Research into functional and object data analysis has been rapidly expanding in the last few decades and increasingly covers data that extend beyond smooth curves, such as longitudinal data, data snippets, and time series. Some functional and related data reside in nonlinear spaces such as density functions, covariances, and images, and can be viewed as samples of random objects. The corresponding methodology is becoming widely popular and is used in many fields including medicine, biology, public health, engineering, finance, economics, and environmental sciences. These developments are leading to many new challenges in estimation, inference, prediction, and computation.
In line with the mission of CJS to promote the interface between statistical theory, methodology, and applications, we seek submissions that include innovative theory, methodology, or novel applications in functional and object data analysis. The accepted papers will be published in a special issue of CJS.
The submission deadline is 31 January 2021. Papers may be submitted here.