Example of Wiley Interdisciplinary Reviews: Computational Statistics format
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Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format
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Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format Example of Wiley Interdisciplinary Reviews: Computational Statistics format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Wiley Interdisciplinary Reviews: Computational Statistics — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Statistics and Probability #67 of 239 down down by 33 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 94 Published Papers | 242 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 05/06/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

2.6

37% from 2019

CiteRatio for Wiley Interdisciplinary Reviews: Computational Statistics from 2016 - 2020
Year Value
2020 2.6
2019 1.9
2018 3.2
2017 3.3
2016 2.3
graph view Graph view
table view Table view

0.693

5% from 2019

SJR for Wiley Interdisciplinary Reviews: Computational Statistics from 2016 - 2020
Year Value
2020 0.693
2019 0.663
2018 1.022
2017 1.128
2016 0.506
graph view Graph view
table view Table view

1.495

26% from 2019

SNIP for Wiley Interdisciplinary Reviews: Computational Statistics from 2016 - 2020
Year Value
2020 1.495
2019 1.183
2018 1.494
2017 1.37
2016 1.069
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 37% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 5% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 26% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Wiley Interdisciplinary Reviews: Computational Statistics

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Wiley

Wiley Interdisciplinary Reviews: Computational Statistics

The goals of WIREs Computational Statistics are to (1) present the current state of the art of Computational Statistics through an ongoing series of commissioned reviews written by leading researchers; (2) capture the crucial interdisciplinary flavor of this field by including...... Read More

Statistics and Probability

Mathematics

i
Last updated on
05 Jun 2020
i
ISSN
1939-5108
i
Impact Factor
High - 1.75
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
apa
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1002/WICS.101
Principal component analysis
Hervé Abdi1, Lynne J. Williams2

Abstract:

Principal component analysis PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal compon... Principal component analysis PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis CA in order to handle qualitative variables and as multiple factor analysis MFA in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and upon the singular value decomposition SVD of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc. read more read less

Topics:

Principal component analysis (66%)66% related to the paper, Sparse PCA (66%)66% related to the paper, Relationship square (64%)64% related to the paper, Correspondence analysis (59%)59% related to the paper, Variables (53%)53% related to the paper
View PDF
6,398 Citations
Journal Article DOI: 10.1002/WICS.73
Response surface methodology
André I. Khuri1, Siuli Mukhopadhyay2

Abstract:

The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called... The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc. read more read less

Topics:

Response surface methodology (54%)54% related to the paper, Robust parameter design (51%)51% related to the paper, Taguchi methods (51%)51% related to the paper
1,064 Citations
Journal Article DOI: 10.1002/WICS.51
Partial least squares regression and projection on latent structure regression (PLS Regression)
Hervé Abdi1

Abstract:

Partial least squares (PLS) regression (a.k.a. projection on latent structures) is a recent technique that combines features from and generalizes principal component analysis (PCA) and multiple linear regression. Its goal is to predict a set of dependent variables from a set of independent variables or predictors. This predic... Partial least squares (PLS) regression (a.k.a. projection on latent structures) is a recent technique that combines features from and generalizes principal component analysis (PCA) and multiple linear regression. Its goal is to predict a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by extracting from the predictors a set of orthogonal factors called latent variables which have the best predictive power. These latent variables can be used to create displays akin to PCA displays. The quality of the prediction obtained from a PLS regression model is evaluated with cross-validation techniques such as the bootstrap and jackknife. There are two main variants of PLS regression: The most common one separates the roles of dependent and independent variables; the second one—used mostly to analyze brain imaging data—gives the same roles to dependent and independent variables. Copyright © 2010 John Wiley & Sons, Inc. For further resources related to this article, please visit the WIREs website. read more read less

Topics:

Partial least squares regression (71%)71% related to the paper, Regression analysis (68%)68% related to the paper, Segmented regression (65%)65% related to the paper, Regression diagnostic (64%)64% related to the paper, Latent variable model (64%)64% related to the paper
View PDF
1,062 Citations
Journal Article DOI: 10.1002/WICS.1212
The Comprehensive R Archive Network
Kurt Hornik1

Abstract:

The Comprehensive R Archive Network CRAN is a network of sites acting as the primary web service distributing R sources and binaries, extension packages, and documentation. We discuss this function... The Comprehensive R Archive Network CRAN is a network of sites acting as the primary web service distributing R sources and binaries, extension packages, and documentation. We discuss this function... read more read less
524 Citations
open accessOpen access Journal Article DOI: 10.1002/WICS.82
Fuzzy set theory
H.-J. Zimmermann1

Abstract:

Since its inception in 1965, the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of this theory can be found, for example, in artificial intelligence, computer science, medicine, control engineering, decision theory, expert systems, logic, management science, operations research, p... Since its inception in 1965, the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of this theory can be found, for example, in artificial intelligence, computer science, medicine, control engineering, decision theory, expert systems, logic, management science, operations research, pattern recognition, and robotics. Mathematical developments have advanced to a very high standard and are still forthcoming to day. In this review, the basic mathematical framework of fuzzy set theory will be described, as well as the most important applications of this theory to other theories and techniques. Since 1992 fuzzy set theory, the theory of neural nets and the area of evolutionary programming have become known under the name of ‘computational intelligence’ or ‘soft computing’. The relationship between these areas has naturally become particularly close. In this review, however, we will focus primarily on fuzzy set theory. Applications of fuzzy set theory to real problems are abound. Some references will be given. To describe even a part of them would certainly exceed the scope of this review. Copyright © 2010 John Wiley & Sons, Inc. For further resources related to this article, please visit the WIREs website. read more read less

Topics:

Fuzzy set operations (67%)67% related to the paper, Type-2 fuzzy sets and systems (65%)65% related to the paper, Fuzzy logic (63%)63% related to the paper, Fuzzy set (61%)61% related to the paper, Neuro-fuzzy (58%)58% related to the paper
View PDF
493 Citations
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To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Wiley Interdisciplinary Reviews: Computational Statistics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Wiley Interdisciplinary Reviews: Computational Statistics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Wiley Interdisciplinary Reviews: Computational Statistics?

The 5 most common citation types in order of usage for Wiley Interdisciplinary Reviews: Computational Statistics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Wiley Interdisciplinary Reviews: Computational Statistics Endnote style according to Elsevier guidelines.

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