Example of Frontiers in Applied Mathematics and Statistics format
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Example of Frontiers in Applied Mathematics and Statistics format Example of Frontiers in Applied Mathematics and Statistics format Example of Frontiers in Applied Mathematics and Statistics format Example of Frontiers in Applied Mathematics and Statistics format Example of Frontiers in Applied Mathematics and Statistics format Example of Frontiers in Applied Mathematics and Statistics format Example of Frontiers in Applied Mathematics and Statistics format
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open access Open Access

Frontiers in Applied Mathematics and Statistics — Template for authors

Publisher: Frontiers Media
Categories Rank Trend in last 3 yrs
Statistics and Probability #88 of 239 down down by None rank
Applied Mathematics #255 of 548 down down by None rank
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 209 Published Papers | 421 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 24/08/2023
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Related Journals

open access Open Access

Springer

Quality:  
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CiteRatio: 2.9
SJR: 1.151
SNIP: 1.392
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Quality:  
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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.

2.0

900% from 2019

CiteRatio for Frontiers in Applied Mathematics and Statistics from 2016 - 2020
Year Value
2020 2.0
2019 0.2
graph view Graph view
table view Table view

0.31

SJR for Frontiers in Applied Mathematics and Statistics from 2020 - 2020
Year Value
2020 0.31
graph view Graph view
table view Table view

0.742

Year Value
2020 0.742
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 900% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • This journal’s SNIP is in the top 10 percentile category.

Frontiers in Applied Mathematics and Statistics

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Frontiers Media

Frontiers in Applied Mathematics and Statistics

Frontiers in Applied Mathematics and Statistics is a new, open-access journal launched in May 2015 welcoming submissions in various specialties of statistics and applied mathematics.... Read More

Mathematical finance

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Last updated on
24 Aug 2023
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ISSN
2297-4687
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Open Access
Yes
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
frontiersinSCNS_ENG_HUMS
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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 25 (1982) 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.3389/FAMS.2017.00009
Semi-Stochastic Gradient Descent Methods
Jakub Konečný1, Peter Richtárik1

Abstract:

In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient and a random number of stochastic gradients is computed, following a geo... In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient and a random number of stochastic gradients is computed, following a geometric law. For strongly convex objectives, the method converges linearly. The total work needed for the method to output an epsilon-accurate solution in expectation, measured in the number of passes over data, is proportional to the condition number of the problem and inversely proportional to the number of functions forming the average. This is achieved by running the method with number of stochastic gradient evaluations per epoch proportional to conditioning of the problem. The SVRG method of Johnson and Zhang arises as a special case. To illustrate our theoretical results, S2GD only needs the workload equivalent to about 2.1 full gradient evaluations to find a 10e-6 accurate solution for a problem with 10e9 functions and a condition number of 10e3. read more read less

Topics:

Stochastic gradient descent (67%)67% related to the paper, Gradient descent (66%)66% related to the paper, Condition number (55%)55% related to the paper, Convex optimization (55%)55% related to the paper, Convex function (52%)52% related to the paper
View PDF
196 Citations
open accessOpen access Journal Article DOI: 10.3389/FAMS.2017.00014
A Deep Learning Approach to Diabetic Blood Glucose Prediction
Hrushikesh N. Mhaskar1, Sergei V. Pereverzyev, Maria D. van der Walt2

Abstract:

We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patient... We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patients; i.e., the machine need not re-calibrate on the new patients in the data set. We demonstrate how deep learning can outperform shallow networks in this example. One novelty is to demonstrate how a parsimonious deep representation can be constructed using domain knowledge. read more read less

Topics:

Data set (52%)52% related to the paper, Deep learning (52%)52% related to the paper, Domain knowledge (51%)51% related to the paper, Novelty (50%)50% related to the paper, Representation (mathematics) (50%)50% related to the paper
View PDF
104 Citations
open accessOpen access Journal Article DOI: 10.3389/FAMS.2018.00062
Randomized Distributed Mean Estimation: Accuracy vs. Communication
Jakub Konečný1, Peter Richtárik2, Peter Richtárik1, Peter Richtárik3

Abstract:

