Example of AStA Advances in Statistical Analysis format
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Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format
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Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format Example of AStA Advances in Statistical Analysis format
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open access Open Access

AStA Advances in Statistical Analysis — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Social Sciences (miscellaneous) #118 of 334 down down by 11 ranks
Analysis #66 of 164 down down by 7 ranks
Statistics and Probability #102 of 239 up up by 1 rank
Economics and Econometrics #314 of 661 down down by 25 ranks
Applied Mathematics #278 of 548 down down by 31 ranks
Modeling and Simulation #178 of 290 down down by 25 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 97 Published Papers | 181 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 13/07/2020
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SJR: 1.151
SNIP: 1.392
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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.

0.98

6% from 2018

Impact factor for AStA Advances in Statistical Analysis from 2016 - 2019
Year Value
2019 0.98
2018 1.047
2017 0.643
2016 0.868
graph view Graph view
table view Table view

1.9

19% from 2019

CiteRatio for AStA Advances in Statistical Analysis from 2016 - 2020
Year Value
2020 1.9
2019 1.6
2018 1.6
2017 1.4
2016 2.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 6% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 19% 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.

0.507

7% from 2019

SJR for AStA Advances in Statistical Analysis from 2016 - 2020
Year Value
2020 0.507
2019 0.546
2018 1.185
2017 0.548
2016 0.919
graph view Graph view
table view Table view

1.109

31% from 2019

SNIP for AStA Advances in Statistical Analysis from 2016 - 2020
Year Value
2020 1.109
2019 0.847
2018 1.203
2017 0.875
2016 1.334
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 7% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 31% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

AStA Advances in Statistical Analysis

Guideline source: View

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Springer

AStA Advances in Statistical Analysis

AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.AStA - Advances in Statistical Analysis has three designated sections: S...... Read More

Mathematics

i
Last updated on
13 Jul 2020
i
ISSN
1863-8171
i
Impact Factor
Medium - 0.695
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
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/S10182-010-0143-0
Design and analysis of computer experiments
Sonja Kuhnt1, David M. Steinberg2

Abstract:

The design and analysis of computer experiments as a relatively young research field is not only of high importance for many industrial areas but also presents new challenges and open questions for statisticians. This editorial introduces a special issue devoted to the topic. The included papers present an interesting mixture... The design and analysis of computer experiments as a relatively young research field is not only of high importance for many industrial areas but also presents new challenges and open questions for statisticians. This editorial introduces a special issue devoted to the topic. The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications. read more read less

Topics:

Applied research (52%)52% related to the paper
View PDF
2,583 Citations
Journal Article DOI: 10.1007/S10182-008-0060-7
On composite marginal likelihoods

Abstract:

Composite marginal likelihoods are pseudolikelihoods constructed by compounding marginal densities. In several applications, they are convenient surrogates for the ordinary likelihood when it is too cumbersome or impractical to compute. This paper presents an overview of the topic with emphasis on applications. Composite marginal likelihoods are pseudolikelihoods constructed by compounding marginal densities. In several applications, they are convenient surrogates for the ordinary likelihood when it is too cumbersome or impractical to compute. This paper presents an overview of the topic with emphasis on applications. read more read less

Topics:

Marginal likelihood (54%)54% related to the paper
366 Citations
open accessOpen access Journal Article DOI: 10.1007/S10182-012-0196-3
Spatio-temporal modeling of particulate matter concentration through the SPDE approach
Michela Cameletti1, Finn Lindgren2, Daniel Simpson2, Håvard Rue2

Abstract:

