Description:
The
Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical
aspects of
statistics and
probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing
the subject. While we maintain our traditional strength in
statistical inference, design, classical probability, and large sample
methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians,
and scientists.
We publish high quality articles in all branches of statistics, probability,
discrete mathematics,
machine
learning, and
bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes
of statistics, probability, machine learning, and general
biostatistics. Thoughtful letters to the editors, interesting problems
in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.
We want to serve as the broadest
international platform for high quality research on every aspect of our field, traditional and cutting edge. The quality and the breadth
of our
editorial
board reflects that singular priority.
