Journal of the American Statistical Association
Journal of the American Statistical Association is a journal of statistical science that publishes research in statistical applications, theory and methods.
Journal of Computational and Graphical Statistics
JCGS publishes research into latest techniques on computational and graphical methods in statistics, including data analysis and numerical graphical displays.
The American Statistician
The American Statistician publishes articles on statistics, statistical practice, statistics teaching, and statistical computing and graphics.
Journal of Business & Economic Statistics
JBES publishes leading research in business and economic statistics, including topics in finance, macroeconomics and microeconomics.
Journal of Applied Statistics
Papers on the application of statistical methodology and principles to real-world problems in disciplines like ecology, economics, medicine & social sciences.
Journal of Biopharmaceutical Statistics
Publishes works on quality applications of statistics, statistical methodologies and biostatistics in biopharmaceutical research and development.
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We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis.
More than 50 years ago, John Tukey called for a reformation of academic statistics. In “The Future of Data Analysis,” he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or “data analysis.
The rise of internet-based services and products in the late 1990s brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision making. Over the past two decades, organizations such as Airbnb, Alibaba, Amazon, Baidu, Booking.com, Alphabet’s Google, LinkedIn, Lyft, Meta’s Facebook, Microsoft, Netflix, Twitter, Uber, and Yandex have invested tremendous resources in online controlled experiments (OCEs) to assess the impact of innovation on their customers and businesses.
Non-fungible tokens (NFT) have recently emerged as a novel blockchain-hosted financial asset class that has attracted major transaction volumes. However, preprocessing and analysis of NFT transaction data, which investors often rely on for their investment decisions, pose several challenges not commonly encountered in traditional financial data.
The digital clinical trial is fast emerging as a pragmatic trial that can improve a trial’s design including recruitment and retention, data collection and analytics. To that end, digital platforms such as electronic health records or wearable technologies that enable passive data collection can be leveraged, alleviating burden from the participant and study coordinator.
The goal of this article is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: (a) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, (b) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained).