Example of Biometrical Journal format
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Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format Example of Biometrical Journal format
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Biometrical Journal — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Statistics, Probability and Uncertainty #48 of 152 up up by 2 ranks
Statistics and Probability #76 of 239 up up by 9 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 382 Published Papers | 906 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 18/05/2023
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Insights
General info
Top papers
Popular templates
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Why choose from SciSpace
FAQ

Related Journals

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SJR: 5.062
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CiteRatio: 6.5
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open access Open Access

Springer

Quality:  
Good
CiteRatio: 1.5
SJR: 0.352
SNIP: 1.624

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.

1.416

13% from 2018

Impact factor for Biometrical Journal from 2016 - 2019
Year Value
2019 1.416
2018 1.255
2017 1.114
2016 1.075
graph view Graph view
table view Table view

2.4

9% from 2019

CiteRatio for Biometrical Journal from 2016 - 2020
Year Value
2020 2.4
2019 2.2
2018 2.1
2017 1.8
2016 1.7
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 13% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 9% 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.

1.108

15% from 2019

SJR for Biometrical Journal from 2016 - 2020
Year Value
2020 1.108
2019 0.966
2018 1.115
2017 1.169
2016 0.935
graph view Graph view
table view Table view

1.135

9% from 2019

SNIP for Biometrical Journal from 2016 - 2020
Year Value
2020 1.135
2019 1.038
2018 0.99
2017 0.815
2016 1.066
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 15% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Biometrical Journal

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Wiley

Biometrical Journal

Approved by publishing and review experts on SciSpace, this template is built as per for Biometrical Journal formatting guidelines as mentioned in Wiley author instructions. The current version was created on 18 May 2023 and has been used by 425 authors to write and format their manuscripts to this journal.

Statistics, Probability and Uncertainty

Statistics and Probability

General Medicine

Decision Sciences

i
Last updated on
18 May 2023
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ISSN
0323-3847
i
Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
i
Bibliography Name
apa
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Citation Type
Author Year
(Blonder et al. 1982)
i
Bibliography Example
Blonder, G. E., Tinkham, M., & Klapwijk, T. M. (1982). Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7), 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1002/BIMJ.200810425
Simultaneous inference in general parametric models.
Torsten Hothorn1, Frank Bretz2, Peter H. Westfall3
01 Jun 2008 - Biometrical Journal

Abstract:

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity an... Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth read more read less

Topics:

General linear model (63%)63% related to the paper, Fiducial inference (63%)63% related to the paper, Frequentist inference (62%)62% related to the paper, Linear model (62%)62% related to the paper, Statistical model (58%)58% related to the paper
View PDF
10,545 Citations
open accessOpen access Journal Article DOI: 10.1002/BIMJ.200410135
Estimation of the Youden Index and its associated cutoff point.
Ronen Fluss1, David Faraggi1, Benjamin Reiser1
01 Aug 2005 - Biometrical Journal

Abstract:

The Youden Index is a frequently used summary measure of the ROC (Receiver Operating Characteristic) curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cutoff point) for the marker. In this paper we compare several estimation procedures for the Youden Ind... The Youden Index is a frequently used summary measure of the ROC (Receiver Operating Characteristic) curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cutoff point) for the marker. In this paper we compare several estimation procedures for the Youden Index and its associated cutoff point. These are based on (1) normal assumptions; (2) transformations to normality; (3) the empirical distribution function; (4) kernel smoothing. These are compared in terms of bias and root mean square error in a large variety of scenarios by means of an extensive simulation study. We find that the empirical method which is the most commonly used has the overall worst performance. In the estimation of the Youden Index the kernel is generally the best unless the data can be well transformed to achieve normality whereas in estimation of the optimal threshold value results are more variable. read more read less

Topics:

Youden's J statistic (61%)61% related to the paper, Kernel smoother (51%)51% related to the paper, Kernel (statistics) (50%)50% related to the paper, Empirical distribution function (50%)50% related to the paper, Receiver operating characteristic (50%)50% related to the paper
View PDF
1,760 Citations
open accessOpen access Journal Article DOI: 10.1002/BIMJ.200710415
Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection.
Marcus D. Ruopp1, Neil J. Perkins1, Brian W. Whitcomb1, Enrique F. Schisterman1
01 Jun 2008 - Biometrical Journal

