Category Archives: non-parametric model

Calculating Derivatives from Random Forests

Posted on 27 June 2020 by ecoquant

(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 | Leave a comment

Result of our own fiddling: Bob Watson and climate risk

Posted on 20 March 2019 by ecoquant

https://sms.cam.ac.uk/media/746045 Professor Bob Watson, University of East Anglia, presents the summary risk, climate change: The question is not whether the Earth’s climate will change in response to human activities, but when, where and by how much. Human activities are changing … Continue reading →

Posted in Anthropocene, attribution, carbon dioxide, Carbon Worshipers, catastrophe modeling, climate, climate change, climate data, climate disruption, climate economics, climate education, climate grief, climate justice, ecological disruption, ecology, Ecology Action, environment, global blinding, global warming, greenhouse gases, greenwashing, meteorology, National Center for Atmospheric Research, non-parametric model, Principles of Planetary Climate, radiative forcing, reasonableness, science, solar democracy, solar domination, solar energy, Solar Freakin' Roadways, solar power, SolarPV.tv, Solpad, Sonnen community, Spaceship Earth, stranded assets, sustainability, the energy of the people, the green century, the tragedy of our present civilization, the value of financial assets, tragedy of the horizon, utility company death spiral, water, wind energy, wind power | Leave a comment

Stream flow and P-splines: Using built-in estimates for smoothing

Posted on 28 January 2019 by ecoquant

Mother Brook in Dedham Massachusetts was the first man-made canal in the United States. Dug in 1639, it connects the Charles River at Dedham, to the Neponset River in the Hyde Park section of Boston. It was originally an important … Continue reading →

Posted in American Statistical Association, citizen data, citizen science, Clausius-Clapeyron equation, Commonwealth of Massachusetts, cross-validation, data science, dependent data, descriptive statistics, dynamic linear models, empirical likelihood, environment, flooding, floods, Grant Foster, hydrology, likelihood-free, meteorological models, model-free forecasting, non-mechanistic modeling, non-parametric, non-parametric model, non-parametric statistics, numerical algorithms, precipitation, quantitative ecology, statistical dependence, statistical series, stream flow, Tamino, the bootstrap, time series, water vapor | 2 Comments

The Johnson-Lindenstrauss Lemma, and the paradoxical power of random linear operators. Part 1.

Posted on 20 November 2018 by ecoquant

Updated, 2018-12-04 I’ll be discussing the ramifications of: William B. Johnson and Joram Lindenstrauss, “Extensions of Lipschitz mappings into a Hilbert space, Contemporary Mathematics, 26:189–206, 1984. for several posts here. Some introduction and links to proofs and explications will be … Continue reading →

Posted in clustering, data science, dimension reduction, information theoretic statistics, Johnson-Lindenstrauss Lemma, k-NN, Locality Sensitive Hashing, mathematics, maths, multivariate statistics, non-parametric model, numerical algorithms, numerical linear algebra, point pattern analysis, random projections, recommender systems, science, stochastic algorithms, stochastics, subspace projection methods | 1 Comment

Why smooth?

Posted on 8 December 2016 by ecoquant

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 | 1 Comment