11 results match your criteria Annual Review Of Statistics And Its Application[Journal]

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Computational Neuroscience: Mathematical and Statistical Perspectives.

Annu Rev Stat Appl 2018 Mar 8;5:183-214. Epub 2017 Dec 8.

Boston University, Boston, MA, USA, 02215.

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 strengths of mechanistic theory and the statistical paradigm. Read More

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http://dx.doi.org/10.1146/annurev-statistics-041715-033733DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454918PMC

Multiple Systems Estimation (or Capture-Recapture Estimation) to Inform Public Policy.

Annu Rev Stat Appl 2018 Mar;5:95-118

University of Edinburgh, School of Mathematics, Edinburgh EH9 3FD.

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. Read More

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http://dx.doi.org/10.1146/annurev-statistics-031017-100641DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6055983PMC
March 2018
11 Reads

Bayesian Modeling and Analysis of Geostatistical Data.

Annu Rev Stat Appl 2017 Mar 28;4:245-266. Epub 2016 Nov 28.

Department of Biostatistics, University of California, Los Angeles, California 90095-1772.

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. Read More

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http://dx.doi.org/10.1146/annurev-statistics-060116-054155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5790124PMC
March 2017
2 Reads

Two-Part and Related Regression Models for Longitudinal Data.

Annu Rev Stat Appl 2017 Mar;4:283-315

Medical Research Council Biostatistics Unit, Institute of Public Health, University of Cambridge, Cambridge CB2 0SR, United Kingdom.

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. Read More

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http://dx.doi.org/10.1146/annurev-statistics-060116-054131DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5590716PMC

Statistical Methods in Integrative Genomics.

Annu Rev Stat Appl 2016 Jun 18;3:181-209. Epub 2016 Apr 18.

Department of Biostatistics, Department of Genetics, University of North Carolina, Chapel Hill, NC 27599; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 27516.

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 on the motivation and rationale of these methods. We conclude with some summary points and future research directions. Read More

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http://dx.doi.org/10.1146/annurev-statistics-041715-033506DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963036PMC
June 2016
3 Reads

Bayes and the Law.

Annu Rev Stat Appl 2016 Jun 9;3:51-77. Epub 2016 Mar 9.

School of Electronic Engineering and Computer Science, Queen Mary University London, London E1 4NS, United Kingdom.

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 and explains the main reasons for its lack of impact on legal practice. Read More

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http://dx.doi.org/10.1146/annurev-statistics-041715-033428DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934658PMC
June 2016
2 Reads

Multiset Statistics for Gene Set Analysis.

Annu Rev Stat Appl 2015 Apr;2:95-111

Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.

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 via some multiset technique. Read More

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http://dx.doi.org/10.1146/annurev-statistics-010814-020335DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405258PMC
April 2015
22 Reads

Dynamic Treatment Regimes.

Annu Rev Stat Appl 2014 ;1:447-464

Department of Statistics and Institute for Social Research, University of Michigan, Ann Arbor, USA, 48109.

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 . They provide one way to operationalize a . Read More

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http://dx.doi.org/10.1146/annurev-statistics-022513-115553DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231831PMC
January 2014
4 Reads

Brain Imaging Analysis.

Authors:
F Dubois Bowman

Annu Rev Stat Appl 2014 Jan;1:61-85

Department of Biostatistics and Bioinformatics, Emory University, Center for Biomedical Imaging Statistics, Atlanta, GA.

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 or neurological disorders and, with respect to brain function, to further examine the responses of these disorders to treatment. Read More

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http://dx.doi.org/10.1146/annurev-statistics-022513-115611DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4189192PMC
January 2014

Statistics and Related Topics in Single-Molecule Biophysics.

Authors:
Hong Qian S C Kou

Annu Rev Stat Appl 2014 Jan;1:465-492

Department of Statistics, Harvard University, MA 02138.

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 witnessed extensive growth both in experiments and theory. Read More

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http://dx.doi.org/10.1146/annurev-statistics-022513-115535DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4084599PMC
January 2014
1 Read

Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data.

Annu Rev Stat Appl 2014 Jan;1(1):279-300

Dept of Human Genetics, UCLA.

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. Read More

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http://dx.doi.org/10.1146/annurev-statistics-022513-115638DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4062304PMC
January 2014
8 Reads
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