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  • Articles  (161)
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  • Articles  (161)
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  • 1
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    Sibling Comparison Studies (2021)
    Annual Reviews
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    Publication Date: 2021-10-21
    Description: Unmeasured confounding is one of the main sources of bias in observational studies. A popular way to reduce confounding bias is to use sibling comparisons, which implicitly adjust for several factors in the early environment or upbringing without requiring them to be measured or known. In this article we provide a broad exposition of the statistical analysis methods for sibling comparison studies. We further discuss a number of methodological challenges that arise in sibling comparison studies. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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  • 2
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    Perspective on Data Science (2021)
    Annual Reviews
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    Publication Date: 2021-09-16
    Description: The field of data science currently enjoys a broad definition that includes a wide array of activities which borrow from many other established fields of study. Having such a vague characterization of a field in the early stages might be natural, but over time maintaining such a broad definition becomes unwieldy and impedes progress. In particular, the teaching of data science is hampered by the seeming need to cover many different points of interest. Data scientists must ultimately identify the core of the field by determining what makes the field unique and what it means to develop new knowledge in data science. In this review we attempt to distill some core ideas from data science by focusing on the iterative process of data analysis and develop some generalizations from past experience. Generalizations of this nature could form the basis of a theory of data science and would serve to unify and scale the teaching of data science to large audiences. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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  • 3
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    Methods Based on Semiparametric Theory for Analysis in the Presence of Missing Data (2021)
    Annual Reviews
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    Publication Date: 2021-08-20
    Description: A statistical model is a class of probability distributions assumed to contain the true distribution generating the data. In parametric models, the distributions are indexed by a finite-dimensional parameter characterizing the scientific question of interest. Semiparametric models describe the distributions in terms of a finite-dimensional parameter and an infinite-dimensional component, offering more flexibility. Ordinarily, the statistical model represents distributions for the full data intended to be collected. When elements of these full data are missing, the goal is to make valid inference on the full-data-model parameter using the observed data. In a series of fundamental works, Robins, Rotnitzky, and colleagues derived the class of observed-data estimators under a semiparametric model assuming that the missingness mechanism is at random, which leads to practical, robust methodology for many familiar data-analytic challenges. This article reviews semiparametric theory and the key steps in this derivation. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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  • 4
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    Recent Challenges in Actuarial Science (2021)
    Annual Reviews
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    Publication Date: 2021-08-20
    Description: For centuries, mathematicians and, later, statisticians, have found natural research and employment opportunities in the realm of insurance. By definition, insurance offers financial cover against unforeseen events that involve an important component of randomness, and consequently, probability theory and mathematical statistics enter insurance modeling in a fundamental way. In recent years, a data deluge, coupled with ever-advancing information technology and the birth of data science, has revolutionized or is about to revolutionize most areas of actuarial science as well as insurance practice. We discuss parts of this evolution and, in the case of non-life insurance, show how a combination of classical tools from statistics, such as generalized linear models and, e.g., neural networks contribute to better understanding and analysis of actuarial data. We further review areas of actuarial science where the cross fertilization between stochastics and insurance holds promise for both sides. Of course, the vastness of the field of insurance limits our choice of topics; we mainly focus on topics closer to our main areas of research. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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  • 5
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    Flexible Models for Complex Data with Applications (2021)
    Annual Reviews
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    Publication Date: 2021-03-07
    Description: Probability distributions are the building blocks of statistical modeling and inference. It is therefore of the utmost importance to know which distribution to use in what circumstances, as wrong choices will inevitably entail a biased analysis. In this article, we focus on circumstances involving complex data and describe the most popular flexible models for these settings. We focus on the following complex data: multivariate skew and heavy-tailed data, circular data, toroidal data, and cylindrical data. We illustrate the strength of flexible models on the basis of concrete examples and discuss major applications and challenges.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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  • 6
