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» Quantile and histogram estimation
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WSC
2004
13 years 6 months ago
Experimental Performance Evaluation of Histogram Approximation for Simulation Output Analysis
We summarize the results of an experimental performance evaluation of using an empirical histogram to approximate the steady-state distribution of the underlying stochastic proces...
E. Jack Chen, W. David Kelton
KDD
2000
ACM
101views Data Mining» more  KDD 2000»
13 years 9 months ago
Incremental quantile estimation for massive tracking
Data--call records, internet packet headers, or other transaction records--are coming down a pipe at a ferocious rate, and we need to monitor statistics of the data. There is no r...
Fei Chen, Diane Lambert, José C. Pinheiro
NIPS
2007
13 years 6 months ago
How SVMs can estimate quantiles and the median
We investigate quantile regression based on the pinball loss and the ǫ-insensitive loss. For the pinball loss a condition on the data-generating distribution P is given that ensu...
Andreas Christmann, Ingo Steinwart
WSC
2007
13 years 7 months ago
Kernel estimation for quantile sensitivities
Quantiles, also known as value-at-risk in financial applications, are important measures of random performance. Quantile sensitivities provide information on how changes in the i...
Guangwu Liu, L. Jeff Hong
SIGMOD
2005
ACM
162views Database» more  SIGMOD 2005»
14 years 5 months ago
Fast and Approximate Stream Mining of Quantiles and Frequencies Using Graphics Processors
We present algorithms for fast quantile and frequency estimation in large data streams using graphics processor units (GPUs). We exploit the high computational power and memory ba...
Naga K. Govindaraju, Nikunj Raghuvanshi, Dinesh Ma...