ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process.The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/asmb.2381
Weak signals in high-dimension regression: detection, estimation and prediction.
来源期刊:Applied stochastic models in business and industryDOI:10.1002/ASMB.2340
Good and bad market research: A critical review of Net Promoter Score
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2417
Time series of functional data with application to yield curves
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2443
Nonlocal spatial clustering in automated brain hematoma and edema segmentation
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2431
Using Degradation Models to Assess Pipeline Life
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2489
Quantile forecasting based on a bivariate hysteretic autoregressive model with GARCH errors and time ‐varying correlations
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2479
Large‐scale automated forecasting for network safety and security monitoring
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2436
An interview with Sam C. Saunders
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2429
Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2395
Multivariate asset‐pricing model based on subordinated stable processes
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2446
Is reliability a new science? A paper from the panel session held at the 10th International Conference on Mathematical Methods in Reliability
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2442
A combined filtering approach to high‐frequency volatility estimation with mixed‐type microstructure noises
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2352
Integrative Interaction Analysis using Threshold Gradient Directed Regularization.
来源期刊:Applied stochastic models in business and industryDOI:10.1002/ASMB.2342
Bayesian semiparametric Markov switching stochastic volatility model
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2434
Discussion of “Birnbaum‐Saunders distribution: A review of models, analysis, and applications” and a novel financial extreme value data analytics from natural disasters
来源期刊:Applied Stochastic Models in Business and IndustryDOI:10.1002/ASMB.2400