Example of Journal of Statistical Computation and Simulation format
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Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format
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Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format Example of Journal of Statistical Computation and Simulation format
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open access Open Access

Journal of Statistical Computation and Simulation — Template for authors

Publisher: Taylor and Francis
Categories Rank Trend in last 3 yrs
Statistics, Probability and Uncertainty #60 of 152 down down by 7 ranks
Statistics and Probability #98 of 239 down down by 10 ranks
Applied Mathematics #269 of 548 down down by 57 ranks
Modeling and Simulation #177 of 290 down down by 38 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 746 Published Papers | 1433 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 25/06/2020
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FAQ

Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 4.9
SJR: 0.657
SNIP: 0.944
open access Open Access

Springer

Quality:  
Good
CiteRatio: 1.9
SJR: 0.507
SNIP: 1.109
open access Open Access

Elsevier

Quality:  
Good
CiteRatio: 2.2
SJR: 1.478
SNIP: 1.49

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

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

0.918

20% from 2018

Impact factor for Journal of Statistical Computation and Simulation from 2016 - 2019
Year Value
2019 0.918
2018 0.767
2017 0.869
2016 0.757
graph view Graph view
table view Table view

1.9

12% from 2019

CiteRatio for Journal of Statistical Computation and Simulation from 2016 - 2020
Year Value
2020 1.9
2019 1.7
2018 1.7
2017 1.7
2016 1.5
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 20% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 12% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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.

0.626

6% from 2019

SJR for Journal of Statistical Computation and Simulation from 2016 - 2020
Year Value
2020 0.626
2019 0.664
2018 0.717
2017 0.704
2016 0.763
graph view Graph view
table view Table view

1.193

7% from 2019

SNIP for Journal of Statistical Computation and Simulation from 2016 - 2020
Year Value
2020 1.193
2019 1.118
2018 0.89
2017 1.366
2016 1.192
graph view Graph view
table view Table view

insights Insights

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

insights Insights

  • SNIP of this journal has increased by 7% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Journal of Statistical Computation and Simulation

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Taylor and Francis

Journal of Statistical Computation and Simulation

Approved by publishing and review experts on SciSpace, this template is built as per for Journal of Statistical Computation and Simulation formatting guidelines as mentioned in Taylor and Francis author instructions. The current version was created on 25 Jun 2020 and has been used by 614 authors to write and format their manuscripts to this journal.

Mathematics

i
Last updated on
25 Jun 2020
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ISSN
0094-9655
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Impact Factor
High - 1.157
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
Taylor and Francis Custom Citation
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B. 1982; 25(7):4515–4532. Available from: 10.1103/PhysRevB.25.4515.

Top papers written in this journal

Journal Article DOI: 10.1080/00949659308811554
Generalized linear mixed models a pseudo-likelihood approach
Russell D. Wolfinger1, Michael O'Connell2

Abstract:

A useful extension of the generalized linear model involves the addition of random effects andlor correlated errors. A pseudo-likelihood estimation procedure is developed to fit this class of mixed models based on an approximate marginal model for the mean response. The procedure is implemented via iterated fitting of a weigh... A useful extension of the generalized linear model involves the addition of random effects andlor correlated errors. A pseudo-likelihood estimation procedure is developed to fit this class of mixed models based on an approximate marginal model for the mean response. The procedure is implemented via iterated fitting of a weighted Gaussian linear mixed model to a modified dependent variable. The approach allows for flexible specification of covariance structures for both the random effects and the correlated errors. An estimate of an additional dispersion parameter for underlying exponential family distributions is optionally automatic. The method allows for subject-specific and population-averaged inference, and the Salamander data example from McCullagh and Nelder (1989) is used to illustrate both. read more read less

Topics:

Generalized linear mixed model (69%)69% related to the paper, Mixed model (58%)58% related to the paper, Generalized linear model (57%)57% related to the paper, Hierarchical generalized linear model (57%)57% related to the paper, Covariance (56%)56% related to the paper
1,256 Citations
open accessOpen access Journal Article DOI: 10.1080/10629360600810434
Fully conditional specification in multivariate imputation
S. van Buuren1, J.P.L. Brand2, Catharina G.M. Groothuis-Oudshoorn, Donald B. Rubin3

Abstract:

The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the specified conditional densiti... The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the specified conditional densities can be incompatible, and therefore the stationary distribution to which the Gibbs sampler attempts to converge may not exist. This study investigates practical consequences of this problem by means of simulation. Missing data are created under four different missing data mechanisms. Attention is given to the statistical behavior under compatible and incompatible models. The results indicate that multiple imputation produces essentially unbiased estimates with appropriate coverage in the simple cases investigated, even for the incompatible models. Of particular interest is that these results were produced using only five Gibbs iterations starting from a simple draw from observed marginal distributions. It thus appears that, despite the theoretical weaknesses, the actual performance of conditional model specification for multivariate imputation can be quite good, and therefore deserves further study. © 2006 Taylor & Francis. read more read less

Topics:

Imputation (statistics) (62%)62% related to the paper, Gibbs sampling (58%)58% related to the paper, Conditional probability distribution (58%)58% related to the paper, Missing data (55%)55% related to the paper, Marginal distribution (55%)55% related to the paper
View PDF
977 Citations
Journal Article DOI: 10.1080/00949650215733
Permutation tests for multi-factorial analysis of variance
Marti J. Anderson1, Cajo J. F. ter Braak2

Abstract:

