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| Registros recuperados: 15 | |
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| Carlin, John B.; Galati, John C.; Royston, Patrick. |
| A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based on a simple data management paradigm in which the imputed datasets are all stored along with the original data in a single dataset with a vertically stacked format, as proposed by Royston in his ice and micombine commands. Stacking into a single dataset simplifies the management of the imputed datasets compared with storing them individually. Analysis and manipulation of the stacked datasets is performed with a new prefix command, mim, which can accommodate data... |
| Tipo: Article |
Palavras-chave: Mim; Mimstack; Ice; Micombine; Miset; Mifit; Multiple imputation; Missing data; Missing at random; Research Methods/ Statistical Methods. |
| Ano: 2008 |
URL: http://purl.umn.edu/120928 |
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| Phan, Thi-thu-hong; Poisson Caillault, Émilie Poisson; Lefebvre, Alain; Bigand, André. |
| Time series with missing values occur in almost any domain of applied sciences. Ignoring missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). This paper proposes an approach to fill in large gap(s) within time series data under the assumption of effective information. To obtain the imputation of missing values, we find the most similar sub-sequence to the sub-sequence before (resp. after) the missing values, then complete the gap by the next (resp. previous) sub-sequence of the most similar one. Dynamic Time Warping algorithm is applied to compare sub-sequences, and combined with the shape-feature extraction algorithm for reducing insignificant solutions. Eight well-known and real-world data... |
| Tipo: Text |
Palavras-chave: Imputation; Missing data; Univariate time series; DTW; Similarity. |
| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00396/50696/51387.pdf |
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| White, Ian R.; Higgins, Julian P.T.. |
| A new command, metamiss, performs meta-analysis with binary outcomes when some or all studies have missing data. Missing values can be imputed as successes, as failures, according to observed event rates, or by a combination of these according to reported reasons for the data being missing. Alternatively, the user can specify the value of, or a prior distribution for, the informative missingness odds ratio. |
| Tipo: Article |
Palavras-chave: Metamiss; Meta-analysis; Missing data; Informative missingness odds ratio; Research Methods/ Statistical Methods. |
| Ano: 2009 |
URL: http://purl.umn.edu/122700 |
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| Registros recuperados: 15 | |
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