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Therefore, for a such time series data with non-linear trend, linear spline interpolation brings a better estimate for two or more sequentially missing values than linear regression.
These analyses are based on likelihood estimation, which is more robust to missing values than moment estimation.
Socioeconomic position exhibited more missing values than other variables since it was derived from ten other variables.
This was especially observed for PNC1 which had a higher number of missing values than the other MCH indicators.
More missing values than this were taken as an indicator of fundamental insufficiencies in the respondent's answers.
The alpha-factor dataset contained more time points than the cdc28 and the elutriation dataset with fewer missing values than the cdc15 dataset.
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List for the number of missing values per country: In addition, since each socio-economic variable is expressed in time and have strong indirect correlation (r = − 0.8413), the recent year has less probability of having missing value than the old one.
All markers with more than 20% missing values, more than 5% heterozygous genotypes, or minor allele frequencies less than 2.5% were excluded from the analysis.
Additional file 1: List of the selected relevant indicators with bivariate correlation more than 0.2 with MMR, missing values less than 20% and P-value less than 0.05 by category.
In the first scenario, indicators with correlations more than 0.5, significance level of less than 0.05, and missing values less than 20% were evaluated.
Only indicators with statistically significant correlation more than 0.2, and missing values less than 20% were maintained.
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