Several permutation strategies are often possible for tests of individual terms in analysis-of-variance (ANOVA) designs These include restricted permutations, permutation of whole groups of units, permutation of some form of residuals or some combination of these It is unclear, especially for complex designs involving random ... Several permutation strategies are often possible for tests of individual terms in analysis-of-variance (ANOVA) designs These include restricted permutations, permutation of whole groups of units, permutation of some form of residuals or some combination of these It is unclear, especially for complex designs involving random factors, mixed models or nested hierarchies, just which permutation strategy should be used for any particular test The purpose of this paper is two-fold: (i) we provide a guideline for constructing an exact permutation strategy, where possible, for any individual term in any ANOVA design; and (ii) we provide results of Monte Carlo simulations to compare the level accuracy and power of different permutation strategies in two-way ANOVA, including random and mixed models, nested hierarchies and tests of interaction terms Simulation results showed that permutation of residuals under a reduced model generally had greater power than the exact test or alternative approximate permutation read more read less

Topics:

Random permutation (72%)72% related to the paper, Permutation (62%)62% related to the paper, Analysis of covariance (51%)51% related to the paper, F-test (51%)51% related to the paper
920 Citations
Journal Article DOI: 10.1080/00949650903530745
A new family of generalized distributions
Gauss M. Cordeiro1, Mário de Castro2

Abstract:

Kumaraswamy [Generalized probability density-function for double-bounded random-processes, J. Hydrol. 462 (1980), pp. 79–88] introduced a distribution for double-bounded random processes with hydrological applications. For the first time, based on this distribution, we describe a new family of generalized distributions (denot... Kumaraswamy [Generalized probability density-function for double-bounded random-processes, J. Hydrol. 462 (1980), pp. 79–88] introduced a distribution for double-bounded random processes with hydrological applications. For the first time, based on this distribution, we describe a new family of generalized distributions (denoted with the prefix ‘Kw’) to extend the normal, Weibull, gamma, Gumbel, inverse Gaussian distributions, among several well-known distributions. Some special distributions in the new family such as the Kw-normal, Kw-Weibull, Kw-gamma, Kw-Gumbel and Kw-inverse Gaussian distribution are discussed. We express the ordinary moments of any Kw generalized distribution as linear functions of probability weighted moments (PWMs) of the parent distribution. We also obtain the ordinary moments of order statistics as functions of PWMs of the baseline distribution. We use the method of maximum likelihood to fit the distributions in the new class and illustrate the potentiality of the new model with a... read more read less

Topics:

Generalized inverse Gaussian distribution (69%)69% related to the paper, Inverse distribution (68%)68% related to the paper, Generalized integer gamma distribution (68%)68% related to the paper, Natural exponential family (67%)67% related to the paper, Kumaraswamy distribution (66%)66% related to the paper
View PDF
742 Citations
open accessOpen access Journal Article DOI: 10.1080/00949655.2010.520163
Comparisons of various types of normality tests
Bee Wah Yap1, C. H. Sim2

Abstract:

Normality tests can be classified into tests based on chi-squared, moments, empirical distribution, spacings, regression and correlation and other special tests. This paper studies and compares the power of eight selected normality tests: the Shapiro–Wilk test, the Kolmogorov–Smirnov test, the Lilliefors test, the Cramer–von ... Normality tests can be classified into tests based on chi-squared, moments, empirical distribution, spacings, regression and correlation and other special tests. This paper studies and compares the power of eight selected normality tests: the Shapiro–Wilk test, the Kolmogorov–Smirnov test, the Lilliefors test, the Cramer–von Mises test, the Anderson–Darling test, the D'Agostino–Pearson test, the Jarque–Bera test and chi-squared test. Power comparisons of these eight tests were obtained via the Monte Carlo simulation of sample data generated from alternative distributions that follow symmetric short-tailed, symmetric long-tailed and asymmetric distributions. Our simulation results show that for symmetric short-tailed distributions, D'Agostino and Shapiro–Wilk tests have better power. For symmetric long-tailed distributions, the power of Jarque–Bera and D'Agostino tests is quite comparable with the Shapiro–Wilk test. As for asymmetric distributions, the Shapiro–Wilk test is the most powerful test followed b... read more read less

Topics:

Normality test (74%)74% related to the paper, Shapiro–Wilk test (73%)73% related to the paper, Jarque–Bera test (60%)60% related to the paper, Anderson–Darling test (60%)60% related to the paper, Lilliefors test (60%)60% related to the paper
View PDF
545 Citations
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Frequently asked questions

1. Can I write Journal of Statistical Computation and Simulation in LaTeX?

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

2. Do you follow the Journal of Statistical Computation and Simulation guidelines?

Yes, the template is compliant with the Journal of Statistical Computation and Simulation 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 Journal of Statistical Computation and Simulation?

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 Journal of Statistical Computation and Simulation citation style.

4. Can I use the Journal of Statistical Computation and Simulation 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 Journal of Statistical Computation and Simulation.

5. Can I use a manuscript in Journal of Statistical Computation and Simulation 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 Journal of Statistical Computation and Simulation that you can download at the end.

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

7. Where can I find the template for the Journal of Statistical Computation and Simulation?

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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.

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SciSpace's Journal of Statistical Computation and Simulation 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.

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11. What is the output that I would get after using Journal of Statistical Computation and Simulation?

After writing your paper autoformatting in Journal of Statistical Computation and Simulation, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Statistical Computation and Simulation'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 Journal of Statistical Computation and Simulation?

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 Journal of Statistical Computation and Simulation. 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 Journal of Statistical Computation and Simulation?

The 5 most common citation types in order of usage for Journal of Statistical Computation and Simulation 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 Journal of Statistical Computation and Simulation?

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16. Can I download Journal of Statistical Computation and Simulation 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 Journal of Statistical Computation and Simulation Endnote style according to Elsevier guidelines.

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