### Distributed Solar: The Democratizaton of Energy

### Blogroll

### climate change

- AIP's history of global warming science: impacts
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- `The unchained goddess'
- `Who to believe on climate change': Simple checks
- World Weather Attribution
- Berkeley Earth Surface Temperature
- NOAA Annual Greenhouse Gas Index report
- "Getting to the Energy Future We Want," Dr Steven Chu
- Simple models of climate change
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper)

### Archives

### Jan Galkowski

# Category Archives: splines

## Calculating Derivatives from Random Forests

(Comment on prediction intervals for random forests, and links to a paper.) (Edits to repair smudges, 2020-06-28, about 0945 EDT. Closing comment, 2020-06-30, 1450 EDT.) There are lots of ways of learning about mathematical constructs, even about actual machines. One … Continue reading

Posted in bridge to somewhere, Calculus, dependent data, dynamic generalized linear models, dynamical systems, ensemble methods, ensemble models, filtering, forecasting, hierarchical clustering, linear regression, model-free forecasting, Monte Carlo Statistical Methods, non-mechanistic modeling, non-parametric model, non-parametric statistics, numerical algorithms, prediction, R statistical programming language, random forests, regression, sampling, splines, statistical learning, statistical series, statistics, time derivatives, time series
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## Phase plane plots of COVID-19 deaths

There are many ways of presenting analytical summaries of new series data for which the underlying mechanisms are incompletely understood. With respect to series describing the COVID-19 pandemic, Tamino has used piecewise linear models. I have mentioned how I prefered … Continue reading

## “Lockdown WORKS”

Originally posted on Open Mind:

Over 2400 Americans died yesterday from Coronavirus. Here are the new deaths per day (“daily mortality”) in the USA since March 10, 2020 (note: this is an exponential plot) As bad as that news is,…

## Why smooth?

I’ve encountered a number of blog posts this week which seem not to understand the Bias-Variance Tradeoff in regard to Mean-Squared-Error. These arose in connection with smoothing splines, which I was studying in connection with multivariate adaptive regression splines, that … Continue reading

Posted in Akaike Information Criterion, American Statistical Association, Antarctica, carbon dioxide, climate change, denial, global warming, information theoretic statistics, likelihood-free, multivariate adaptive regression splines, non-parametric model, science denier, smoothing, splines, statistical dependence
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## “All models are wrong. Some models are useful.” — George Box

(Image courtesy of the Damien Garcia.) As a statistician and quant, I’ve thought hard about that oft-cited Boxism. I’m not sure I agree. It’s not that there is such a thing as a perfect model, or correct model, whatever in … Continue reading

Posted in abstraction, American Association for the Advancement of Science, astronomy, astrophysics, mathematics, model-free forecasting, numerics, perceptions, physical materialism, physics, rationality, reason, reasonableness, science, spatial statistics, splines, statistics, the right to know, theoretical physics, time series
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## Gavin Simpson updates his temperature analysis

See the very interesting discussion at his blog, From the bottom of the heap. It would be nice to see some information theoretic measures on these results, though.

Posted in AMETSOC, Anthropocene, astrophysics, Berkeley Earth Surface Temperature project, carbon dioxide, changepoint detection, climate, climate change, climate data, climate disruption, climate models, ecology, environment, evidence, Gavin Simpson, Generalize Additive Models, geophysics, global warming, HadCRUT4, hiatus, Hyper Anthropocene, information theoretic statistics, Kalman filter, maths, meteorology, numerical analysis, R, rationality, reasonableness, splines, time series
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