📑 A new paper by CPC-CG members introduces the first method that can predict how many relatives of any kind a person is likely to have at different points in their life, and how likely each outcome is:
https://www.demographic-research.org/articles/volume/54/9

#demography #kinship #mathematicaldemography #populationstudies #lifeCourse #mortality #fertility #probability #matrixalgebra #combinatorics #convolution #kin #familyStructure #analyticModel #populationResearch #population #family #familystructures #demographicforecasting

http://financemetrics.scienceontheweb.net Maximising the value of a portfolio. Calculating #Variance, #CoVariance and portfolio Variance using the magic of Matrix Algebra. Calculations feature 5 ftse stocks, BP, Vodafone, UU.L, Tesco and Morrison. Detailed exposition leads one through x transpose multiplied b x. #MatrixAlgebra

NGL, I'm really struggling with #matrixalgebra and it's honestly making me regret my degree XD I've spent 2hrs on the same problem, and I still haven't come close to solving it

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#freeonlinelearning #colisty #courselist #sasandrintegration #sas/iml #sas/stat #dataanalysis #statisticalmodeling #matrixalgebra #logisticregression #anova #mixedmodels #programmingwithsas #rtosas

https://colisty.netlify.app/courses/sas_-programming-for-r-users/

Any #math people (especially #MatrixAlgebra people) who can help with my deceptively simple matrix decomposition question? https://math.stackexchange.com/questions/4848363/inverse-symmetric-square-root-of-a-diagonal-plus-a-low-rank-symmetric-matrix

If you are looking for something to do, why not brush up on #MatrixAlgebra? Curran and Bauer (CenterStat) have releaased a free matrix algebra refresher aimed at quantitative researchers who need to know what their black box models are doing ;-]

Their content is pretty decent. And their podcast "Quantitude" is entertaining, too.

https://centerstat.org/matrix-review/

#MatrixAlgebra

Okay, so I figured this part out: a matrix multiplied by its transposition is a covariance matrix. By which I mean: the higher the value in a given (row, col), the more data in those axes were correlated.

https://en.wikipedia.org/wiki/Covariance_matrix

To simplify, consider a 3x3 matrix A and multiply A by transpose(A).

What each cell of the result is telling you is how likely it is that when you change the value on the row axis, the value on the column axis changes the same way. So the diagonal will always be large, because data on an axis will always correlate with itself (i.e. when you change the value of x, the value of x changes in exactly the same way, x*x = x^2), but cell 0,2, for example, tells you how much changing x causes z to change the same way (if it’s the same value as cell 0,0, then the points lie on a diagonal in the xz-plane: changing x causes the exact same change in z).

I still need to cogitate a bit on why the eigenvector with the largest eigenvalue of this matrix is the axis along which the data has the highest variance in the original coordinate space.