Computational Statistics (CompStat) is an international journal that fosters the publication of applications and methodological research in the field of computational statistics. The journal provides a forum for computer scientists, mathematicians, and statisticians working in a variety of areas in statistics, including biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge-based systems, and Bayesian computing. CompStat papers emphasize the contribution to and influence of computing on statistics and vice versa. The journal also publishes hardware, software, and package reports. Officially cited as: Comput Stat
Computational Statistics
Description
Identifiers
ISSN | 0943-4062 |
e-ISSN | 1613-9658 |
Publisher
Springer Berlin Heidelberg
Additional information
Data set: Springer
Articles
Computational Statistics > 2019 > 34 > 4 > 1693-1710
The missing data problem is common in longitudinal or repeated measurements data. When the missingness mechanism is nonignorable, the distribution of the observed response and indicators of missingness should be modelled jointly using either ‘shared random-effects model’ or ‘correlated random-effects model’. However, computational challenges arise in the model fitting due to intractable numerical...
Computational Statistics > 2019 > 34 > 4 > 1649-1674
In clinical practice, researchers usually categorize continuous variables for risk assessment. Many algorithms have been developed to find one optimal cut point to group variables into two halves; however, there is often need to determine the optimal number of cut points and their locations at the same time. In this paper we proposed a new AIC criterion, where the AIC values were corrected with cross-validation...