(This article was first published on

We have seen yesterday that finding an optimal strategy to publish is
not that simple. And actually, it can be even more difficult in the
case the journal rejects the paper (not because it is not correct, but
because "it
does not fit" with the standards, the **Freakonometrics - Tag - R-english**, and kindly contributed to R-bloggers)*quality*of the journal, the audience, the editor's mood, or whatever). The author has basically two choices,

- forget about the article and move to something else (e.g. start a
blog where he/she will be the author
*and*the editor) - pretend that the article is worth publishing and then try to find another journal with similar interests

But this last choice is not that easy, since sometimes the author think that this journal was indeed the one that should publish it (e.g. all the articles on the subject have been published in that journal).

So I was wondering if there were clusters of journals, i.e. journals that publish

*almost*the same kind of articles (so that next time one of my paper is rejected by the editor, I just go to for some journal in the same cluster).

So what I did is extremely simple: I looked at articles

*titles*and looked for correlations between words frequency (I could have done that in key words, but I am not a big fan of those key words). I looked at 35 journals (that are somehow related to my areas of interest) and looked at titles of all articles published over the last 20 years. Then I kept the top 1000 of words, and I removed standard short words ("

*a*", "

*the*", "

*is*", etc). Actually, my top words looks like

"models" "model" "data" "estimation" "analysis" "time"

"processes" "risk" "random" "stochastic" "regression"

"market" "approach" "optimal" "based" "information"

"evidence" "linear" "games" "bayesian" "theory" "effects"

"distribution" "multivariate" "tests" "markets" "markov"

"equilibrium" "dynamic" "process" "distributions"

"application" "stock" "likelihood"

*principal component analysis*on my dataset (containing 960 variables - here

*words*- and 35 observations - here

*journal names*).

library("FactoMineR")

res.pca = PCA(MATRICE, scale.unit=TRUE, ncp=5,

graph=FALSE)

plot.PCA(res.pca, axes=c(1, 2), choix="ind")

Here, we can clearly observe some clusters : on the up-left

*Journal of Finance*and

*Journal of Banking and Finance*(say financial journals) on the top-right

*Biometrika*,

*Biometrics*,

*Computational Statistics and Data Analysis*and

*Journal of Econometrics*(

*JASA*is not far away, i.e. applied statistics journal). And below, on the right,

*Stochastic Processes and their Applications, Annals of Applied Probability*,

*Journal of Applied Probability*,

*Annals of Probability*,

*Proceedings of AMS*and

*Topology and Applications*(ie more theoretical journal).

Note that the projection is rather robust: if I consider my first 200 words, the graph is the same

In order to go further in the interpretation, we can also plot variables, i.e. words from titles,

where we cannot distinguish anything. So if I just look at my top 30, here they are,

On top left we see

*market(s)*,

*risk*or

*information*; on top right

*analysis*,

*effects*,

*models*or

*tests*; while below we see

*Markov*or

*process(es)*. And we can observe interesting facts: in finance in statistics, we talk about

*dynamics*while in theoretical (mathematical) journal it is about

*processes*.

But the goal was to find cluster, i.e. classes of journals that publish papers with similar titles.

Here we have

If some classes a rather natural (

*Journal of Applied Proba*. and

*Advances in Applied Proba.or*some strong correlation are not simple to understand, (e.g.

*Economic Theory, Journal of Economic Theory*and*Journal of Mathematical Economics*)*Insurance: Mathematics and Economics*and

*Management Science*or

*Annals of Statistics*and the

*Journal of Multivariate Analysis*).

Again, it might be possible to spend hours on the graphs, but if I want - someday - to submit something to one of those journals, I guess I have to stop here, and move to something else...

To

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