Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

Self-similar processes are stochastic processes that are invariant in distribution under
suitable time scaling, and are a subject intensively studied in the last few decades. This …

Deep learning methods operate in regimes that defy the traditional statistical mindset.
Neural network architectures often contain more parameters than training samples, and are …

We construct quantum error-correcting codes that embed a finite-dimensional code space in
the infinite-dimensional Hilbert space of rotational states of a rigid body. These codes, which …

Modern Directional Statistics collects important advances in methodology and theory for
directional statistics over the last two decades. It provides a detailed overview and analysis …

Isotropic Gaussian random fields on the sphere are characterized by Karhunen–Loève
expansions with respect to the spherical harmonic functions and the angular power …

Introducing flexible covariance functions is critical for interpolating spatial data since the
properties of interpolated surfaces depend on the covariance function used for Kriging. An …

One of the most widespread and important applications in wireless sensor networks (WSNs)
is the continuous data collection, such as monitoring the variety of ambient temperature and …

We prove a new class of inequalities, yielding bounds for the normal approximation in the
Wasserstein and the Kolmogorov distance of functionals of a general Poisson process …

The last decades have seen an unprecedented increase in the availability of data sets that
are inherently global and temporally evolving, from remotely sensed networks to climate …