- Referenced in 99 articles
- means clustering are not robust to missing data, and may lose effectiveness even with ... missing values. Methods for imputing missing data are needed, therefore, to minimize the effect ... investigate automated methods for estimating missing data. Results: We present a comparative study of several ... imputation methods to the amount of missing data over the range of 1–20% missing...
- Referenced in 339 articles
- observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried ... capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according...
- Referenced in 36 articles
- Amelia: Amelia II: A Program for Missing Data , Amelia II ”multiply imputes” missing data ... from a time-series-cross-sectional data set (such as collected by years for each ... have about the values of missing cells in their data. Amelia II also includes useful...
- Referenced in 32 articles
- interactive projection pursuit. Views of the data can be reshaped. Points can be labeled ... labeling, brushing, and sharing of projections. Missing data are accommodated and their patterns...
- Referenced in 16 articles
- coupled with small sample sizes or missing data sets. LogXact aims to provide simple ... continuous and binary, polytonomous, count, and missing data. Users of the software can be confident...
- Referenced in 13 articles
- Chained Equations (MICE): Implementation in Stata. Missing data are a common occurrence in real datasets ... standard route to estimating models with missing covariate data under a missing-at-random assumption ... MICE approach to multiple imputation. Real data from an observational study in ovarian cancer...
- Referenced in 17 articles
- used for exploring the data and the structure of the missing and/or imputed values. Depending ... missings and allows to explore the data including missing values. In addition, the quality...
- Referenced in 15 articles
- ordinary tab-delimited form and have ”missing data...
- Referenced in 29 articles
- impute missing values particularly in the case of mixed-type data. It uses a random ... observed values of a data matrix to predict the missing values. It can be used...
- Referenced in 15 articles
- value shrinkage. OptShrink is a simple, completely data-driven algorithm for denoising a low-rank ... used in the missing data setting and for a large class of noise models...
- Referenced in 10 articles
- arbitrary dimension where the pattern of missing data is monotone. Through the use of parsimonious/shrinkage ... handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference...
- Referenced in 14 articles
- other types of statistical modeling of data. It offers the fitting fuctions found in commercial ... maximum likelihood estimation of parameters from missing data structures, under normal theory. Complex ’nonstandard’ models...
- Referenced in 8 articles
- Monte Carlo likelihood inference for missing data models. We describe a Monte Carlo method ... likelihood estimate (MLE), when there are missing data and the observed data likelihood ... closed form. This method uses simulated missing data that are independent and identically distributed...
- Referenced in 20 articles
- probit or cauchit models to pair-comparison data. Included are the standard ‘unstructured’ Bradley-Terry ... simple and efficient approach to handling missing covariate data, and suitably-defined residuals for diagnostic...
- Referenced in 22 articles
- also be used to impute missing values in data sets through hot deck imputation methods...
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- framework that allows for measurement errors, missing data, vector GARCH errors and constraints...
- Referenced in 27 articles
- table, which is useful for data tables with many missing (unobserved) entries; and adding offsets ... destroying sparsity, which is useful when the data is poorly scaled...
- Referenced in 11 articles
- guidelines regarding, e.g., simulation designs, contamination, missing data models or evaluation criteria are necessary...
- Referenced in 9 articles
- package introduces many new features for data analysis using BART such as variable selection, interaction ... detection, model diagnostic plots, incorporation of missing data and the ability to save trees...
- Referenced in 33 articles
- such methods requires accurate and complete structural data as well as force field parameters such ... addition of missing atomic coordinates to molecular structures from the Protein Data Bank ... calculations, including adding a limited number of missing heavy atoms to biomolecular structures, estimating titration...