In this work, we consider a hierarchical spatio-temporal model for particulate matter (PM) concentration in the North-Italian region Piemonte. The model involves a Gaussian Field (GF), affected by a measurement error, and a state process characterized by a first order autoregressive dynamic model and spatially correlated inno... In this work, we consider a hierarchical spatio-temporal model for particulate matter (PM) concentration in the North-Italian region Piemonte. The model involves a Gaussian Field (GF), affected by a measurement error, and a state process characterized by a first order autoregressive dynamic model and spatially correlated innovations. This kind of model is well discussed and widely used in the air quality literature thanks to its flexibility in modelling the effect of relevant covariates (i.e. meteorological and geographical variables) as well as time and space dependence. However, Bayesian inference—through Markov chain Monte Carlo (MCMC) techniques—can be a challenge due to convergence problems and heavy computational loads. In particular, the computational issue refers to the infeasibility of linear algebra operations involving the big dense covariance matrices which occur when large spatio-temporal datasets are present. The main goal of this work is to present an effective estimating and spatial prediction strategy for the considered spatio-temporal model. This proposal consists in representing a GF with Matern covariance function as a Gaussian Markov Random Field (GMRF) through the Stochastic Partial Differential Equations (SPDE) approach. The main advantage of moving from a GF to a GMRF stems from the good computational properties that the latter enjoys. In fact, GMRFs are defined by sparse matrices that allow for computationally effective numerical methods. Moreover, when dealing with Bayesian inference for GMRFs, it is possible to adopt the Integrated Nested Laplace Approximation (INLA) algorithm as an alternative to MCMC methods giving rise to additional computational advantages. The implementation of the SPDE approach through the R-library INLA ( www.r-inla.org ) is illustrated with reference to the Piemonte PM data. In particular, providing the step-by-step R-code, we show how it is easy to get prediction and probability of exceedance maps in a reasonable computing time. read more read less

Topics:

Markov chain Monte Carlo (56%)56% related to the paper, Matérn covariance function (54%)54% related to the paper, Covariance (54%)54% related to the paper, Bayesian inference (53%)53% related to the paper, Gaussian (51%)51% related to the paper
View PDF
337 Citations
Journal Article DOI: 10.1007/S10182-008-0072-3
Thinning operations for modeling time series of counts—a survey
Christian H. Weiß1

Abstract:

The analysis of time series of counts is an emerging field of science. To obtain an ARMA-like autocorrelation structure, many models make use of thinning operations to adapt the ARMA recursion to the integer-valued case. Most popular among these probabilistic operations is the concept of binomial thinning, leading to the clas... The analysis of time series of counts is an emerging field of science. To obtain an ARMA-like autocorrelation structure, many models make use of thinning operations to adapt the ARMA recursion to the integer-valued case. Most popular among these probabilistic operations is the concept of binomial thinning, leading to the class of INARMA models. These models are proved to be useful, especially for processes of Poisson counts, but may lead to difficulties in the case of different count distributions. Therefore, several alternative thinning concepts have been developed. This article reviews such thinning operations and shows how they are successfully applied to define integer-valued ARMA models. read more read less
330 Citations
open accessOpen access Journal Article DOI: 10.1007/S10182-013-0216-Y
Bandwidth selection for kernel density estimation: a review of fully automatic selectors
Nils-Bastian Heidenreich1, Anja Schindler1, Stefan Sperlich2

Abstract:

On the one hand, kernel density estimation has become a common tool for empirical studies in any research area. This goes hand in hand with the fact that this kind of estimator is now provided by many software packages. On the other hand, since about three decades the discussion on bandwidth selection has been going on. Altho... On the one hand, kernel density estimation has become a common tool for empirical studies in any research area. This goes hand in hand with the fact that this kind of estimator is now provided by many software packages. On the other hand, since about three decades the discussion on bandwidth selection has been going on. Although a good part of the discussion is about nonparametric regression, this parameter choice is by no means less problematic for density estimation. This becomes obvious when reading empirical studies in which practitioners have made use of kernel densities. New contributions typically provide simulations only to show that the own selector outperforms some of the existing methods. We review existing methods and compare them on a set of designs that exhibit few bumps and exponentially falling tails. We concentrate on small and moderate sample sizes because for large ones the differences between consistent methods are often negligible, at least for practitioners. As a byproduct we find that a mixture of simple plug-in and cross-validation methods produces bandwidths with a quite stable performance. read more read less

Topics:

Kernel density estimation (58%)58% related to the paper, Nonparametric regression (53%)53% related to the paper, Density estimation (52%)52% related to the paper, Estimator (51%)51% related to the paper
View PDF
212 Citations
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3. Can I cite my article in multiple styles in AStA Advances in Statistical Analysis?

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 AStA Advances in Statistical Analysis citation style.

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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 AStA Advances in Statistical Analysis that you can download at the end.

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12. Is AStA Advances in Statistical Analysis'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 AStA Advances in Statistical Analysis?

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 AStA Advances in Statistical Analysis. 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 AStA Advances in Statistical Analysis?

The 5 most common citation types in order of usage for AStA Advances in Statistical Analysis 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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16. Can I download AStA Advances in Statistical Analysis 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 AStA Advances in Statistical Analysis Endnote style according to Elsevier guidelines.

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