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Self-Normalization for Time Series: A Review of Recent Developments
Xiaofeng Shao
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Dive into the research topics of 'Self-Normalization for Time Series: A Review of Recent Developments'. Together they form a unique fingerprint.
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Keyphrases
Self-normalization
100%
Change Point Detection
50%
Weakly Dependent
50%
Self-use
50%
Self-normalized
50%
Spatial Data
25%
Confidence Interval
25%
Robust Inference
25%
Distinctive Features
25%
Spatial-temporal Data
25%
Regression Model
25%
Time Series Data
25%
Time Series Regression
25%
Time-series Change
25%
Locally Stationary Time Series
25%
Stationary Time Series
25%
Two-sample Prediction
25%
Functional Time Series
25%
Fixed-b Asymptotics
25%
Dependent Errors
25%
Long Memory Time Series
25%
Blockwise Empirical Likelihood
25%
Inferential Method
25%
Nonparametric Time Series
25%
Integrated Time Series
25%
Mathematics
Stationary Time Series
100%
Changepoint Detection
100%
Asymptotics
50%
Concludes
50%
Regression Model
50%
Confidence Interval
50%
Time Series Data
50%
Spatial Data
50%
Empirical Likelihood
50%