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Without a priori data, the estimation of an optimal weighting matrix relies heavily on the availability of statistical information that can be extracted from the measured data.
Special attention is paid to the design of the index, quality and sampling data, the estimation of basis risk and its influence on insurance demand, and index-insurance supply.
Although the second method has proved to give the best results, in small-sample settings such as the one found in omics data, the estimation of posterior probabilities and derivative of ROC curve are both lacking of precision leading to far from optimal abstaining areas.
Considering the DGLM from an intuitive perspective, when analyzing weekly data, the estimation of the seasonal variation is performed for every week by considering the nearest 52 weeks and estimating the seasonal variation based on these data.
Comparing self-reported vs. GPS data, the estimation of personal exposure to PB-PAH did not differ markedly at the population level, but the difference was large at an individual level, which has significant implications in air pollution epidemiological studies.
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This means that with the given data, the estimations of the parameters are those that occur with the highest probability.
Health profiles do not incorporate values/preference information which requires such data for the estimation of quality-adjusted life years (QALY).
Additionally, the removing of data, makes the estimation of autoregressive effects complicated.
The RSSI, alone, cannot pinpoint the location, but can contribute data for the estimation of distance.
The results are promising, showing that the proposed methods successfully incorporate the GPS data in the estimation of traffic.
The methods reported were compared, particularly in terms of the collection of biokinetic data and the estimation of dose results.
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