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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
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  • Kalman filtering and smoothing; dynamic linear models



    Also, see datasets and R examples to accompany this excellent text.





    I have used dlm almost exclusively, except when extreme efficiency was required. Since Jouni Helske's KFAS was rewritten, though, I'm increasingly drawn to it, because the noise sources it supports are more diverse than dlm's. KFAS uses the notation and approaches of Durbin, Koopman, and Harvey.

    ``The real problem is that programmers have spent far too much time worrying about efficiency in the wrong places and at the wrong times; premature optimization is the root of all evil (or at least most of it) in programming.''
    Professor Donald Knuth, 1974
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