Sentence examples for local imputation from inspiring English sources

A computational intense local imputation method is the Spline interpolation.

Algorithms used can be divided into two categories: local imputation algorithms and global imputation algorithms.

Local imputation algorithms select a group of genes with the highest relevance (using Euclidian distance [ 17, 23], Pearson correlation [ 22, 23], or covariance estimate [ 46]) to the target gene to impute missing values.

For local imputation algorithms, we used k-Nearest-Neighbors (KNN), iterative k-Nearest-Neighbors (IKNN), sequential k-Nearest-Neighbors (SKNN), least squares adaptive (LSA), local least squares (LLS), iterative LLS ILLSS) and sequential LLS SLLSS).

The data were log2 transformed and missing data were imputed using local least squares imputation from the pcaMethods package [ 16].

In addition, GL2P is less sensitive to the number of neighbors than other local learning-based imputation methods.

Two steps are included in the local least squares imputation procedure [ 2]: 1) selecting k genes based on Pearson correlation coefficients or Euclidean distance; 2) regression and estimation.

We used local least squares imputation (Kim et al. 2005), which is a method for estimating missing values in a matrix (Supplementary Figure S4).

Simple local methods for data imputation such as NN, Mean and Linear Interpolation were shown to be effective, especially when signal's average levels are estimated (Junninen et al. 2004).

Kim and co-workers have extended the Least Square Imputation to Local Least Square Imputation (LLSI) [ 28].

Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local residual draws (LRD) instead borrows the donor's residual.