Signal detection theory (SDT) is an application of statistical decision theory used to detect a signal embedded in noise. SDT is used in psychophysical studies of detection, recognition, and discrimination, and in other areas such as medical research, weather forecasting, survey research, and marketing research.
A general approach to estimating the parameters of the signal detection model is via the use of the
generalized linear model . For example, DeCarlo (1998) shows how signal detection models based on different underlying distributions can easily be considered by using the
generalized linear model with different
link functions .
For discussion of the generalized linear model and the link functions which it uses, see the
Generalized Linear Models chapter.
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