Sentence examples for highest mean squared from inspiring English sources

The test statistics again indicate that the estimated coefficients are not equal and hence are not robust across subsets of the inpatient sample according to medical condition, but differences in predictions are small with highest mean absolute difference at the state level of 0.054 and highest mean squared error of 0.005.

Compared to the method of Yin et al. (2009), InferRho appears to have higher mean squared error for tract length estimation but lower mean squared error for crossing-over and gene-conversion rate estimation.

The estimated locus-specific dropout rates are also close to their true values, but with a slightly higher mean squared error of 5.2 × 10−4.

In our test, as long as there is reasonable quantization noise in the measured time intervals, this method always generates high reconstruction mean square error (MSE).

By analyzing the MD simulation results, the surface residues of Candida antarctica lipase B (CalB) with higher root mean square deviation (RMSD) in a methanol solvent were considered as methanol affecting site and selected for site-directed mutagenesis.

Apart from the geometry, the main difference of this sample compared to the device presented in Ref. [27] is the higher root mean square variation of the height (r h) on the island.

The power spectral density of thin films on LaAlO3 and SrTiO3 substrates exhibits an inflated shape at a spatial frequency into 10 μm−1 range with uniformly formed small grains, while LaNiO3 on sapphire showed a high root mean square roughness.

When simple linear regression was repeated using 24hU osmolality as a predictor of total fluid intake, a slightly higher root mean square error (701 ml) and a lower R (41%) resulted.

The on average slightly less accurate atomic coordinates of XRPD structures do lead to systematically higher root mean square Cartesian displacement (RMSCD) values upon energy minimization than for SX structures, but the RMSCD value is still a good indicator for the detection of structures that deserve a closer look.

These models also have higher mean square errors than the neural network models.

Our simulations also show that Poisson regression with fixed intervention effects has high mean square error (relative to the competing method) under high heterogeneity conditions and has poor coverage.