Nonparametric analysis of signal detection confidence ratings

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings.

How should we measure metacognitive ("type 2") sensitivity, i.e. the efficacy with which observers' confidence ratings discriminate between their own correct and incorrect stimulus classifications? We argue that currently available methods are inadequate because they are influenced by factors such as response bias and type 1 sensitivity (i.e. ability to distinguish stimuli). Extending the signa...

متن کامل

Adaptive Nonparametric Confidence Sets

We construct honest confidence regions for a Hilbert space-valued parameter in various statistical models. The confidence sets can be centered at arbitrary adaptive estimators, and have diameter which adapts optimally to a given selection of models. The latter adaptation is necessarily limited in scope. We review the notion of adaptive confidence regions, and relate the optimal rates of the dia...

متن کامل

Nonparametric Spectral-Spatial Anomaly Detection

Due to abundant spectral information contained in the hyperspectral images, they are suitable data for anomalous targets detection. The use of spatial features in addition to spectral ones can improve the anomaly detection performance. An anomaly detector, called nonparametric spectral-spatial detector (NSSD), is proposed in this work which utilizes the benefits of spatial features and local st...

متن کامل

On nonparametric confidence set estimation

The problem of adaptive estimation of regression function from noisy observations is considered in the paper. We provide an adaptive confidence set B̂N of level 1− α, 0 < α < 1, for the unknown function f . Here B̂N is a L2-ball of (random) diameter τ̂N , centered at the wavelet adaptive estimate f̂N . We show that if it is known a priori that f belongs to a Besov functional class F∗, then the prop...

متن کامل

Confidence Intervals for Nonparametric Regression

In non-parametric function estimation, providing a confidence interval with the right coverage is a challenging problem. This is especially the case when the underlying function has a wide range of unknown degrees of smoothness. Here we propose two methods of constructing an average coverage confidence interval built from block shrinkage estimation methods. One is based on the James-Stein shrin...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Behavior Research Methods & Instrumentation

سال: 1977

ISSN: 0005-7878

DOI: 10.3758/bf03214001