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  • Convergence Diagnostics for Markov Chain Monte Carlo
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Vivekananda Roy

    Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is indispensable for performing Bayesian analysis. Two critical questions that MCMC practitioners need to address are where to start and when to stop the simulation. Although a great amount of research has gone into establishing

    更新日期:2020-03-09
  • Nonparametric Spectral Analysis of Multivariate Time Series
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Rainer von Sachs

    Spectral analysis of multivariate time series has been an active field of methodological and applied statistics for the past 50 years. Since the success of the fast Fourier transform algorithm, the analysis of serial auto- and cross-correlation in the frequency domain has helped us to understand the dynamics in many serially correlated data without necessarily needing to develop complex parametric

    更新日期:2020-03-09
  • Robust Small Area Estimation: An Overview
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Jiming Jiang; J. Sunil Rao

    A small area typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain. While traditional small area methods and models are widely used nowadays, there have also been much work and interest in robust statistical inference for small area estimation (SAE). We survey

    更新日期:2020-03-09
  • Representation Learning: A Statistical Perspective
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Jianwen Xie; Ruiqi Gao; Erik Nijkamp; Song-Chun Zhu; Ying Nian Wu

    Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning

    更新日期:2020-03-09
  • Q-Learning: Theory and Applications
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Jesse Clifton; Eric Laber

    Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes

    更新日期:2020-03-09
  • Bayesian Additive Regression Trees: A Review and Look Forward
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Jennifer Hill; Antonio Linero; Jared Murray

    Bayesian additive regression trees (BART) provides a flexible approach to fitting a variety of regression models while avoiding strong parametric assumptions. The sum-of-trees model is embedded in a Bayesian inferential framework to support uncertainty quantification and provide a principled approach to regularization through prior specification. This article presents the basic approach and discusses

    更新日期:2020-03-09
  • Algebraic Statistics in Practice: Applications to Networks
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Marta Casanellas; Sonja Petrović; Caroline Uhler

    Algebraic statistics uses tools from algebra (especially from multilinear algebra, commutative algebra, and computational algebra), geometry, and combinatorics to provide insight into knotty problems in mathematical statistics. In this review, we illustrate this on three problems related to networks: network models for relational data, causal structure discovery, and phylogenetics. For each problem

    更新日期:2020-03-09
  • A Survey of Tuning Parameter Selection for High-Dimensional Regression
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Yunan Wu; Lan Wang

    Penalized (or regularized) regression, as represented by lasso and its variants, has become a standard technique for analyzing high-dimensional data when the number of variables substantially exceeds the sample size. The performance of penalized regression relies crucially on the choice of the tuning parameter, which determines the amount of regularization and hence the sparsity level of the fitted

    更新日期:2020-03-09
  • Randomized Experiments in Education, with Implications for Multilevel Causal Inference
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Stephen W. Raudenbush; Daniel Schwartz

    Education research has experienced a methodological renaissance over the past two decades, with a new focus on large-scale randomized experiments. This wave of experiments has made education research an even more exciting area for statisticians, unearthing many lessons and challenges in experimental design, causal inference, and statistics more broadly. Importantly, educational research and practice

    更新日期:2020-03-09
  • Modern Algorithms for Matching in Observational Studies
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Paul R. Rosenbaum

    Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and

    更新日期:2020-03-09
  • DNA Mixtures in Forensic Investigations: The Statistical State of the Art
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Julia Mortera

    Forensic science has experienced a period of rapid change because of the tremendous evolution in DNA profiling. Problems of forensic identification from DNA evidence can become extremely challenging, both logically and computationally, in the presence of complicating features, such as in mixed DNA trace evidence. Additional complicating aspects are possible, such as missing data on individuals, heterogeneous

    更新日期:2020-03-09
  • Statistical Methods for Extreme Event Attribution in Climate Science
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Philippe Naveau; Alexis Hannart; Aurélien Ribes

    Changes in the Earth's climate have been increasingly observed. Assessing the likelihood that each of these changes has been caused by human influence is important for decision making on mitigation and adaptation policy. Because of their large societal and economic impacts, extreme events have garnered much media attention—have they become more frequent and more intense, and if so, why? To answer such

    更新日期:2020-03-09
  • Testing Statistical Charts: What Makes a Good Graph?
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Susan Vanderplas; Dianne Cook; Heike Hofmann

    It has been approximately 100 years since the very first formal experimental evaluations of statistical charts were conducted. In that time, technological changes have impacted both our charts and our testing methods, resulting in a dizzying array of charts, many different taxonomies to classify graphics, and several different philosophical approaches to testing the efficacy of charts and graphs experimentally

    更新日期:2020-03-09
  • The Role of Statistical Evidence in Civil Cases
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Joseph L. Gastwirth

    Civil cases outnumber criminal cases in federal courts, and statistical evidence has become more important in a wide variety of them. In contrast to science, which is concerned with general phenomena, legal cases concern one plaintiff or a class of plaintiffs and replication of the events that led to the case is not possible. This review describes the legal process, the way statistics are used in several

    更新日期:2020-03-09
  • Calibrating the Scientific Ecosystem Through Meta-Research
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    Tom E. Hardwicke; Stylianos Serghiou; Perrine Janiaud; Valentin Danchev; Sophia Crüwell; Steven N. Goodman; John P.A. Ioannidis

