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Minimum Sample Size Requirements for Seasonal Forecasting Models
 In Foresight, Issue 6
, 2007
"... much data do I need? ” is probably the most common question our business clients “How ask us. The answer is rarely simple. Rather ..."
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Cited by 6 (0 self)
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much data do I need? ” is probably the most common question our business clients “How ask us. The answer is rarely simple. Rather
Exact computation of minimum sample size for estimation of Poisson parameters,” arXiv:0707.2116v1 [math.ST
, 2007
"... In this paper, we develop an exact method for the determination of the minimum sample size for the estimation of the proportion of a finite population with prescribed margin of error and confidence level. By characterizing the behavior of the coverage probability with respect to the proportion, we s ..."
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Cited by 10 (8 self)
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In this paper, we develop an exact method for the determination of the minimum sample size for the estimation of the proportion of a finite population with prescribed margin of error and confidence level. By characterizing the behavior of the coverage probability with respect to the proportion, we
MINSIZE2: a computer program for determining effect size and minimum sample size for statistical significance for univariate, multivariate, and nonparametric
, 1999
"... The American Psychological Association’s editorial style urges authors to provide effect size estimates. Several journals, including Educational and Psychological Measurement, have adopted author guidelines that call for determining the minimum sample size necessary for a given result to have been ..."
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Cited by 7 (0 self)
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The American Psychological Association’s editorial style urges authors to provide effect size estimates. Several journals, including Educational and Psychological Measurement, have adopted author guidelines that call for determining the minimum sample size necessary for a given result to have been
MINIMUM SAMPLE SIZES FOR CONFIDENCE INTERVALS FOR GINI’S MEAN DIFFERENCE: A NEW APPROACH FOR THEIR DETERMINATION
"... The sample mean difference ∆ ̂ is an unbiased estimator of Gini’s mean difference ∆. It is well known that ∆ ̂ is asymptotically normally distributed (Hoeffding, 1948). In order to obtain confidence intervals for ∆, ∆ ̂ must be standardized and hence its variance Var ( ∆ ̂ ) must be estimated. In th ..."
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. In this paper we study the effective coverage of the confidence intervals for ∆, when using a specific unbiased estimator)ˆ(arV ̂ ∆ for the variance of ∆ ̂ , in a nonparametric framework. The empirical determination of the minimum sample size required to reach a good approximation of the nominal coverage
TEACHING: ESTIMATION OF MINIMUM SAMPLE SIZE AND THE IMPACT OF EFFECT SIZE AND ALTERING THE TYPE I & II ERRORS ON IT, IN CLINICAL RESEARCH
"... In any research study, one of the most important questions asked for at the time of planning the study is ‘what should be the sample size in my study’. Estimation of minimum sample size depends upon design of the study, whether it is an estimation problem or a hypothesis testing problem, the type o ..."
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In any research study, one of the most important questions asked for at the time of planning the study is ‘what should be the sample size in my study’. Estimation of minimum sample size depends upon design of the study, whether it is an estimation problem or a hypothesis testing problem, the type
Compressive sampling
, 2006
"... Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired res ..."
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Cited by 1427 (15 self)
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Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
 STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is develop ..."
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Cited by 1032 (76 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework
Minimum Error Rate Training in Statistical Machine Translation
, 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
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Cited by 663 (7 self)
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Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality.
Evaluating the Accuracy of SamplingBased Approaches to the Calculation of Posterior Moments
 IN BAYESIAN STATISTICS
, 1992
"... Data augmentation and Gibbs sampling are two closely related, samplingbased approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accurac ..."
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Cited by 583 (14 self)
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Data augmentation and Gibbs sampling are two closely related, samplingbased approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
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Cited by 548 (13 self)
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For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
Results 1  10
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3,434,088