نتایج جستجو برای: imputation
تعداد نتایج: 16711 فیلتر نتایج به سال:
Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been developed. Current imputation methods rely on single imputation, which ignores the variation in estimation due to imputation. An alternative to single imputation is multiple imputation. In t...
An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we ...
Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple ...
OBJECTIVES Missing laboratory data is a common issue, but the optimal method of imputation of missing values has not been determined. The aims of our study were to compare the accuracy of four imputation methods for missing completely at random laboratory data and to compare the effect of the imputed values on the accuracy of two clinical predictive models. DESIGN Retrospective cohort analysi...
Missing data are often a problem in social science data. Imputation methods fill in the missing responses and lead, under certain conditions, to valid inference. This article reviews several imputation methods used in the social sciences and discusses advantages and disadvantages of these methods in practice. Simpler imputation methods as well as more advanced methods, such as fractional and mu...
The Major work in data pre-processing is handling Missing value imputation in Hepatitis Disease Diagnosis which is one of the primary stage in data mining. Many health datasets are typically imperfect. Just removing the cases from the original datasets can fetch added problems than elucidations. A appropriate technique for missing value imputation can assist to generate high-quality datasets fo...
All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes ...
We are concerned with multiple imputation of the ratio of two variables, which is to be used as a covariate in a regression analysis. If the numerator and denominator are not missing simultaneously, it seems sensible to make use of the observed variable in the imputation model. One such strategy is to impute missing values for the numerator and denominator, or the log-transformed numerator and ...
background: the aim of the article is demonstrating an application of multiple imputation (mi) for handling missing clinical data in the setting of rheumatologic surveys using data derived from 10291 people participating in the first phase of the community oriented program for control of rheumatic disorders (copcord) in iran . methods: five data subsets were produced from the original data set....
To compensate for item nonresponse, hot deck imputation procedures replace missing values with values that occur in the sample. Fractional hot deck imputation replaces each missing observation with a set of imputed values and assigns a weight to each imputed value. Under the model in which observations in an imputation cell are independently and identically distributed, fractional hot deck impu...
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