Fragmentary Taxa, Missing Data, and Ambiguity: Mistaken Assumptions and Conclusions
نویسندگان
چکیده
منابع مشابه
Missing data and convenient assumptions.
T his issue contains the first of several planned statistical primers on methodological problems commonly encountered by outcomes researchers. Although several resources exist for the interested outcomes researcher, they are often scattered throughout different literatures and, in particular, are not translated to the " outcomes " setting. Much of the empirical basis of outcomes research involv...
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The problem of missing data is often considered to be the most important obstacle in reconstructing the phylogeny of fossil taxa and in combining data from diverse characters and taxa for phylogenetic analysis. Empirical and theoretical studies show that including highly incomplete taxa can lead to multiple equally parsimonious trees, poorly resolved consensus trees, and decreased phylogenetic ...
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Information integration is the task of aggregating data from multiple heterogeneous data sources. The understandings of context knowledge of data sources are often the keys to challenging problems in information integration such as handling missing and inconsistent data. Context logic provides a unified framework for the modeling of data sources; nevertheless, the acquisition of large amounts o...
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BACKGROUND Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are 'missing at random' (MAR). This assumption should be checked for plausibility, however it is untestable, thus inferences should be assessed for robustness to departures ...
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Suppose we have a random sample from a population of interest. For each sampled unit we observe the covariate X , which we assume is discrete with support {x1, . . . , xK}. For some units, we also observe the variable Y . Let D = 1 if we observe Y , and D = 0 otherwise. We are interested in the population mean of Y , θ = E[Y ] = ∑K k=1 pkμk, where μk = E[Y |X = xk] and pk = Pr(X = xk). We assum...
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ژورنال
عنوان ژورنال: Systematic Biology
سال: 2002
ISSN: 1076-836X,1063-5157
DOI: 10.1080/10635150252899824