Communications in Statistics

Communications in Statistics is a peer-reviewed scientific journal that publishes papers related to statistics. It is published by Taylor & Francis in two series, Theory and Methods and Simulation and Computation.

Communications in Statistics - Theory and Methods
Publication details
Publication history
Standard abbreviations
Commun. Stat. - Theory Methods
Comm. Statist. Theory Methods
ISSN0361-0926 (print)
1532-415X (web)
OCLC no.48483352
Communications in Statistics - Simulation and Computation
Publication details
Publication history
Standard abbreviations
Commun. Stat. - Simul. Comput.
Comm. Statist. Simulation Comput.
ISSN0361-0918 (print)
1532-4141 (web)
OCLC no.48483321

Communications in Statistics – Theory and Methods

This series started publishing in 1970, and publishes papers related to statistical theory and methods. It publishes 20 issues each year. Based on Web of Science, the five most cited papers in the journal are:[1]

  • Kulldorff M. A spatial scan statistic, 1997, 982 cites.
  • Holland PW, Welsch RE. Robust regression using iteratively reweighted least-squares, 1977, 526 cites.
  • Sugiura N. Further analysts of the data by Akaike's information criterion and the finite corrections, 1978, 490 cites.
  • Hosmer DW, Lemesbow S. Goodness of fit tests for the multiple logistic regression model, 1980, 401 cites.
  • Iman RL, Conover WJ. Small sample sensitivity analysis techniques for computer models.with an application to risk assessment, 1980, 312 cites.

Abstracting and indexing

Communications in Statistics – Theory and Methods is indexed in the following services:

Communications in Statistics – Simulation and Computation

This series started publishing in 1972, and publishes papers related to computational statistics. It publishes 6 issues each year. Based on Web of Science, the five most cited papers in the journal are:[2]

  • Iman RL, Conover WJ. A distribution-free approach to inducing rank correlation among input variables, 1982, 519 cites.
  • Wolfinger R. Covariance structure selection in general mixed models, 1993, 248 cites.
  • Helland IS, On the structure of partial least squares regression, 1988, 246 cites.
  • McCulloch JH. Simple consistent estimators of stable distribution parameters, 1986, 191 cites.
  • Sullivan Pepe M, Anderson GL. A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data, 1994, 162 cites.

Abstracting and indexing

Communications in Statistics – Simulation and Computation is indexed in the following services:

  • Current Index to Statistics
  • Science Citation Index Expanded
  • Zentralblatt MATH


  1. ^ Taylor and Francis. "Communications in Statistics - Theory and Methods, Most Cited Articles". Retrieved 2015-03-28.
  2. ^ Taylor and Francis. "Communications in Statistics - Simulation and Computation, Most Cited Articles". Retrieved 2015-03-28.

External links

Albert Vexler

Albert Vexler was born in Birobidzhan, The Jewish Autonomous Oblast, USSR on 23 September 1971. His Ph.D. degree in Statistics and Probability Theory was obtained from the Hebrew University of Jerusalem in 2003. His Ph.D. advisor was Marcy Bogen Professor, Fellow of the American Statistical Association. Dr. Vexler was a postdoctoral research fellow in the Biometry and Mathematical Statistics Branch at the National Institute of Child Health and Human Development. Currently, Dr. Vexler is a tenured Full Professor at the State University of New York at Buffalo, Department of Biostatistics. Dr. Vexler has authored and co-authored various publications that contribute to both the theoretical and applied aspects of statistics. His papers and statistical software developments have appeared in statistical and biostatistical journals, which have the top rated impact factors and are historically recognized as the leading scientific journals. Dr. Vexler was awarded a National Institutes of Health (NIH) grant to develop novel nonparametric data analysis and statistical methodology. The results of this effort can be found via a public access resource housed by the US National Library of Medicine.

Dr. Albert Vexler has belonged to the first cohort of investigators that proposed and discovered novel density-based empirical likelihood methodology. He has introduced the density-based empirical likelihood approach for creating nonparametric test statistics that efficiently approximate optimal parametric Neyman-Pearson statistics using minimum distribution assumptions on data. Recently, several statistical academic books referred the density-based empirical likelihood methodology to classical statistical procedures.

