Example of Computational Statistics format
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Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format
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Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format Example of Computational Statistics format
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open access Open Access

Computational Statistics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Statistics, Probability and Uncertainty #73 of 152 down down by 26 ranks
Statistics and Probability #123 of 239 down down by 42 ranks
Computational Mathematics #92 of 152 down down by 22 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 318 Published Papers | 495 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 10/06/2020
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Related Journals

open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 5.9
SJR: 5.062
SNIP: 4.015
open access Open Access
recommended Recommended

Oxford University Press

Quality:  
High
CiteRatio: 9.9
SJR: 3.599
SNIP: 2.056
open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.5
SJR: 0.663
SNIP: 1.246
open access Open Access

Taylor and Francis

Quality:  
Good
CiteRatio: 2.7
SJR: 1.061
SNIP: 1.483

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.

1.6

14% from 2019

CiteRatio for Computational Statistics from 2016 - 2020
Year Value
2020 1.6
2019 1.4
2018 1.4
2017 1.8
2016 2.0
graph view Graph view
table view Table view

0.494

8% from 2019

SJR for Computational Statistics from 2016 - 2020
Year Value
2020 0.494
2019 0.538
2018 0.632
2017 0.803
2016 0.706
graph view Graph view
table view Table view

0.907

6% from 2019

SNIP for Computational Statistics from 2016 - 2020
Year Value
2020 0.907
2019 0.859
2018 0.781
2017 0.911
2016 0.954
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 14% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 8% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 6% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Computational Statistics

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Springer

Computational Statistics

Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statisti...... Read More

Statistics, Probability and Uncertainty

Statistics and Probability

Computational Mathematics

Decision Sciences

i
Last updated on
10 Jun 2020
i
ISSN
0943-4062
i
Impact Factor
Medium - 0.902
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
SPBASIC
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Beenakker CWJ (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

open accessOpen access Journal Article DOI: 10.1007/S00180-012-0317-1
Goodness-of-fit indices for partial least squares path modeling
Jörg Henseler1, Marko Sarstedt2
01 Apr 2013 - Computational Statistics

Abstract:

This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoFrel), we estimate PLS path models with simulated data, and contrast their values with fit ind... This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoFrel), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoFrel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data. read more read less

Topics:

Partial least squares path modeling (64%)64% related to the paper, Partial least squares regression (59%)59% related to the paper, Goodness of fit (55%)55% related to the paper, Structural equation modeling (51%)51% related to the paper, Covariance (50%)50% related to the paper
View PDF
1,102 Citations
Journal Article DOI: 10.1007/S00180-007-0072-X
Bayesian spatial modeling of genetic population structure
Jukka Corander1, Jukka Sirén1, Elja Arjas1
01 Jan 2008 - Computational Statistics

Abstract:

Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characte... Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently. While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available, described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data sets illustrate well the benefits of incorporating spatial information to such an analysis. read more read less

Topics:

Population (56%)56% related to the paper, Bayesian hierarchical modeling (56%)56% related to the paper, Spatial analysis (55%)55% related to the paper, Bayesian inference (54%)54% related to the paper, Markov chain Monte Carlo (53%)53% related to the paper
409 Citations
Journal Article DOI: 10.1007/S00180-010-0217-1
maxLik: A package for maximum likelihood estimation in R
Arne Henningsen1, Ott Toomet2
01 Sep 2011 - Computational Statistics

Abstract:

This paper describes the package maxLik for the statistical environment R. The package is essentially a unified wrapper interface to various optimization routines, offering easy access to likelihood-specific features like standard errors or information matrix equality (BHHH method). More advanced features of the optimization ... This paper describes the package maxLik for the statistical environment R. The package is essentially a unified wrapper interface to various optimization routines, offering easy access to likelihood-specific features like standard errors or information matrix equality (BHHH method). More advanced features of the optimization algorithms, such as forcing the value of a particular parameter to be fixed, are also supported. read more read less

Topics:

Likelihood function (54%)54% related to the paper, Maximum likelihood sequence estimation (53%)53% related to the paper, Restricted maximum likelihood (51%)51% related to the paper, Fisher information (50%)50% related to the paper
View PDF
398 Citations
Journal Article DOI: 10.1007/S001800050022
Adaptive proposal distribution for random walk Metropolis algorithm
Heikki Haario1, Eero Saksman1, Johanna Tamminen2
01 Sep 1999 - Computational Statistics

Abstract:

The choice of a suitable MCMC method and further the choice of a proposal distribution is known to be crucial for the convergence of the Markov chain. However, in many cases the choice of an effective proposal distribution is difficult. As a remedy we suggest a method called Adaptive Proposal (AP). Although the stationary dis... The choice of a suitable MCMC method and further the choice of a proposal distribution is known to be crucial for the convergence of the Markov chain. However, in many cases the choice of an effective proposal distribution is difficult. As a remedy we suggest a method called Adaptive Proposal (AP). Although the stationary distribution of the AP algorithm is slightly biased, it appears to provide an efficient tool for, e.g., reasonably low dimensional problems, as typically encountered in non-linear regression problems in natural sciences. As a realistic example we include a successful application of the AP algorithm in parameter estimation for the satellite instrument ‘GOMOS’. In this paper we also present systematic performance criteria for comparing Adaptive Proposal algorithm with more traditional Metropolis algorithms. read more read less

Topics:

Metropolis–Hastings algorithm (56%)56% related to the paper, Markov chain Monte Carlo (55%)55% related to the paper, Markov chain (52%)52% related to the paper, Stationary distribution (51%)51% related to the paper
376 Citations
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Computational Statistics format uses SPBASIC citation style.

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Frequently asked questions

1. Can I write Computational Statistics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Computational Statistics guidelines and auto format it.

2. Do you follow the Computational Statistics guidelines?

Yes, the template is compliant with the Computational Statistics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Computational Statistics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Computational Statistics citation style.

4. Can I use the Computational Statistics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Computational Statistics.

5. Can I use a manuscript in Computational Statistics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Computational Statistics that you can download at the end.

6. How long does it usually take you to format my papers in Computational Statistics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Computational Statistics.

7. Where can I find the template for the Computational Statistics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computational Statistics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Computational Statistics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Computational Statistics an online tool or is there a desktop version?

SciSpace's Computational Statistics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Computational Statistics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Computational Statistics?”

11. What is the output that I would get after using Computational Statistics?

After writing your paper autoformatting in Computational Statistics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computational Statistics's impact factor high enough that I should try publishing my article there?

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 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 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 Computational Statistics?

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

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

15. How do I submit my article to the Computational Statistics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computational Statistics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Computational Statistics in Endnote format?

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 Computational Statistics Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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