a high-risk group of rectal cancer patients treated with a neo-adjuvant chemoradiotherapy. Moreover, a new efficient algorithm for the evaluation of the asymptotic distribution for a large class of maximally selected statistics is given enabling the fast evaluation of a large number of cutpoints. JF - Biometrics SN - 0006-341X AD - Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstraße 33, D-80539 München, Germany. TI - Generalized Maximally Selected Statistics. KW - breakpoint KW - dose-response KW - monotonic KW - r AU - Hothorn, Torsten AU - Zeileis, Achim PY - 2008/03/05/ UR - http://dx.doi.org/10.1111/j.1541-0420.2008.00995.x ER -