Bootstrapping probability-proportional-to-size samples via calibrated empirical population

Barbiero, Alessandro; Manzi, Giancarlo; Mecatti, Fulvia

doi:10.1080/00949655.2013.833204

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…Our proposed confidence interval adjustment is somewhat related to other methods of accounting for measurement error (Stram et al 1999;Barbiero and Manzi 2015;Buonaccorsi et al 2018). Most similar to our work is that of Szpiro and Paciorek (2014), who adjust for errors in inferences that are used for downstream epidemiological analysis.…”

Section: Quantification In Practicementioning

confidence: 90%

A bootstrapping approach to social media quantification

Daughton

Paul

2021

Soc. Netw. Anal. Min.

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This work considers the use of classifiers in a downstream aggregation task estimating class proportions, such as estimating the percentage of reviews for a movie with positive sentiment. We derive the bias and variance of the class proportion estimator when taking classification error into account to determine how to best trade off different error types when tuning a classifier for these tasks. Additionally, we propose a method for constructing confidence intervals that correctly adjusts for classification error when estimating these statistics. We conduct experiments on four document classification tasks comparing our methods to prior approaches across classifier thresholds, sample sizes, and label distributions. Prior approaches have focused on providing the most accurate point estimate while this work focuses on the creation of correct confidence intervals that appropriately account for classifier error. Compared to the prior approaches, our methods provide lower error and more accurate confidence intervals.

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Section: Quantification In Practicementioning

confidence: 90%

A bootstrapping approach to social media quantification

Daughton

Paul

2021

Soc. Netw. Anal. Min.

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“…We use the proposal of Barbiero et al. (2015) to calibrate those weights in order to better mimic certain characteristics of U . The idea is to define new weights

w_{C A L, i}^{*}

to be as close as possible to

π_{i}^{‐ 1}

, while satisfying the constraints that the induced pseudo‐population size equals the one of U , while the percentage of those anticipated to have a highly ranked tax gap remains unaffected in the pseudo‐bootstrap population.…”

Section: Maximising the Expected Tax Revenuementioning

confidence: 99%

“…In the case of unequal probabilities sampling, the pseudo-bootstrap population is often based on repeating each Step 1 sample unit w * HT,i = − 1 i times, appropriately approximated by an integer. We use the proposal of Barbiero et al (2015) to calibrate those weights in order to better mimic certain characteristics of U. The idea is to define new weights w * CAL,i to be as close as possible to − 1 i , while satisfying the constraints that the induced pseudo-population size equals the one of U, while the percentage of those anticipated to have a highly ranked tax gap remains unaffected in the pseudo-bootstrap population.…”

Section: Bootstrap For Unequal Probabilities Samplingmentioning

confidence: 99%

Sample Size Determination for Risk-Based Tax Auditing

Δελλαπόρτας

Ioannidis

Kotsogiannis

2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary A modern system of Revenue Administration requires an effective and efficient management of compliance which in turn requires a well‐designed taxpayers audit strategy. The selection of taxpayers to be audited by Revenue Authorities is a non‐standard sample size determination problem, involving an initial random sample from the population and, based on the statistical information derived from it, a risk‐based auditing scheme whose sole objective is to select for auditing the taxpayers with the highest estimated risk in the population. This paper provides a methodological approach that estimates the initial optimal random sample size such that the Revenue Administration Authority maximises their expected tax revenues. The methodology is illustrated using administrative data from the UK’s Revenue Authority, Her Majesty’s Revenue and Customs (HMRC).

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“…Barbiero and Mecatti (2010) consider two x-balanced methods, where inverses of first order inclusion probabilities are rounded down and additional pseudoelements are included in the pseudopopulation to reach the minimum absolute difference between total values of an auxiliary variable in the real population and the pseudopopulation. Barbiero, Manzi and Mecatti (2015) define k w as calibration weights rounded to the nearest integer. There are two possible limitations of the above algorithms.…”

Section: )mentioning

confidence: 99%

On the generalisation of Quatember’s bootstrap

Żądło

2021

Statistics in Transition New Series

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The problem of the estimation of the design-variance and the design-MSE of different estimators and predictors is considered. Bootstrap algorithms applicable to complex sampling designs are used. A generalisation of the bootstrap procedure studied by Quatember (2014) is proposed. In most of the cases considered in our simulation study it leads to more accurate estimates (or to very similar ones in remaining cases) of the design-MSE and the design-variance compared with the original algorithm and its other counteparts.

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Bootstrapping probability-proportional-to-size samples via calibrated empirical population

Cited by 7 publications

References 16 publications

A bootstrapping approach to social media quantification

A bootstrapping approach to social media quantification

Sample Size Determination for Risk-Based Tax Auditing

On the generalisation of Quatember’s bootstrap

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