期刊： STATISTICA SINICA, 2023; 33 ()
In high-dimensional prediction problems, we propose subsampling the predictors prior to the analysis. Specifically, we draw features using random samp......
期刊： STATISTICA SINICA, 2023; 33 ()
The study considers a vector autoregressive model for high-dimensional mixed frequency data, where selective time series are collected at different fr......
期刊： STATISTICA SINICA, 2023; 33 ()
We test the number of spikes in a generalized spiked covariance matrix, the spiked eigenvalues of which may be much larger or smaller than the nonspik......
期刊： STATISTICA SINICA, 2023; 33 (1)
Motivated by applications, we propose a class of dynamic single-index varying-coefficient models to explore the varying interaction effects on the res......
期刊： STATISTICA SINICA, 2023; 33 (2)
In this study, we investigate varying-coefficient models for spatial data distributed over two-dimensional domains. First, we approximate the univaria......
期刊： STATISTICA SINICA, 2023; 33 (4)
Informative terminal events often occur in long-term recurrent event follow-up studies. To explicitly reflect the effects of such events on recurrent ......
期刊： STATISTICA SINICA, 2023; 33 (1)
We consider the problem of model selection for a high-dimensional gen-eralized estimating equation (GEE) in a marginal regression analysis for cluster......
期刊： STATISTICA SINICA, 2023; 33 ()
Estimations of high-dimensional banded covariance matrices are widely used in multivariate statistical analysis. To ensure the validity of such estima......
期刊： STATISTICA SINICA, 2023; 33 ()
We examine the problem of variable selection for high-dimensional sparse Cox models. We propose using a computationally efficient procedure, the Cheby......
期刊： STATISTICA SINICA, 2023; 33 (1)
We investigate statistical inference for the mean function of stationary functional time series data with an infinite moving average structure. We pro......
期刊： STATISTICA SINICA, 2023; 33 (1)
This study examines two-sample tests for functional time series data, which have become widely available with the advent of modern complex observation......
期刊： STATISTICA SINICA, 2023; 33 (4)
Understanding the heterogeneity over spatial locations is an important problem that has been widely studied in applications such as economics and envi......
期刊： STATISTICA SINICA, 2023; 33 ()
High-dimensional classification is an important statistical problem with applications in many areas. One widely used classifier is the linear discrimi......
期刊： STATISTICA SINICA, 2023; 33 (1)
In modern scientific research, data heterogeneity is commonly observed owing to the abundance of complex data. We propose a factor regression model fo......
期刊： STATISTICA SINICA, 2023; 33 ()
Estimating the dependence structure in the data is a key task when analyzing compositional data. Real-world compositional data sets are often complex ......
