Empirical likelihood


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Transforming the empirical likelihood towards better accuracy

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The maximum empirical likelihood point estimator is obtained by maximising the empirical likelihood function under a system of constraints. A single vector of weights, which can be used to estimate various parameters, is created.


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The confidence intervals are range-preserving and asymmetric, with the shape driven by the distribution of the data. In this thesis we focus on the extension and application of design-based empirical likelihood methods to various problems occurring in survey inference. First, a design-based empirical likelihood methodology for parameter estimation in two surveys context, in presence of alignment and benchmark constraints, is developed.

Second, a design-based empirical likelihood multiplicity adjusted estimator for multiple frame surveys is proposed. Third, design-based empirical likelihood is applied to a practical problem of census coverage estimation. We evaluate the asymptotic performance of the empirical likelihood estimators in a series of simulations on real and artificial data. We also discuss the computational aspects of the calculations necessary to obtain empirical likelihood point estimates and confidence intervals and propose a practical way to obtain empirical likelihood confidence intervals in situations when they might be difficult to obtain using standard approaches.

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Downloads from ePrints over the past year. Other digital versions may also be available to download e. This repository has been built using EPrints software , developed at the University of Southampton, but available to everyone to use. Empirical likelihood estimation and inference in a regression, accelerated failure time and instrumental variable model are currently under development.

DescStat endog.

Empirical likelihood - Wikipedia

DescStatUV endog. A class to compute confidence intervals and hypothesis tests involving mean, variance, kurtosis and skewness of a univariate random variable. DescStatMV endog.

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Important results are well motivated, discussed and illustrated by real data examples. R Posted Mar R function to compute empirical likelihood using a self-concordant convex criterion.

Thanks to Leo Belzile for spotting an error in the hpp function, which is fixed in the linked code. That did not affect computation in the region of the solution, but might have affected progress towards the solution. The input is a matrix of values and a vector of count where the i'th count is the number of times the i'th row of the matrix appears as data.

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It is meant to handle cases such as zero inflated counts so the user does not have to store all those zeros. Justin Grana's python code for descriptive statistics by empirical likelihood Justin Grana's python code for regression by empirical likelihood Justin Grana's python code for regression confidence intervals el. S Splus functions to calculate empirical likelihood for a vector mean. Free with no gaurantees.

Empirical Likelihood Inference with Public-Use Survey Data - Changbao Wu

Also outdated vs scel.

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