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Tag Archives: econometrics
Hebbian History
memeengine replied to your post: Or can the influence of any ancestor ever fade down to zero? (Or, well… to arbitrarily small size?) An AR[1] process can go arbitrarily ↓0 as time↑∞. But in real life the sins of the fathers … Continue reading
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Tagged Africa, autoregression, civil law, class mobility, common law, Daron Acemoglu, econometrics, economic growth, GDP, GDP per capita, growth, Hebbian learning, hierarchy, history, local linear approximation, Louisiana, methodology, Nathan Nunn, OLS, power, race, racism, regression analysis, slavery, statistics, structure, Sweden, the law, USA, wealth
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“You can keep blaming your parents for your life in your 20’s, but by the time you’re 30 it’s your own fault.” —having a difficult time getting an original source on this quote This is like unknotting an autoregressive term … Continue reading
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Tagged autoregression, autoregressive, econometrics, family, individualism, kids, life, noncommutativ, parents, psychology, sociology, statistics, the future, the past, time series
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http://video.ted.com/assets/player/swf/EmbedPlayer.swf Noticed: It’s easier for me to grok statistical significance (p’s and t’s) from a scatterplot than magnitude (β’s). Even though magnitude can be the most important thing, it’s “hidden” off to the left. Note to self: look off to the left … Continue reading
It takes ~20 observations to verify your first significant digit of the mean with confidence. Do you know how many observations it takes to verify your first sigfig of the variance? More like 1000. And that’s just to get one … Continue reading
I feel vindicated in several ways by the Netflix Engineering team’s recent blog post explaining what they did with the results of the Netflix Prize. What they wrote confirms what I’ve been saying about recommendations as well as my experience … Continue reading
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Tagged BellKor, business, collaborative filtering, computational homology, consulting, dimension reduction, domain knowledge, Doug Hinton, econometrics, ensemble methods, Gilbert Strang, John Wilder Tukey, Kaggle, linear algebra, machine learning, Netflix, neural networks, Pragmatic Chaos, RBM's, recommendation engines, regression, restricted Boltzann machines, statistics, SVD, topology, web startups
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A First Lesson in Econometrics by John J. Siegfried
As nice as it is to be able to assume normality, … there are problems. The most obvious problem is that we could be wrong. One … very nice thing … is that, in many situations, … [being wrong] won’t … Continue reading