Waller, Lance A.
Published in
Annual Review of Statistics and Its Application
Maps provide a data framework for the statistical analysis of georeferenced data observations. Since the middle of the twentieth century, the field of spatial statistics has evolved to address key inferential questions relating to spatially defined data, yet many central statistical properties do not translate to spatially indexed and spatially cor...
Dümbgen, Lutz
Published in
Annual Review of Statistics and Its Application
Statistical models defined by shape constraints are a valuable alternative to parametric models or nonparametric models defined in terms of quantitative smoothness constraints. While the latter two classes of models are typically difficult to justify a priori, many applications involve natural shape constraints, for instance, monotonicity of a dens...
Carrón Duque, Javier Marinucci, Domenico
Published in
Annual Review of Statistics and Its Application
This review is devoted to recent developments in the statistical analysis of spherical data, strongly motivated by applications in cosmology. We start from a brief discussion of cosmological questions and motivations, arguing that most cosmological observables are spherical random fields. Then, we introduce some mathematical background on spherical...
Boukouvalas, Zois Shafer, Allison
Published in
Annual Review of Statistics and Its Application
With the evolution of social media, cyberspace has become the default medium for social media users to communicate, especially during high-impact events such as pandemics, natural disasters, terrorist attacks, and periods of political unrest. However, during such events, misinformation can spread rapidly on social media, affecting decision-making a...
Ke, Zheng Tracy Ji, Pengsheng Jin, Jiashun Li, Wanshan
Published in
Annual Review of Statistics and Its Application
Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. We review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, we review Topic-SCORE, a statistical approach to topic mod...
Bingham, Derek Butler, Troy Estep, Don
Published in
Annual Review of Statistics and Its Application
We describe and compare two formulations of inverse problems for a physics-based process model in the context of uncertainty and random variability: the Bayesian inverse problem and the stochastic inverse problem. We describe the foundations of the two problems in order to create a context for interpreting the applicability and solutions of inverse...
Geskus, Ronald B.
Published in
Annual Review of Statistics and Its Application
The role of competing risks in the analysis of time-to-event data is increasingly acknowledged. Software is readily available. However, confusion remains regarding the proper analysis: When and how do I need to take the presence of competing risks into account? Which quantities are relevant for my research question? How can they be estimated and wh...
Aleshin-Guendel, Serge Steorts, Rebecca C.
Published in
Annual Review of Statistics and Its Application
Entity resolution is the process of merging and removing duplicate records from multiple data sources, often in the absence of unique identifiers. Bayesian models for entity resolution allow one to include a priori information, quantify uncertainty in important applications, and directly estimate a partition of the records. Markov chain Monte Carlo...
Imbens, Guido W.
Published in
Annual Review of Statistics and Its Application
Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects are not known to the decision makers and need to be estimated from data. This fundamental problem has been known and studied for many years in many disciplines. In the past thirty years, however, the amou...
Zhou, Ling Gong, Ziyang Xiang, Pengcheng
Published in
Annual Review of Statistics and Its Application
Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that hav...
