The following pages link to Jinyuan Chang (Q282443):
Displaying 29 items.
- High dimensional stochastic regression with latent factors, endogeneity and nonlinearity (Q82524) (← links)
- Principal component analysis for second-order stationary vector time series (Q82525) (← links)
- Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood (Q282446) (← links)
- (Q342661) (redirect page) (← links)
- Cramér-type moderate deviations for Studentized two-sample \(U\)-statistics with applications (Q342663) (← links)
- Marginal empirical likelihood and sure independence feature screening (Q385789) (← links)
- (Q449967) (redirect page) (← links)
- On the approximate maximum likelihood estimation for diffusion processes (Q449968) (← links)
- Confidence regions for entries of a large precision matrix (Q1668572) (← links)
- A new scope of penalized empirical likelihood with high-dimensional estimating equations (Q1990574) (← links)
- High dimensional generalized empirical likelihood for moment restrictions with dependent data (Q2343775) (← links)
- Simulation‐based hypothesis testing of high dimensional means under covariance heterogeneity (Q4556714) (← links)
- Peter Hall's Contribution to Empirical Likelihood (Q4558590) (← links)
- Testing for unit roots based on sample autocovariances (Q5081571) (← links)
- OUP accepted manuscript (Q5113007) (← links)
- Double-bootstrap methods that use a single double-bootstrap simulation (Q5247443) (← links)
- Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering (Q5347400) (← links)
- Erratum: Testing for high-dimensional white noise using maximum cross-correlations (Q5384438) (← links)
- Testing for high-dimensional white noise using maximum cross-correlations (Q5384447) (← links)
- A frequency domain analysis of the error distribution from noisy high-frequency data (Q5384590) (← links)
- High-dimensional empirical likelihood inference (Q5857982) (← links)
- Estimation of Subgraph Densities in Noisy Networks (Q5881090) (← links)
- Testing the martingale difference hypothesis in high dimension (Q6108287) (← links)
- Optimal covariance matrix estimation for high-dimensional noise in high-frequency data (Q6150511) (← links)
- An autocovariance-based learning framework for high-dimensional functional time series (Q6150516) (← links)
- Central limit theorems for high dimensional dependent data (Q6178582) (← links)
- Edge differentially private estimation in the \(\beta\)-model via jittering and method of moments (Q6550969) (← links)
- Statistical Inferences for Complex Dependence of Multimodal Imaging Data (Q6567943) (← links)
- Modelling matrix time series via a tensor CP-decomposition (Q6600880) (← links)
