About this Research Topic
Nonparametric methods form a major part of statistical and econometrics theory and applications. Since the 1970s, the development of semi- and non- parametric models and techniques have flourished. The era of Big Data and Artificial Intelligence applications has opened the doors for researchers to develop new methods of utilizing the huge amount of data in every aspect of our modern society.
Yet the key question remains: What are the roles of nonparametric techniques in the era of big data? We welcome methodological research papers and other contributions on how nonparametric techniques are used to solve problems involving big data. The Article Collection will also bring together theoretical, practical, and state-of-the-art applications of mathematics, probability, and statistical techniques in econometrics and statistics in the era of big data.
We invite researchers to contribute original research articles and other contributions that advance the use of nonparametric techniques in econometrics and statistics. Topics include but are by no means limited to high-dimensional data analysis, Bayesian methodology, network inferences, generalized modeling, and expository/research papers that make the case for or against the importance of nonparametric modeling in the era of big data. Topics in the following areas are especially encouraged:
- Risk modeling in modern finance using big data and artificial intelligence;
- Nonparametric predictive regression;
- Nonparametric Estimation and Forecasting of Financial Time Series;
- Distributive computational methods for nonparametric statistics and econometrics models with big data.
Keywords: non-parametric, nonparametric, techniques, big data, statistics, econometrics, risk modeling
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.