Judgment post‐stratification in finite mixture modeling: An example in estimating the prevalence of osteoporosis

Omidvar, Sedigheh; Jozani, Mohammad Jafari; Nematollahi, Nader

doi:10.1002/sim.7984

Cited by 21 publications

(16 citation statements)

References 28 publications

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“…The main approach in the aforementioned papers is either to average the ranking information in the sample or to smooth it through isotonic regression. Omidvar et al (2018) estimated the prevalence of osteoporosis using a JPS sample in a finite mixture model. Zamanzade and Wang (2017) constructed several estimators for the population proportion using a JPS sample.…”

Section: Introductionmentioning

confidence: 99%

Judgment Post-stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example

Öztürk

Kravchuk

2021

JABES

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This paper presents novel estimators for a judgment post-stratified (JPS) sample, which combine the ranking information from different methods or rankers. A JPS sample divides the units in the original simple random sample (SRS) into several ranking groups based on the relative positions (ranks) of the units in their individual small comparison sets. Ranks in the comparison sets may be assigned with several different ranking procedures. When considered separately, each ranking method leads to a different JPS sample estimator of the population mean or total. Here we introduce equally or unequally weighted estimators, which combine the ranking information from multiple sources. The unequal weights utilize the standard errors of the individual ranking methods estimators. The weighted estimators provide a substantial improvement over an SRS estimator and a JPS estimator based on a single ranking method. The new estimators are applied to crop establishment phenotypic data from an agricultural field experiment.Supplementary materials accompanying this paper appear online.

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Section: Introductionmentioning

confidence: 99%

Judgment Post-stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example

Öztürk

Kravchuk

2021

JABES

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“…They described three different sampling designs with varying degrees of without replacement samples and constructed non‐parametric confidence intervals for population quantiles. In infinite population settings, recent developments in JPS sampling designs can be found in Frey (2016), Omidvar, Jozani & Nematollahi (2018), Zamanzade & Wang (2017), and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Model‐based inference using judgement post‐stratified samples in finite populations

Öztürk

Kavlak

2021

Aus NZ J of Statistics

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Summary In survey sampling studies, statistical inference can be constructed either using design based randomisation or super population model. Design‐based inference using judgement post‐stratified (JPS) sampling is available in the literature. This paper develops statistical inference based on super population model in a finite population setting using JPS sampling design. For a JPS sample, first a simple random sample (SRS) is constructed without replacement. The sample units in this SRS are then stratified based on judgement ranking in a small comparison set to induce a data structure in the sample. The paper shows that the mean of a JPS sample is model unbiased and has smaller mean square prediction error (MSPE) than the MSPE of a simple random sample mean. Using an unbiased estimator of the MSPE, the paper also constructs prediction confidence interval for the population mean. A small‐scale empirical study shows that the JPS sample predictor performs better than an SRS predictor when the quality of ranking information in JPS sampling is not poor. The paper also shows that the coverage probabilities of prediction intervals are very close to the nominal coverage probability. Proposed inferential procedure is applied to a real data set obtained from an agricultural research farm.

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“…[5] focused on parametric inference from JPS samples. [14] estimated finite mixture models with JPS samples [21] studied the estimation problem of a binary population using JPS data. In the literature of the categorical ordinal variables, various research studied the estimation of the ordinal population from RSS samples.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Ordinal Populations from Judgment Post-Stratification

Alvandi¹,

Hatefi²

2021

Preprint

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In surveys requiring cost efficiency, such as medical research, measuring the variable of interest (e.g., disease status) is expensive and/or time-consuming; However, we often have access to easily attainable characteristics about sampling units. These characteristics are not typically employed in data collection process. Judgment post-stratification (JPS) sampling enables us to supplement the random samples from the population of interest with these characteristics as ranking information. In this paper, we develop methods based on JPS samples for the estimation of categorical ordinal populations. We develop various estimators from JPS data even for a situation that JPS suffers from empty strata. We also propose JPS estimators using multiple ranking resources. Through extensive numerical studies, we evaluate the performance of the methods in the estimation of the population. Finally, the developed estimation methods are applied to bone mineral data to estimate the bone disorder status of patients aged 50 and older.

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Judgment post‐stratification in finite mixture modeling: An example in estimating the prevalence of osteoporosis

Cited by 21 publications

References 28 publications

Judgment Post-stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example

Judgment Post-stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example

Model‐based inference using judgement post‐stratified samples in finite populations

Analysis of Ordinal Populations from Judgment Post-Stratification

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