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Klassisch @DB_Bahn - bin in München am Weg zur @GfoeSoc, und musste die letzte Strecke mit #SEV fahren. Vier von uns standen im Regen bei der Haltestelle, und der Fahrer ist vorbeigefahren, hat uns nett zugewinkt, und ist dann leer weiter gefahren. Zumindest war er höflich 🤔?.

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The issue is that we end up with some pretty weird heteroskedasticity in the estimates of both xT and sigma - meaning that if we, e.g., just try to do a regression comparing the two, regression dilution kicks in, and we underestimate b1. Any tips? (end).

Suppose xO1 and xO2 include many paired noisy observations, e.g. xO1 = {1.1, 1.5, 2.2, 0.5, . }, xO2 = {1.5, 1.2, 1,8, 0.7, . }. I know I can estimate sigma_i^2 ~ (xO1i - xO2i)^2/2, and xTi ~ (xO1i + xO2i)/2. But, how can I go about estimating b0 and b1?. .

"True" system state is xT, with many observations xTi drawn from a distribution with strictly positive values. Observation error is normally distributed with mean 0, and sigma = b0 + b1*xTi. I don't know b0, b1, or xT, but have access to repeated noisy observations xO1 and xO2.

I'm trying to fit a model describing how observation error varies with system state, based on repeated noisy observations. I'm sure this is a solved problem, but I'm not finding the right keywords to search for. Any suggestions? Model is as follows:.

Looks like a cool paper!. General question about DID: does it assume linearity (or at least a known functional form)? E.g. if y = {2,4} and x = {3,9} for times 1 & 2, then DID is (9-4)-(3-2) = 4 - so, maybe a treatment effect, or just a nonlinear function, e.g. f(t+1) = f(t)^2.

RT @BExplo_research: . and another publication from @biodivkultur together with the Explos! 🎉The newly developed mowing index can be used….

RT @SpaceTimeEco: plant Ellenberg #indicatorValues do not reflect.#soilMoisture but atmospheric #VaporPressureDeficit. see our new #microcl….