This study provides a holistic and quantitative overview of over 800 mathematical methods
(eg, financial and risk models, statistical tests, statistics and advanced algorithms) taken out …

ABSTRACT A computationally simple approach to inference in state space models is
proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an …

In this work, we introduce a new modeling and inferential tool for dynamical processing of
time series. The approach is called recurrent dictionary learning (RDL). The proposed model …

This paper presents a methodology to estimate donations for non-profit hunger relief
organizations. These organizations are committed to alleviating hunger around the world …

This paper investigates the existence of herding movements towards several systematic risk
factors derived from the Capital Asset Pricing Model (CAPM) and its extensions. The …

This paper aims to discuss some practical problems on linear state space models with
estimated parameters. While the existing research focuses on the prediction mean square …

The problem of finding appropriate weights for combining several density forecasts is an
important issue that is currently being debated in the forecast combination literature. A …

A new approach to inference in state space models is proposed, based on approximate
Bayesian computation (ABC). ABC avoids evaluation of the likelihood function by matching …

This work proposes a new approach for dynamical modeling; we call it sequential transform
learning. This is loosely based on the transform (analysis dictionary) learning formulation …

This paper proposes and discusses a new procedure to estimate the forecast distribution for
nonlinear autoregressive time series. The approach employs a feed-forward neural network …