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Accession Number ADA581726
Title Confidence Intervals for Binary Responses-R50 & the Logistic Model.
Publication Date Oct 2012
Media Count 14p
Personal Author A. M. Hurwitz
Abstract Logistic regression is a non-linear method for modeling a binary response variable. For example, y = (success, failure) for blip-scan radar detections. Such responses cannot be modeled using regular linear regression. In our work, many applications of logistic regression present themselves. In the present discussion, models allowing independent slopes and independent intercepts are considered for comparing multiple groups of measures. The question that we consider here is the construction of a confidence interval about the difference in the radar 'Range 50' (R50) values for two logistic curves with each value (viz. R1, R0) arising from the separate curve. R50 represents the range at which radar achieves 50% detection probability. This problem is the same as the problem of prediction of the LD50 ('lethal dose/effective dose 50 %') value in medical science. We approach the problem analytically using parametric methods. A feature is the use of 'inverse prediction' or calibration methods. Our results are based on the large-sample properties of Maximum Likelihood estimation, and improve on results based on the least-squares model. The application is also given for general Rp/Lp 'that is, range/dose values not equal to R50. Results for large and small samples are checked against a 'truth source' generated using a Bootstrap program.
Keywords Binary arithmetic
Confidence limits
Linear regression analysis
Nonlinear systems
Regression analysis

Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
63H - Radiofrequency Detection
Corporate Author Test Wing (6510th), Edwards AFB, CA.
Document Type Technical report
Title Note Presentation 1 Oct 2013-30 Oct 2013.
NTIS Issue Number 1325
Contract Number N/A

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