Sentence examples for multiple statistical models from inspiring English sources

The systematic method with multiple statistical models was coded for numerical solutions, and demonstrated for eight HMA IC projects.

The objective of this study was to compare predictive values of multiple statistical models for diagnosing renal colic patients in the same data set.

Multivariate analysis can explicitly test for multiple statistical models, including the designed paradigm, and allows for spatial and temporal model detection.

Here we present results of multiple statistical models that predict daily, gridded surface salinity at 1 km resolution across Chesapeake Bay as a function of level 2 surface reflectance estimates from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua platform.

Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs) of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior) sequence-based biological knowledge.

It also uses multiple statistical models to better compress quality values by the Huffman coding.

Statistical prediction: Multiple empirical, statistical models are used to estimate future blood glucose values and their error bounds.

When multiple possible statistical models fit the observed data similarly well, it is risky to make inferences and predictions based only on a single model [ 47].

The multiple regression statistical model is used to obtain quantitatively the signal of the quasi-biennial oscillation (QBO) and the trend.

Model ensembles such as multiple statistical approaches, multiple dynamic models or multiple hybrid models for drought predictions were highlighted.

A fourth and recent approach for estimating air pollutant exposures, called "land use regression" (LUR) models, fit concentrations measured at multiple sites using statistical models and land characteristics, traffic and other data as independent variables, which then are used to predict pollutant concentrations at other sites [ 13].