AgroStat2024’s Post

A very useful workshop to learn how to perform analysis of covariance, test invariance on a measurement model, and how to fit a cross-lagged model. Do not miss AgroStat2024

[PDF] Solution of logistic differential equation in an uncertain environment using neutrosophic numbers M Parikh, M Sahni The modeling and forecasting of population dynamics, as well as growth in biological systems more generally, have required the construction of various growth models. This paper presents the logistic growth model, which is a modified version …

When you refer to the Central Limit Theorem do always specify which one you mean: ➡️ Lindeberg-Levy the oldest one; needs IID variables and finite mean and variance. In other words - it's quite limited (independently and same distributed random variables) but has simple assumptions offering faster convergence in distribution - no free lunch 🤷♂️ ➡️ Lyapunov relaxes the need for same distributions, but has strong assumption: finite first 2 moments and sufficiently fast vanishing higher moments as N grows (meaning "not to heavy tails" ) - no free lunch 🤷♂️ ➡️ Lindeberg as Lyapunov, relaxes the need for identically distributed variables, but has a stronger condition, that no individual variable dominates the sum. Or differently - that contributions to the total variance from variables showing large deviations from their means variable vanish as N grows. BTW, if Lindeberg condition holds, then Lyapunov one holds too. ➡️ Gibrat's law aka Multiplicative CLT for IID log-normally distributed variables. Naturally uses the product rather than sums, leading to the log-normal distribution. Or the normal one for log(sum). ➡️ Generalized Kolmogorov-Gniedenko LT about converging to alpha-stable distributions (Gaussian is a special case, but not the main target here!) for IID variables but do NOT require finite variance and allows for heavy-tailed ones (Levy stable ones). at least as far as I recall... ➡️ CLTs mentioned in the Wither's and Orey's papers - allowing for dependent variables. e.g. read https://lnkd.in/dBHdEbJU Also, check out this interesting article. https://lnkd.in/di44fQpe #statistics #datascience #clt

Getting stuck on an SAT Math question? Ask yourself: Can I just use Desmos? Many questions, especially those that include equations, can be solved by using the Desmos calculator. If you’re stuck, stop and check to see if Desmos can make the problem easier! For more tips and tricks for the SAT Math section, visit our blog: https://loom.ly/XtHHvM4 #SAT #Math #SATTest #TestPrep

Good and concise summary.

can we able to create indexes on derived columns?

The PARIO redefines soil texture analysis by using automation to reduce the time and effort needed for soil particle size analysis. With analysis time reduced to just 3-8 hours, precision detection, seamless USDA classification integration, and robust compatibility with lab software, it ensures reliable, efficient results for your research needs. Discover how PARIO can enhance your soil research: https://lnkd.in/gTJqJvct #soil #soilscience #analysis #research

Did you know that a computational score can assess the health of soil? 🌱 🌾 Indeed, a structural equation modeling – a form of regression – analyzes biological and physical soil parameters to derive a soil health scoring function. 📊 As the structural equation modeling approach assesses and weights simultaneously several soil health indicators, it already accounts for measurement errors, making it more reliable.

Good evening everyone, I am reproposing a previously shared article on the generalized inhomogeneous Burgers equation. I have revised the form and notation and corrected some errors in the calculations. Here I also post the abstract and the introduction to the article, waiting to send it to some platform and also to some journal for peer review.  In this article we study generalizations of the inhomogeneous Burgers equation. First at the operator level, in the sense that we replace classical differential derivations by operators with certain properties, and then we increase the spatial dimensions of the Burgers equation, which is usually studied in one spatial dimension. This allows us, in one dimension, to find mathematical relationships between solutions of hyperbolic Brownian motion and the Burgers equations, which usually study the behaviour of mechanical fluids, and also, through appropriate transformations, to obtain in some cases exact solutions that depend on Hermite polynomials composed of appropriate functions. In the multi-dimensional case, this generalization allows us, by means of the method of invariant spaces, to find exact solutions on Riemannian and pseudo-Riemannian varieties, such as Schwarzschild and Ricci Solitons space, with time dictated by fractional derivatives, such as a Caputo-type operator of fractional evolution.

I am happy to announce the publication of our paper "Semiparametric copula models applied to the decomposition of claim amounts" with Sébastien FARKAS in Scandinavian Actuarial Journal. In this paper, we consider claims that may simultaneously activate several guarantees, and we propose a conditional copula model to take into account dependence between the losses affected to each line of business. Preprint available here : https://lnkd.in/evt_JsW8