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TSP
2010
13 years 4 months ago
Estimating multiple frequency-hopping signal parameters via sparse linear regression
Abstract--Frequency hopping (FH) signals have well-documented merits for commercial and military applications due to their near-far resistance and robustness to jamming. Estimating...
Daniele Angelosante, Georgios B. Giannakis, Nichol...
ISBI
2011
IEEE
13 years 1 months ago
Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression
Most algorithms used for imaging genetics examine statistical effects of each individual genetic variant, one at a time. We developed a new approach, based on ridge regression, to...
Omid Kohannim, Derrek P. Hibar, Jason L. Stein, Ne...
EPEW
2006
Springer
14 years 1 months ago
Precise Regression Benchmarking with Random Effects: Improving Mono Benchmark Results
Benchmarking as a method of assessing software performance is known to suffer from random fluctuations that distort the observed performance. In this paper, we focus on the fluctua...
Tomás Kalibera, Petr Tuma
NIPS
2003
13 years 11 months ago
Nonstationary Covariance Functions for Gaussian Process Regression
We introduce a class of nonstationary covariance functions for Gaussian process (GP) regression. Nonstationary covariance functions allow the model to adapt to functions whose smo...
Christopher J. Paciorek, Mark J. Schervish
ICASSP
2010
IEEE
13 years 10 months ago
Multiple frequency-hopping signal estimation via sparse regression
Frequency hopping (FH) signals have well-documented merits for commercial and military applications due to their near-far resistance and robustness to jamming. Estimating FH signa...
Daniele Angelosante, Georgios B. Giannakis, Nichol...