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Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11807 (17 self)
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situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
Bootstrapping the Stein Variance Estimator
"... This paper applies the bootstrap methods proposed by Efron (1979) to the Stein variance estimator proposed by Stein (1964). It is shown by Monte Carlo experiments that the parametric bootstrap yields the considerable accurate estimates of mean, standard error and confidence limits of the Stein va ..."
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This paper applies the bootstrap methods proposed by Efron (1979) to the Stein variance estimator proposed by Stein (1964). It is shown by Monte Carlo experiments that the parametric bootstrap yields the considerable accurate estimates of mean, standard error and confidence limits of the Stein
Variance Estimation for Domains
, 2005
"... publishes monthly estimates of employment levels, one of the key indicators of the U.S. economy, for many domains. To assess the quality of these estimates, it is important to publish their associated standard error estimates. In our simulation study, the standard designbased variance estimators of ..."
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publishes monthly estimates of employment levels, one of the key indicators of the U.S. economy, for many domains. To assess the quality of these estimates, it is important to publish their associated standard error estimates. In our simulation study, the standard designbased variance estimators
Chapter 12. Variance Estimation
"... Sampling error is the uncertainty associated with an estimate that is based on data gathered from a sample of the population rather than the full population. Note that samplebased estimates will vary depending on the particular sample selected from the population. Measures of the magnitude of sampl ..."
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of sampling error, such as the variance and the standard error (the square root of the variance), reflect the variation in the estimates over all possible samples that could have been selected from the population using the same sampling methodology. The American Community Survey (ACS) is committed
Chapter 12. Variance Estimation
"... Sampling error is the difference between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population (as from a census). Note that samplebased estimates will vary depending on the particular sample selected from the population ..."
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the population. Measures of the magnitude of sampling error, such as the variance and the standard error (the square root of the variance), reflect the variation in the estimates over all possible samples that could have been selected from the population using the same sampling methodology. The American
Chapter 12. Variance Estimation
"... Sampling error is the difference between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population (as from a census). Note that samplebased estimates will vary depending on the particular sample selected from the population ..."
Abstract
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the population. Measures of the magnitude of sampling error, such as the variance and the standard error (the square root of the variance), reflect the variation in the estimates over all possible samples that could have been selected from the population using the same sampling methodology. The American
Variance Estimates and Model Selection
, 1998
"... The large majority of the criteria for model selection are functions of the # 2 , the usual variance estimate for a regression model. The validity of the usual variance estimate depends on some assumptions, most critically the validity of the model being estimated. This is often violated in model se ..."
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Cited by 1 (0 self)
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The large majority of the criteria for model selection are functions of the # 2 , the usual variance estimate for a regression model. The validity of the usual variance estimate depends on some assumptions, most critically the validity of the model being estimated. This is often violated in model
Derandomizing and Rerandomizing Variance Estimators
, 1997
"... This technical report is meant to accompany the paper [7] and should be read in conjunction with that work. It describes several concepts which were alluded to in [7] but not elaborated on. We give algorithms for computing the derandomized estimator, introduce the concept of rerandomization, examine ..."
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Cited by 1 (1 self)
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, examine heuristically the question of how the derandomized and standard variance estimators behave as the splitting constants get small, and consider the use of the derandomized estimator in sequential stopping situations.
Poststratification and conditional variance estimation
 Journal of the American Statistical Association
, 1993
"... Poststratification estimation is a technique used in sample surveys to improve efficiency of estimators. Survey weights are adjusted to force the estimated numbers of units in each of a set of estimation cells to be equal to known population totals. The resulting weights are then used in forming es ..."
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Cited by 12 (6 self)
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estimates of means or totals of variables collected in the survey. For example, in a household survey the estimation cells may be based on age/race/sex categories of individuals and the known totals may come from the most recent population census. Although the variance of a poststratified estimator can
Results 1  10
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921,351