Journal ArticleDOI
On the Epsilon Most Stringent Test Between Two Vector Lines in Gaussian Noise
TLDR
This paper addresses the problem of distinguishing between two vector lines observed through noisy measurements and proposes a suboptimal test, called the epsilon most stringent test, which has a very simple form and its statistical properties are expressed in closed-form.Abstract:
This paper addresses the problem of distinguishing between two vector lines observed through noisy measurements. This is a hypothesis testing problem where the two hypotheses are composite since the signal amplitudes are deterministic and not known. An ideal criterion of optimality, namely the most stringent test, consists in minimizing the maximum shortcoming of the test subject to a constrained false alarm probability. The maximum shortcoming corresponds to the maximum gap between the power function of the test and the envelope power function which is defined as the supremum of the power over all tests satisfying the prescribed false alarm probability. The most stringent test is unfortunately intractable. Hence, a suboptimal test, called the epsilon most stringent test, is proposed. This test has a very simple form and its statistical properties are expressed in closed-form. It is numerically shown that the proposed test has a small loss of optimality and that it outperforms the generalized likelihood ratio test.read more
Citations
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Statistical Signal Processing Detection Estimation And Time Series Analysis
TL;DR: This statistical signal processing detection estimation and time series analysis will help people to read a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop.
Journal ArticleDOI
Evaluating the Reliability of Macroeconomic Models.
Proceedings ArticleDOI
Learning-based epsilon most stringent test for Gaussian samples classification
Lionel Fillatre,Igor Nikiforov +1 more
TL;DR: This paper studies the problem of classifying some Gaussian samples into one of two parametric probabilistic models, also called sources, when the parameter and the a priori probability of each source are unknown.
References
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Digital communications
TL;DR: This month's guest columnist, Steve Bible, N7HPR, is completing a master’s degree in computer science at the Naval Postgraduate School in Monterey, California, and his research area closely follows his interest in amateur radio.
Book
An Introduction to Signal Detection and Estimation
TL;DR: Signal Detection in Discrete Time and Signal Estimation in Continuous Time: Elements of Hypothesis Testing and Elements of Parameter Estimation.