Finite mixtures of skew distributions have emerged as an effective tool in modelling
heterogeneous data with asymmetric features. With various proposals appearing rapidly in …

In certain non-stationary processes, the non-stationary dynamics is caused by degradation
or wearing of certain process components. Such dynamics can be characterized by a latent …

This research studies the long-term behavior monitoring of the COVID-19 pandemic through
estimation with nonzero skewness. The COVID-19 data may contain outliers that could result …

We propose an inertial sensor-based approach to activity recognition for pedestrian indoor
navigation. In the considered scenario a mobile device is held in a hand in front of the user …

Process control can become challenging when the measurements are affected by irregular
noise. Classical approaches utilize Gaussian methods to alleviate the sensory noise …

In this paper, we present a filtering and smoothing scheme for process variables
characterized by a hidden monotonic trend. The proposed method models the transition …

In this study, the stochastic phenomenological bifurcations (P-bifurcations) of generalized
Chua's circuit (GCC) driven by skew-normal distributed noise have been investigated by …

Process industries involve processes that have complex, interdependent, and sometimes
uncontrollable/unobservable features that are subject to a variety of uncertainties such as …

Observed macroeconomic data–notably GDP growth rate, inflation and interest rates–can
be, and usually are skewed. Economists attempt to fit models to data by matching first and …

Data-driven modeling has been finding increasing prominence in process systems
engineering in both academia and industries. Latent variable modeling forms an important …