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    Distance-Based Statistical Inference (2021)
    Annual Reviews
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    Publication Date: 2021-03-07
    Description: Statistical distances, divergences, and similar quantities have an extensive history and play an important role in the statistical and related scientific literature. This role shows up in estimation, where we often use estimators based on minimizing a distance. Distances also play a prominent role in hypothesis testing and in model selection. We review the statistical properties of distances that are often used in scientific work, present their properties, and show how they compare to each other. We discuss an approximation framework for model-based inference using statistical distances. Emphasis is placed on identifying in what sense and which statistical distances can be interpreted as loss functions and used for model assessment. We review a special class of distances, the class of quadratic distances, connect it with the classical goodness-of-fit paradigm, and demonstrate its use in the problem of assessing model fit. These methods can be used in analyzing very large samples.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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  • 7
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    Simulation and Analysis Methods for Stochastic Compartmental Epidemic Models (2021)
    Annual Reviews
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    Publication Date: 2021-03-07
    Description: This article considers simulation and analysis of incidence data using stochastic compartmental models in well-mixed populations. Several simulation approaches are described and compared. Thereafter, we provide an overview of likelihood estimation for stochastic models. We apply one such method to a real-life outbreak data set and compare models assuming different kinds of stochasticity. We also give references for other publications where detailed information on this topic can be found.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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  • 8
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    Consequences of Asking Sensitive Questions in Surveys (2021)
    Annual Reviews
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    Publication Date: 2021-03-07
    Description: I review selected articles from the survey methodology literature on the consequences of asking sensitive questions in censuses and surveys, using a total survey error (TSE) framework. I start with definitions of sensitive questions and move to examination of the impact of including sensitive questions on various sources of survey error—specifically, survey respondents’ willingness to participate in a survey (unit nonresponse), their willingness to respond to next rounds of interviews (wave nonresponse), their likelihood to provide an answer to sensitive questions after agreeing to participate in the survey (item nonresponse), and the accuracy of respondents’ answers to sensitive questions (measurement error). I also review the simultaneous impact of sensitive questions on multiple sources of error in survey estimates and discuss strategies to mitigate the impact of asking sensitive questions on measurement errors. I conclude with a summary and suggestions for future research.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
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  • 9
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    Statistical Evaluation of Medical Tests (2021)
    Annual Reviews
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    Publication Date: 2021-03-07
    Description: In this review, we present an overview of the main aspects related to the statistical evaluation of medical tests for diagnosis and prognosis. Measures of diagnostic performance for binary tests, such as sensitivity, specificity, and predictive values, are introduced, and extensions to the case of continuous-outcome tests are detailed. Special focus is placed on the receiver operating characteristic (ROC) curve and its estimation, with emphasis on the topic of covariate adjustment. The extension to the case of time-dependent ROC curves for evaluating prognostic accuracy is also touched upon. We apply several of the approaches described to a data set derived from a study aimed to evaluate the ability of homeostasis model assessment of insulin resistance (HOMA-IR) levels to identify individuals at high cardio-metabolic risk and how such discriminatory ability might be influenced by age and gender. We also outline software available for the implementation of the methods.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
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  • 10
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    Statistical Applications in Educational Measurement (2021)
    Annual Reviews
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    Publication Date: 2021-03-07
    Description: Educational measurement assigns numbers to individuals based on observed data to represent individuals’ educational properties such as abilities, aptitudes, achievements, progress, and performance. The current review introduces a selection of statistical applications to educational measurement, ranging from classical statistical theory (e.g., Pearson correlation and the Mantel–Haenszel test) to more sophisticated models (e.g., latent variable, survival, and mixture modeling) and statistical and machine learning (e.g., high-dimensional modeling, deep and reinforcement learning). Three main subjects are discussed: evaluations for test validity, computer-based assessments, and psychometrics informing learning. Specific topics include item bias detection, high-dimensional latent variable modeling, computerized adaptive testing, response time and log data analysis, cognitive diagnostic models, and individualized learning.
    Print ISSN: 2326-8298
    Electronic ISSN: 2326-831X
    Topics: Mathematics
    Published by Annual Reviews
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