We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint. Our analysis does not rely on any statistical assumptions about the source of the vectors. This problem arises as a subp... We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint. Our analysis does not rely on any statistical assumptions about the source of the vectors. This problem arises as a subproblem in many applications, including reduce-all operations within algorithms for distributed and federated optimization and learning. We propose a flexible family of randomized algorithms exploring the trade-off between expected communication cost and estimation error. Our family contains the full-communication and zero-error method on one extreme, and an epsilon-bit communication and O(1/(epsilon n)) error method on the opposite extreme. In the special case where we communicate, in expectation, a single bit per coordinate of each vector, we improve upon existing results by obtaining O(r/n) error, where r is the number of bits used to represent a floating point value. read more read less

Topics:

Randomized algorithm (53%)53% related to the paper, Floating point (53%)53% related to the paper
View PDF
72 Citations
open accessOpen access Journal Article DOI: 10.3389/FAMS.2019.00044
Deep Learning in Music Recommendation Systems
Markus Schedl1

Abstract:

Like in many other research areas, deep learning (DL) is increasingly adopted in music recommender systems (MRS). Deep neural networks are used in this area particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music items (tracks or artists) from musi... Like in many other research areas, deep learning (DL) is increasingly adopted in music recommender systems (MRS). Deep neural networks are used in this area particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music items (tracks or artists) from music playlists or listening sessions. Latent item factors are commonly integrated into content-based filtering and hybrid MRS, whereas sequence models of music items are used for sequential music recommendation, e.g., automatic playlist continuation. This review article explains particularities of the music domain in RS research. It gives an overview of the state of the art that employs deep learning for music recommendation. The discussion is structured according to the dimensions of neural network type, input data, recommendation approach (content-based filtering, collaborative filtering, or both), and task (standard or sequential music recommendation). In addition, we discuss major challenges faced in MRS, in particular in the context of the current research on deep learning. read more read less

Topics:

Music information retrieval (67%)67% related to the paper, Recommender system (57%)57% related to the paper, Collaborative filtering (57%)57% related to the paper, Deep learning (52%)52% related to the paper
View PDF
64 Citations
open accessOpen access Journal Article DOI: 10.3389/FAMS.2019.00043
Performance of Some Estimators of Relative Variability
Raydonal Ospina1, Fernando Marmolejo-Ramos2

Abstract:

The classic coefficient of variation (CV) is the ratio of the standard deviation to the mean and can be used to compare normally distributed data with respect to their variability, this measure has been widely used in many fields. In the Social Sciences, the CV is used to evaluate demographic heterogeneity and social aggregat... The classic coefficient of variation (CV) is the ratio of the standard deviation to the mean and can be used to compare normally distributed data with respect to their variability, this measure has been widely used in many fields. In the Social Sciences, the CV is used to evaluate demographic heterogeneity and social aggregates such as race, sex, education and others. Data of this nature are usually not normally distributed, and the distributional characteristics can vary widely. In this sense, more accurate and robust estimator variations of the classic CV are needed to give a more realistic picture of the behaviour of collected data. In this work, we empirically evaluate five measures of relative variability, including the classic CV, of finite sample sizes via Monte Carlo simulations. Our purpose is to give an insight into the behaviour of these estimators, as their performance has not previously been systematically investigated. To represent different behaviours of the data, we considered some statistical distributions -- which are frequently used to model data across various research fields. To enable comparisons, we consider parameters of these distributions that lead to a similar range of values for the CV. Our results indicate that CV estimators based on robust statistics of scale and location are more accurate and give the highest measure of efficiency. Finally, we study the stability of a robust CV estimator in psychological and genetic data and compare the results with the traditional CV. read more read less

Topics:

Robust statistics (56%)56% related to the paper, Estimator (55%)55% related to the paper, Standard deviation (52%)52% related to the paper, Stability (probability) (51%)51% related to the paper
View PDF
51 Citations
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Frontiers in Applied Mathematics and Statistics format uses frontiersinSCNS_ENG_HUMS citation style.

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13. What is Sherpa RoMEO Archiving Policy for Frontiers in Applied Mathematics and 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 Frontiers in Applied Mathematics and 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 Frontiers in Applied Mathematics and Statistics?

The 5 most common citation types in order of usage for Frontiers in Applied Mathematics and 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 Frontiers in Applied Mathematics and Statistics Endnote style according to Elsevier guidelines.

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