107,423 research outputs found
Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests
Computerized adaptive testing is becoming increasingly popular due to
advancement of modern computer technology. It differs from the conventional
standardized testing in that the selection of test items is tailored to
individual examinee's ability level. Arising from this selection strategy is a
nonlinear sequential design problem. We study, in this paper, the sequential
design problem in the context of the logistic item response theory models. We
show that the adaptive design obtained by maximizing the item information leads
to a consistent and asymptotically normal ability estimator in the case of the
Rasch model. Modifications to the maximum information approach are proposed for
the two- and three-parameter logistic models. Similar asymptotic properties are
established for the modified designs and the resulting estimator. Examples are
also given in the case of the two-parameter logistic model to show that without
such modifications, the maximum likelihood estimator of the ability parameter
may not be consistent.Comment: Published in at http://dx.doi.org/10.1214/08-AOS614 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Fast Cross-Validation via Sequential Testing
With the increasing size of today's data sets, finding the right parameter
configuration in model selection via cross-validation can be an extremely
time-consuming task. In this paper we propose an improved cross-validation
procedure which uses nonparametric testing coupled with sequential analysis to
determine the best parameter set on linearly increasing subsets of the data. By
eliminating underperforming candidates quickly and keeping promising candidates
as long as possible, the method speeds up the computation while preserving the
capability of the full cross-validation. Theoretical considerations underline
the statistical power of our procedure. The experimental evaluation shows that
our method reduces the computation time by a factor of up to 120 compared to a
full cross-validation with a negligible impact on the accuracy
Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection
In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous
Bias correction and confidence intervals following sequential tests
An important statistical inference problem in sequential analysis is the
construction of confidence intervals following sequential tests, to which
Michael Woodroofe has made fundamental contributions. This paper reviews
Woodroofe's method and other approaches in the literature. In particular it
shows how a bias-corrected pivot originally introduced by Woodroofe can be used
as an improved root for sequential bootstrap confidence intervals.Comment: Published at http://dx.doi.org/10.1214/074921706000000590 in the IMS
Lecture Notes--Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
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