672 research outputs found
Multivariate manifold-valued curve regression in time
Fr\'echet global regression is extended to the context of bivariate curve
stochastic processes with values in a Riemannian manifold. The proposed
regression predictor arises as a reformulation of the standard least-squares
parametric linear predictor in terms of a weighted Fr\'echet functional mean.
Specifically, in our context, in this reformulation, the Euclidean distance is
replaced by the integrated quadratic geodesic distance. The regression
predictor is then obtained from the weighted Fr\'echet curve mean, lying in the
time-varying geodesic submanifold, generated by the regressor process
components involved in the time correlation range. The regularized Fr\'echet
weights are computed in the time-varying tangent spaces. The uniform
weak-consistency of the regression predictor is proved. Model selection is also
addressed. A simulation study is undertaken to illustrate the performance of
the spherical curve variable selection algorithm proposed in a multivariate
framework.Comment: 24 pages, 8 Figure
COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning
A multiple objective space-time forecasting approach is presented involving
cyclical curve log-regression, and multivariate time series spatial residual
correlation analysis. Specifically, the mean quadratic loss function is
minimized in the framework of trigonometric regression. While, in our
subsequent spatial residual correlation analysis, maximization of the
likelihood allows us to compute the posterior mode in a Bayesian multivariate
time series soft-data framework. The presented approach is applied to the
analysis of COVID-19 mortality in the first wave affecting the Spanish
Communities, since March, 8, 2020 until May, 13, 2020. An empirical comparative
study with Machine Learning (ML) regression, based on random k-fold
cross-validation, and bootstrapping confidence interval and probability density
estimation, is carried out. This empirical analysis also investigates the
performance of ML regression models in a hard- and soft- data frameworks. The
results could be extrapolated to other counts, countries, and posterior
COVID-19 waves.Comment: This paper is currently submitte
Consumption patterns: A proposed model for measurement of solution palatability in pigs
In animal production, the palatability of feeds or solutions has typically been inferred from measurements of preference or acceptance. However, laboratory studies in rats have demonstrated that palatability quantified through the analysis of the microstructure of licking can dissociate from simple measures of consumption. The aim of this study was to evaluate palatability in pigs by using consumption patterns. Pigs (n = 24) were exposed (in pairs, with video recording) to different sucrose solutions (0.5, 1, 2, 4, 8, 16, and 32%) over 7 consecutive 10-min tests (1 concentration/d). Total consumption, number of consumption approaches (A), and real consumption time (RCT) were measured. Palatability was estimated through consumption pattern (RCT/A), analogous to the licks/bout measure used in rats. Data was analyzed by sucrose concentration. Spearman correlation coefficients were estimated between the logarithm of sucrose concentration and total consumption, A, RCT, and RCT/A. Total consumption and RCT showed inverted U functions relative to sucrose concentration. Consumption pattern (RCT/A) presented a dose effect (P < 0.005) and positive correlations with sucrose concentration (R = 0.23, P = 0.034). As with rats, consumption pattern could represent an interesting and novel measure of feeding behavior, reflecting palatability in pigs
Downscaling ECMWF seasonal precipitation forecasts in Europe using the RCA model
6 páginas, 4 figuras.--Licencia Creative Commons Reconocimiento-No comercial 3.0The operational performance and usefulness of regional climate models at seasonal time scales are assessed by downscaling an ensemble of global seasonal forecasts. The Rossby Centre RCA regional model was applied to downscale a five-member ensemble from the ECMWF System3 global model in the European Atlantic domain for the period 1981–2001. One month lead time global and regional precipitation predictions were compared over Europe—and particularly over Spain—focusing the study in SON (autumn) dry events. A robust tercile-based probabilistic validation approach was applied to compare the forecasts from global and regional models, obtaining significant skill in both cases, but over a wider area for the later. Finally, we also analyse the performance of a mixed ensemble combining both forecasts.This work was partly supported by projects ENSEMBLES from the 6th FP EU(GOCE-CT-2003-505539), EXTREMBLES (CGL2010-21869) and CORWES (CGL2010-22158-C02) from the Spanish Ministry MICINN (Plan Nacional de I+D+i) and
by ESCENA (200800050084265) from the Spanish Ministry MARM.Peer reviewe
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