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Applied Stochastic Models in Business and Industry is a peer-reviewed journal published by Wiley, covering Statistical Distribution Estimation and Applications, Reliability and Maintenance Optimization, Financial Risk and Volatility Modeling.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Applied Stochastic Models in Business and Industry 12 (3): 45–58.
Formats any DOI in Applied Stochastic Models in Business and Industry style. No sign-up. |
| Publishes research in | Statistical Distribution Estimation and Applications Reliability and Maintenance Optimization Financial Risk and Volatility Modeling Advanced Statistical Process Monitoring Probabilistic and Robust Engineering Design |
| ISSN | 1524-1904 |
| Citation impact (2-yr) | 0.96 |
| h-index | 55 |
| i10-index | 485 |
| Total citations | 20,085 |
| Article processing charge | $3,450 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems – such as those presented in designing and pricing securities, constructing portfolios, and risk management – often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full…
Degradation models have become an important analytic tool for complex systems. During the last two decades, a number of degradation models have been developed to capture the degradation dynamics of a system and aid the subsequent decision‐makings. This paper is aimed at providing a summary of the state of the arts in the field, and…
Abstract Working with multiple regression analysis a researcher usually wants to know a comparative importance of predictors in the model. However, the analysis can be made difficult because of multicollinearity among regressors, which produces biased coefficients and negative inputs to multiple determination from presum ably useful regressors. To solve this problem we apply a tool…
Abstract A new method, the Hilbert–Huang Transform (HHT), developed initially for natural and engineering sciences has now been applied to financial data. The HHT method is specially developed for analysing non‐linear and non‐stationary data. The method consists of two parts: (1) the empirical mode decomposition (EMD), and (2) the Hilbert spectral analysis. The key part…
Abstract We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so‐called ν ‐SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.
