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EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments
Haider Raza,
G Prasad
, Yuhua Li
School of Computing, Eng & Intel. Sys
Faculty Of Computing, Eng. & Built Env.
Research output
:
Contribution to journal
›
Article
›
peer-review
70
Citations (Scopus)
435
Downloads (Pure)
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Dive into the research topics of 'EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments'. Together they form a unique fingerprint.
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Computer Science
Model
100%
Detection Method
100%
Covariate Shift
100%
Exponentially Weighted Moving Average
100%
Multivariate Time Series
75%
Real World
50%
Hypothesis Test
50%
Control
25%
Algorithms
25%
Blind Signal Separation
25%
Data Distribution
25%
Detection Performance
25%
Continuous Monitoring
25%
Orthogonal Transformation
25%
Mathematics
Covariate
100%
Moving Average Model
100%
Exponentially Weighted Moving Average
100%
Univariate
50%
Statistical Hypothesis
50%
Real World System
25%
Points
25%
Continuous System
25%
Crosscorrelation
25%
Stationary Time Series
25%
False Alarm
25%
Immunology and Microbiology
Time
100%
Face
14%