.png){kind=icon}
.png){kind=icon}
Return
A New Random Variable Normalizing Transformation With Application to the GLRT
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2017.2781235.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A means of converting a random variable into an approximate standard normal is described. It is an extension of the transformation inherent in the use of the exponential embedded family approach to multifamily likelihood ratio testing, which helps explain why the transformation employed corrects the deficiencies of the generalized likelihood ratio test.
Keywords:
Signal analysis
signal resolution
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W