Professor Vexler is Associate Editor for Biometrics and Journal of Applied Statistics. These journals belong to the first cohort of academic literature related to the methodology of biostatistical and epidemiological research and clinical trials.

Comparison of statistics journals

This is a comparison of peer-reviewed scientific journals published in the field of statistics.

Computational statistics

Computational statistics, or statistical computing, is the interface between statistics and computer science. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education.As in traditional statistics the goal is to transform raw data into knowledge, but the focus lies on computer intensive statistical methods, such as cases with very large sample size and non-homogeneous data sets.The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro (a former president of the International Association for Statistical Computing) proposed making a distinction, defining 'statistical computing' as "the application of computer science to statistics",

and 'computational statistics' as "aiming at the design of algorithm for implementing

statistical methods on computers, including the ones unthinkable before the computer

age (e.g. bootstrap, simulation), as well as to cope with analytically intractable problems" [sic].The term 'Computational statistics' may also be used to refer to computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial neural networks and generalized additive models.

Gauss Moutinho Cordeiro

Gauss Moutinho Cordeiro (born April 17, 1952) is a Brazilian engineer, mathematician and statistician who has made significant contributions to the theory of statistical inference, mainly through asymptotic theory and applied probability.

Generalised hyperbolic distribution

The generalised hyperbolic distribution (GH) is a continuous probability distribution defined as the normal variance-mean mixture where the mixing distribution is the generalized inverse Gaussian distribution (GIG). Its probability density function (see the box) is given in terms of modified Bessel function of the second kind, denoted by . It was introduced by Ole Barndorff-Nielsen, who studied it in the context of physics of wind-blown sand.

Generalized Pareto distribution

In statistics, the generalized Pareto distribution (GPD) is a family of continuous probability distributions. It is often used to model the tails of another distribution. It is specified by three parameters: location , scale , and shape . Sometimes it is specified by only scale and shape and sometimes only by its shape parameter. Some references give the shape parameter as .

Kruskal–Wallis one-way analysis of variance

The Kruskal–Wallis test by ranks, Kruskal–Wallis H test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric method for testing whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes. It extends the Mann–Whitney U test, which is used for comparing only two groups. The parametric equivalent of the Kruskal–Wallis test is the one-way analysis of variance (ANOVA).

A significant Kruskal–Wallis test indicates that at least one sample stochastically dominates one other sample. The test does not identify where this stochastic dominance occurs or for how many pairs of groups stochastic dominance obtains. For analyzing the specific sample pairs for stochastic dominance, Dunn's test, pairwise Mann-Whitney tests without Bonferroni correction, or the more powerful but less well known Conover–Iman test are sometimes used.

Since it is a non-parametric method, the Kruskal–Wallis test does not assume a normal distribution of the residuals, unlike the analogous one-way analysis of variance. If the researcher can make the assumptions of an identically shaped and scaled distribution for all groups, except for any difference in medians, then the null hypothesis is that the medians of all groups are equal, and the alternative hypothesis is that at least one population median of one group is different from the population median of at least one other group.

List of statistics journals

This is a list of scientific journals published in the field of statistics.

Matrix analytic method

In probability theory, the matrix analytic method is a technique to compute the stationary probability distribution of a Markov chain which has a repeating structure (after some point) and a state space which grows unboundedly in no more than one dimension. Such models are often described as M/G/1 type Markov chains because they can describe transitions in an M/G/1 queue. The method is a more complicated version of the matrix geometric method and is the classical solution method for M/G/1 chains.

Minimum distance estimation

Minimum distance estimation (MDE) is a statistical method for fitting a mathematical model to data, usually the empirical distribution.

Mittag-Leffler distribution

The Mittag-Leffler distributions are two families of probability distributions on the half-line . They are parametrized by a real or . Both are defined with the Mittag-Leffler function, named after Gösta Mittag-Leffler.

