Transient signal detection using prior information in the likelihood ratio test

1993 ◽  
Vol 41 (6) ◽  
pp. 2177-2192 ◽  
Author(s):  
M. Frisch ◽  
H. Messer
Keyword(s):  
Signal Detection ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Prior Information ◽  
Transient Signal ◽  
Ratio Test

scholarly journals A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Hongbo Zhao ◽  
Lei Chen ◽  
Wenquan Feng ◽  
Chuan Lei
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Prior Information ◽  
Finite Size ◽  
Sparse Signals ◽  
Ratio Test ◽  
Sparse Estimation ◽  
Detection Scheme ◽  
Error Exponent ◽  
Multiple Observations

Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.

scholarly journals Likelihood-Ratio-Test Methods for Drug Safety Signal Detection from Multiple Clinical Datasets

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Lan Huang ◽  
Jyoti Zalkikar ◽  
Ram Tiwari
Keyword(s):  
Signal Detection ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Drug Safety ◽  
Drug Exposure ◽  
Type I Error ◽  
Test Methods ◽  
Ratio Test ◽  
Type I ◽  
Safety Signal

Pre- and postmarket drug safety evaluations usually include an integrated summary of results obtained using data from multiple studies related to a drug of interest. This paper proposes three approaches based on the likelihood ratio test (LRT), called the LRT methods, for drug safety signal detection from large observational databases with multiple studies, with focus on identifying signals of adverse events (AEs) from many AEs associated with a particular drug or inversely for signals of drugs associated with a particular AE. The methods discussed include simple pooled LRT method and its variations such as the weighted LRT that incorporates the total drug exposure information by study. The power and type-I error of the LRT methods are evaluated in a simulation study with varying heterogeneity across studies. For illustration purpose, these methods are applied to Proton Pump Inhibitors (PPIs) data with 6 studies for the effect of concomitant use of PPIs in treating patients with osteoporosis and to Lipiodol (a contrast agent) data with 13 studies for evaluating that drug’s safety profiles.

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