Modeling trend in temperature volatility using generalized LASSO
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In this paper, we present methodology for estimating trends in spatio-temporal volatility. We give two algorithms for computing our estimator which are tailored for dense, gridded observations over both space and time, though these can be easily extended to other structures (time-varying network flows, neuroimaging). We motivate our methodology by applying it to a massive climate dataset and discuss the implications for climate analysis.
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