Abstract:

The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (... The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (LOD) and missing from the overall dataset. Disregarding these observations may negatively bias the ROC curve and thus J. Several correction methods have been suggested for mean estimation and testing; however, little has been written about the ROC curve or its summary measures. We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c *) for a biomarker affected by a LOD. We develop unbiased estimators of J and c * via ML for normally and gamma distributed biomarkers. Alpha level confidence intervals are proposed using delta and bootstrap methods for the ML, semi-parametric, and non-parametric approaches respectively. Simulation studies are conducted over a range of distributional scenarios and sample sizes evaluating estimators' bias, root-mean square error, and coverage probability; the average bias was less than one percent for ML and GLM methods across scenarios and decreases with increased sample size. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods. We address the limitations and usefulness of each method in order to give researchers guidance in constructing appropriate estimates of biomarkers' true discriminating capabilities. read more read less

Topics:

Youden's J statistic (55%)55% related to the paper, Receiver operating characteristic (55%)55% related to the paper, Coverage probability (53%)53% related to the paper, Sample size determination (52%)52% related to the paper, Likelihood function (50%)50% related to the paper
View PDF
801 Citations
open accessOpen access Journal Article DOI: 10.1002/BIMJ.201700067
Variable selection - A review and recommendations for the practicing statistician.
Georg Heinze1, Christine Wallisch1, Daniela Dunkler1
02 Jan 2018 - Biometrical Journal

Abstract:

Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well-established if the set of independent variables to consider is fixed and small. Hence, w... Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well-established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10-30. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change-in-estimate criterion, background knowledge, or combinations thereof. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p-values or confidence intervals. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low-dimensional) modeling problems and on performing stability investigations and inference. We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms. read more read less

Topics:

Variables (59%)59% related to the paper, Covariate (58%)58% related to the paper, Linear regression (56%)56% related to the paper, Statistical model (56%)56% related to the paper, Feature selection (55%)55% related to the paper
View PDF
783 Citations
Journal Article DOI: 10.1002/BIMJ.200900028
L1 Penalized Estimation in the Cox Proportional Hazards Model
Jelle J. Goeman1
24 Nov 2009 - Biometrical Journal

Abstract:

This article presents a novel algorithm that efficiently computes L(1) penalized (lasso) estimates of parameters in high-dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high-dimensional dat... This article presents a novel algorithm that efficiently computes L(1) penalized (lasso) estimates of parameters in high-dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high-dimensional data. The new algorithm is based on a combination of gradient ascent optimization with the Newton-Raphson algorithm. It is described for a general likelihood function and can be applied in generalized linear models and other models with an L(1) penalty. The algorithm is demonstrated in the Cox proportional hazards model, predicting survival of breast cancer patients using gene expression data, and its performance is compared with competing approaches. An R package, penalized, that implements the method, is available on CRAN. read more read less

Topics:

Lasso (statistics) (62%)62% related to the paper, Likelihood function (52%)52% related to the paper, Shrinkage estimator (52%)52% related to the paper, Gradient descent (51%)51% related to the paper, Survival function (50%)50% related to the paper
719 Citations
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Biometrical Journal format uses apa citation style.

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Frequently asked questions

1. Can I write Biometrical Journal in LaTeX?

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

2. Do you follow the Biometrical Journal guidelines?

Yes, the template is compliant with the Biometrical Journal 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 Biometrical Journal?

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 Biometrical Journal citation style.

4. Can I use the Biometrical Journal 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 Biometrical Journal.

5. Can I use a manuscript in Biometrical Journal 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 Biometrical Journal that you can download at the end.

6. How long does it usually take you to format my papers in Biometrical Journal?

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

7. Where can I find the template for the Biometrical Journal?

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 Biometrical Journal'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 Biometrical Journal'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. Biometrical Journal an online tool or is there a desktop version?

SciSpace's Biometrical Journal 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 Biometrical Journal?

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 Biometrical Journal?”

11. What is the output that I would get after using Biometrical Journal?

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

12. Is Biometrical Journal'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 Biometrical Journal?

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 Biometrical Journal. 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 Biometrical Journal?

The 5 most common citation types in order of usage for Biometrical Journal 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 Biometrical Journal?

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 Biometrical Journal's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Biometrical Journal 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 Biometrical Journal Endnote style according to Elsevier guidelines.

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