Ravindra Khattree

Ravindra Khattree (born 1959) is an Indian-American statistician and professor of statistics at Oakland University. His contribution to the Fountain–Khattree–Peddada Theorem in Pitman measure of closeness is one of the important results of his work. Khattree is the coauthor of two books and has coedited two volumes. He has served as an associate editor of the Communications in Statistics journal and the editor of the Interstat online journal. He is Chief editor of Journal of Statistics and Applications. He is an elected fellow of the American Statistical Association.Khattree was born in Uttar Pradesh, India. He attended the Ewing Christian College-Allahabad University and the Indian Statistical Institute. In 1985, he earned a doctorate from the University of Pittsburgh with Calyampudi Radhakrishna Rao as his advisor. He became a faculty member at Oakland University in 1991. He was the biostatistics group leader in the Biomedical Research and Informatics Center and a professor of biostatistics in the College of Human Medicine, Michigan State University during 2005–2006. He worked as a senior research scientist at US National Academy of Sciences with assignment at the Radiation Effects Research Foundation (formerly known as Atomic Bomb Casualty Commission), Hiroshima during 2010–2011.Prior to joining Oakland University, he had been a faculty member at the North Dakota State University, Case Western Reserve University and also worked at BFGoodrich Chemical Group. He is the paternal grandson of Binda Prasad Khattri.

Robert V. Hogg

Robert Vincent ("Bob") Hogg (8 November 1924 – 23 December 2014) was an American statistician and professor of statistics of the University of Iowa. Hogg is known for his widely used textbooks on statistics (with his 1963 Ph.D. student Elliot Alan Tanis) and on mathematical statistics (with his 1950 Ph.D. advisor Allen Thornton Craig). Hogg has received recognition for his research on

robust and adaptive nonparametric statistics and for his scholarship on total quality management and statistics education.

Scan statistic

In statistics, a scan statistic or window statistic is a problem relating to the clustering of randomly positioned points. An example of a typical problem is the maximum size of a cluster of points on a line or the longest series of successes recorded by a moving window of fixed length.Joseph Naus first published on the problem in the 1960s, and has been called the "father of the scan statistic" in honour of his early contributions. The results can be applied in epidemiology, public health and astronomy to find unusual clusters of events.It was extended by Martin Kulldorff to multi-dimensional settings and varying window sizes in a 1997 paper, which is (as of 11 October 2015) the most cited article in its journal, Communications in Statistics – Theory and Methods.

Shayle R. Searle

Shayle Robert Searle PhD (26 April 1928 – 18 February 2013) was a New Zealand mathematician who was Professor Emeritus of Biological Statistics at Cornell University. He was a leader in the field of linear and mixed models in statistics, and published widely on the topics of linear models, mixed models, and variance component estimation.Searle was one of the first statisticians to use matrix algebra in statistical methodology, and was an early proponent of the use of applied statistical techniques in animal breeding.

He died at his home in Ithaca, New York.

Stochastic Models

Stochastic Models is a peer-reviewed scientific journal that publishes papers on stochastic models. It is published by Taylor & Francis. It was established in 1985 under the title Communications in Statistics. Stochastic Models and obtained its current name in 2001. According to the Journal Citation Reports, the journal has a 2016 impact factor of 0.380. The founding editor-in-chief was Marcel F. Neuts, the current editor is Peter Taylor (University of Melbourne).

Whittle likelihood

In statistics, Whittle likelihood is an approximation to the likelihood function of a stationary Gaussian time series. It is named after the mathematician and statistician Peter Whittle, who introduced it in his PhD thesis in 1951.

It is commonly utilized in time series analysis and signal processing for parameter estimation and signal detection.

Wrapped asymmetric Laplace distribution

In probability theory and directional statistics, a wrapped asymmetric Laplace distribution is a wrapped probability distribution that results from the "wrapping" of the asymmetric Laplace distribution around the unit circle. For the symmetric case (asymmetry parameter κ = 1), the distribution becomes a wrapped Laplace distribution. The distribution of the ratio of two circular variates (Z) from two different wrapped exponential distributions will have a wrapped asymmetric Laplace distribution. These distributions find application in stochastic modelling of financial data.

Wrapped exponential distribution

In probability theory and directional statistics, a wrapped exponential distribution is a wrapped probability distribution that results from the "wrapping" of the exponential distribution around the unit circle.

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