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Envelopes in multivariate regression models with nonlinearity and heteroscedasticity
X. Zhang, C. E. Lee,
X. Shao
Statistics
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Dive into the research topics of 'Envelopes in multivariate regression models with nonlinearity and heteroscedasticity'. Together they form a unique fingerprint.
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Keyphrases
Nonlinearity
100%
Heteroscedasticity
100%
Multivariate Regression Model
100%
Martingale Difference Divergence
100%
Mean Envelope
85%
Conditional Covariance
28%
Multivariate Linear Model
28%
Model Assumptions
14%
Parameter Estimation
14%
Estimation Method
14%
Dimensionality Reduction
14%
Real-time Data Analysis
14%
Simulation Data Analysis
14%
Conditional Mean
14%
Random Vector
14%
Nested Structure
14%
Conditional Mean Function
14%
Response Reduction
14%
Envelope Components
14%
Parametric Structure
14%
Heteroscedastic Errors
14%
Sufficient Dimension Reduction
14%
Envelope Estimation
14%
Linear Conditional Mean
14%
Mathematics
Conditionals
100%
Regression Model
100%
Heteroscedasticity
100%
Multivariate Regression
100%
Nonlinearity
100%
Covariance
40%
Multivariate Linear Model
40%
Parameter Estimation
20%
Real Data
20%
Parametric
20%
Random Vector
20%
Classical Definition
20%