期刊： STATISTICAL METHODS AND APPLICATIONS, ; ()
Repeated measures designs are widely used in practice to increase power, reduce sample size, and increase efficiency in data collection. Correlation b......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2020; 29 (2)
Observational data with clustered structure may have confounding at one or more levels which when combined critically undermine result validity. We pr......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2019; 28 (1)
This paper focuses on the problem of modeling medical costs with covariates when the cost data are subject to right-censoring. The prevailing methods ......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2019; 28 (4)
In this paper, we develop a dynamic partially functional linear regression model in which the functional dependent variable is explained by the first ......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2018; 27 (2)
In this paper, we introduce an adjusted blockwise empirical likelihood (ABEL) method for long memory time series models. By dividing time series into ......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2018; 27 (4)
Using a wavelet basis, we establish in this paper upper bounds of wavelet estimation on L p(Rd) risk of regression functions with strong mixing data f......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2018; 27 (3)
In this paper, we propose a new clustering procedure for financial instruments. Unlike the prevalent clustering procedures based on time series analys......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2018; 27 (3)
For the first-order autoregressive model, we establish the asymptotic theory of the weighted least squares estimations whether the underlying autoregr......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2017; 26 (4)
This paper presents a novel framework for maximum likelihood (ML) estimation in skew-t factor analysis (STFA) models in the presence of missing values......
期刊： STATISTICAL METHODS AND APPLICATIONS, 2017; 26 (4)
Assume that a linear random-effects model y = X beta + epsilon = X( A alpha + gamma) + epsilon is transformed as Ty = TX beta + T epsilon = TX(A alpha......
