Advances in optical manipulation and observation of neural activity have set the stage for
widespread implementation of closed-loop and activity-guided optical control of neural …

This new edition of this classic title, now in its seventh edition, presents a balanced and
comprehensive introduction to the theory, implementation, and practice of time series …

The primary objective of this paper is threefold. First, to undertake a retrospective view of Mis‐
Specification (M‐S) testing, going back to the early 20th century, with a view to (i) place it in …

Self-normalized processes are of common occurrence in probabilistic and statistical studies.
A prototypical example is Student's t-statistic introduced in 1908 by Gosset, whose portrait is …

Stock market prediction has attracted much attention from academia as well as business.
However, it is a challenging research topic, in which many advanced computational …

We have applied a Long Short-Term Memory neural network to model S&P 500 volatility,
incorporating Google domestic trends as indicators of the public mood and macroeconomic …

We analyse the relationships between return calculation methods, risk and observation
periods. We show that the mean of a return set calculated using logarithmic returns is less …

Through the exogenous growth model, the inflow of international investments promotes
economic growth by the inclusion of foreign technologies in production functions and capital …

In this book, we study stochastic volatility models and methods of pricing, hedging, and
estimation. Among models, we will study models with heavy tails and long memory or long …

In this paper we consider the estimation of the weights of optimal portfolios from the
Bayesian point of view under the assumption that the conditional distributions of the …