This special issue was organized on the occasion of the 13th Workshop on Stochastic Models, Statistics and Their Applications (SMSA 2017) held at Humboldt University of Berlin on February 20-24, 2017. Key areas of the workshop focused on models and methods for problems arising in industry, finance, natural sciences, engineering, and technology. In these areas, solutions are needed, which are able to deal with complex and possibly high-dimensional data, make use of innovative methodologies, and have proven applicability to real-world data. In this special issue, we have 13 papers. The papers consider stochastic models for diverse data sets and streams such as bridge vehicle loads, watermark identification data, text documents, images of digitized handwritten digits, financial asset returns, option prices, and equity data. The methods and algorithms required to handle come from machine learning, stochastic processes, sequential (on-line) change-point detection, regression analysis, image analysis, and robust divergence measures. The work of Barbulescu analyzes the properties of Generalized Regression Neural Network (GRNN) model applied to the prediction of the selected Romanian stock market prices. In the work of Luo et al, a nonlinear PDE model to estimate the American call options and put options under financial crisis scenario is implemented with additional penalty introduced to Black-Scholes formula. The work of Liu et al proposes a compound Poisson bivariate process model of vehicle load with Levy copula function and validates it in a reliability analysis of bridges in China. Migalska proposes in her paper a density-free symmetry verification test for arbitrary symmetries in images. In their work, Darkhovsky et al propose a novel method based on the notion of ϵ-complexity of continuous vector functions for an off-line detection of multiple change-points in multidimensional time series. The work of Rafajlowicz et al proposes a method of spike detection and suppression (SDS method), which extends the jump detector proposed by the Authors in their earlier work and applies it for a laser power control in a 3D additive manufacturing process. Sliwinski et al propose two algorithms for nonlinearity recovery in Hammerstein systems, one based on orthogonal series expansions and the other using aggregation and validate them in numerical simulations. The work of Sousa et al provides an insight into the behavior of GARCH process describing financial markets affected by rumours and other random factors through an analysis of process mean and variance by estimating run length. Kisslinger et al describe a new toolkit based on scaled Bregman divergences for detecting distributional changes in random data, including streams and clouds. The work of Sarnowski et al derives an optimal decision function for a sudden change detection in political business cycle model based on the Markovian process. The work of Walkowiak et al proposes a two-step procedure for open-set classification of text documents and validates it on simulated data and on the collection of Wikipedia documents. The work by Xie et al combines two-tier stochastic frontier model with the traditional Richardson Model and applies it to the Chinese stock market analysis. Finally, the work of Yuan et al derives the Hamilton-Jacobi-Bellman equation for an optimal consumption problem of maximizing the expected discounted value of utility in an economic growth model with random technological shocks. We thank numerous anonymous reviewers for helping in evaluating all submitted manuscripts.
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Jan 25, 2024
Cristina Balint 
Open Access