    While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or research-on-research, is a burgeoning discipline that investigates efficiency, quality, and bias in the scientific ecosystem, topics that have become especially relevant amid widespread concerns about the credibility of the scientific literature. Meta-research may help calibrate

    更新日期:2020-03-09
  • Statistical Significance
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2020-03-09
    D.R. Cox

    A broad review is given of the role of statistical significance tests in the analysis of empirical data. Four main types of application are outlined. The first, conceptually quite different from the others, concerns decision making in such contexts as medical screening and industrial inspection. The others assess the security of conclusions. The article concludes with an outline discussion of some

    更新日期:2020-03-09
  • Computational Neuroscience: Mathematical and Statistical Perspectives.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2019-04-13
    Robert E Kass,Shun-Ichi Amari,Kensuke Arai,Emery N Brown,Casey O Diekman,Markus Diesmann,Brent Doiron,Uri T Eden,Adrienne L Fairhall,Grant M Fiddyment,Tomoki Fukai,Sonja Grün,Matthew T Harrison,Moritz Helias,Hiroyuki Nakahara,Jun-Nosuke Teramae,Peter J Thomas,Mark Reimers,Jordan Rodu,Horacio G Rotstein,Eric Shea-Brown,Hideaki Shimazaki,Shigeru Shinomoto,Byron M Yu,Mark A Kramer

    Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary

    更新日期:2019-11-01
  • Precision Medicine.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2019-03-01
    Michael R Kosorok,Eric B Laber

    Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized

    更新日期:2019-11-01
  • Multiple Systems Estimation (or Capture-Recapture Estimation) to Inform Public Policy.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2018-07-27
    Sheila M Bird,Ruth King

    Estimating population sizes has long been of interest, from the estimation of the human or ecological population size within regions or countries to the hidden number of civilian casualties in a war. Total enumeration of the population, for example, via a census, is often infeasible or simply impractical. However, a series of partial enumerations or observations of the population is often possible

    更新日期:2019-11-01
  • Bayesian Modeling and Analysis of Geostatistical Data.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2018-02-03
    Alan E Gelfand,Sudipto Banerjee

    The most prevalent spatial data setting is, arguably, that of so-called geostatistical data, data that arise as random variables observed at fixed spatial locations. Collection of such data in space and in time has grown enormously in the past two decades. With it has grown a substantial array of methods to analyze such data. Here, we attempt a review of a fully model-based perspective for such data

    更新日期:2019-11-01
  • Two-Part and Related Regression Models for Longitudinal Data.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2017-09-12
    V T Farewell,D L Long,B D M Tom,S Yiu,L Su

    Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular

    更新日期:2019-11-01
  • Statistical Methods in Integrative Genomics.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2016-08-03
    Sylvia Richardson,George C Tseng,Wei Sun

    Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis

    更新日期:2019-11-01
  • Bayes and the Law.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2016-07-12
    Norman Fenton,Martin Neil,Daniel Berger

    Although the last forty years has seen considerable growth in the use of statistics in legal proceedings, it is primarily classical statistical methods rather than Bayesian methods that have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This paper reviews the potential and actual use of Bayes in the law

    更新日期:2019-11-01
  • Multiset Statistics for Gene Set Analysis.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2015-04-29
    Michael A Newton,Zhishi Wang

    An important data analysis task in statistical genomics involves the integration of genome-wide gene-level measurements with preexisting data on the same genes. A wide variety of statistical methodologies and computational tools have been developed for this general task. We emphasize one particular distinction among methodologies, namely whether they process gene sets one at a time (uniset) or simultaneously

    更新日期:2019-11-01
  • Dynamic Treatment Regimes.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2014-11-18
    Bibhas Chakraborty,Susan A Murphy

    A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision

    更新日期:2019-11-01
  • Brain Imaging Analysis.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2014-10-14
    F Dubois Bowman

    The increasing availability of brain imaging technologies has led to intense neuroscientific inquiry into the human brain. Studies often investigate brain function related to emotion, cognition, language, memory, and numerous other externally induced stimuli as well as resting-state brain function. Studies also use brain imaging in an attempt to determine the functional or structural basis for psychiatric

    更新日期:2019-11-01
  • Statistics and Related Topics in Single-Molecule Biophysics.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2014-07-11
    Hong Qian,S C Kou

    Since the universal acceptance of atoms and molecules as the fundamental constituents of matter in the early twentieth century, molecular physics, chemistry and molecular biology have all experienced major theoretical breakthroughs. To be able to actually "see" biological macromolecules, one at a time in action, one has to wait until the 1970s. Since then the field of single-molecule biophysics has

    更新日期:2019-11-01
  • Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data.
    Annu. Rev. Stat. Appl. (IF 5.095) Pub Date : 2014-06-24
    Kenneth Lange,Jeanette C Papp,Janet S Sinsheimer,Eric M Sobel

    Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing. With the advent of inexpensive DNA sequencing, the transition is only accelerating. This brief review highlights some modern techniques with recent successes in statistical genetics. These include: (a) lasso penalized regression and association mapping, (b) ethnic admixture estimation

    更新日期:2019-11-01
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