Estimation of the data variance
Resampling Variance Estimation in Surveys with Missing Data
... unbiased variance estimators, even when large proportions of the data are ...missing data, coupled to appropriate resampling schemes, may produce reasonable results, though it would be essential to ...
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Nonparametric variance function estimation with missing data
... the estimation of the conditional variance function in regression models has gained ...conditional variance function play a crucial role, both being totally ...the estimation of the ...
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Resamping variance estimation in surveys with missing data
... The bootstrap used 100 replicates of the calibrated and imputed Horwitz–Thompson estimator, obtained by the procedure of Shao and Sitter (1996), that is, with missing responses imputed deterministically using a linear ...
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Automatic variance control and variance estimation loops
... Conversely if the variance is over-estimated the convergence of the LMS algorithm may well be too slow [1]. This basic problem has been recognised in [3] where the authors alter the LMS algorithm to implicitly ...
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Methods of variance component estimation
... Bayesian and empirical Bayesian approach and Markov chain Monte Carlo (MCMC) methods We now only shortly mention methods for the so-called Bayesian approach. In this approach we assume that the parameters of the ...
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A. Bootstrap and Jackknife Variance Estimation
... missing data is important in many areas, such as reducing non-response bias in surveys and maintaining medical ...the estimation of imputation uncertainty, which can be implemented as part of any imputation ...
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Estimation of variance functions in assays with ossible unequal replication and nonnormal data
... Table 1 lists AREs for estimators based on (2.1) with T(x) = log x and x relative to using T(x) = x2 for contaminated normal distributions, so is the approximate analogue of Table 2 of D[r] ...
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AN EVALUATION OF THE USE OF PERSONAL COMPUTERS FOR VARIANCE ESTIMATION WITH COMPLEX SURVEY DATA
... Features available in these packages as well as issues related to the actual implementation of the programs, including data preparation steps, number of programming[r] ...
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An Estimation of Unknown Variance of a Normal Distribution: Application to Borno State Rainfall Data
... Bayesian estimation of unknown variance of a normal distribution is examined under different priors using Gibbs sampling approach with an assumption that mean is ...unknown variance of the Normal ...
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Maximum likelihood estimation of variance components
... the variance components model can be used to describe data arising in fields as diverse as designed experiments in agriculture and observational studies in the social ...the estimation of ...
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Estimation of variance components and breeding values
... raw data were shorter than 240 days which may cause a bias in the genetic trend ...the data. Data quality could be improved by adopting ICAR guidelines and best practices in milk ...receive ...
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Variance estimation for a low income proportion
... survey data, it is important to have information about the sampling variability of the ...The estimation of standard errors for such proportions is, however, not simply a matter of applying standard methods ...
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Asymptotic Variance Estimation for the Misclassification SIMEX
... During the study period three calibration exercises involving 92, 32 and 24 children, respectively were devoted to the scoring of caries experience. At the end of each of the three calibration exercises the sensitivity ...
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A NOTE ON ESTIMATION OF VARIANCE COMPONENTS BY MAXIMIZATION
... Repeated measures is a common data structure with multiple measurements on a single unit repeated over time. Multivariate linear models with correlated errors. Repeated measurements analysis is widely used in many ...
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Gini estimation under infinite variance
... the data generating process (which, in most cases, is unknown for fat-tailed data ...the data, which can be usually assumed to be a function of the tail index itself, as in the Paretian case where µ ...
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Sieve Plateau Variance Estimators: A New Approach to Confidence Interval Estimation for Dependent Data
... These data are a subset of a much larger data set consisting of 162 classrooms in three California school ...VR data in the scientific literature to ...
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Nonparametric Variance Estimation for Nearest Neighbor Imputation
... Nonparametric Variance Estimation for Nearest Neighbor Imputation Jun Shao 1 Nearest neighbor imputation is a popular nonparametric hot deck imputation method used to compensate for nonresponse in sample ...
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Bayesian Nonparametric Estimation of Ex post Variance
... Variance estimation is central to many questions in finance and ...ex-post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency ...ex-post ...
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Variance Reduction Procedures for Market Risk Estimation
... the variance-covariance ...past data is not complete or does not represent the future market movements in a reliable ...a data is usually used for estimation of the parameters of assumed ...
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A unified approach to linearization variance estimation from survey data after imputation for item nonresponse
... linearization variance estimator is given by ˆ V for fractional imputation and ˆ V + V ∗ for hot deck imputation, where the pseudo-value ˆ η i is given by ( 24 ) in the case of the population mean and by ( 25 ) ...
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