Estimation of the Proportion of Vectors in a Natural Population of Insects

Thompson, Keith H.

doi:10.2307/2527902

Cited by 98 publications

(112 citation statements)

References 3 publications

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“…Next, we compare our results with those of [4] The proposed model is more efficient than the Thompson estimation model for relatively higher p. However, the p for which the proposed model is more efficient than Thompson's model reduces as k increases. Therefore, the proposed model performs better in situations where p is relatively high which has been a drawback to the use of batch testing strategy.…”

Section: Asymptotic Variancementioning

confidence: 89%

“…That is, the estimator overestimates p as earlier noted by [4]. Since then the batch testing literature has continued to note this fact [20,21] or indicated how it can be derived by approximate expression using Taylor series expansion [22] or by help of Jensen's Inequality [13].…”

Section: Bias Of the Estimatormentioning

confidence: 97%

“…This might not be the case in practice as assumed by [4] and [21] among others and hence we invoke Monte Carlo simulations.…”

Section: Bias Of the Estimatormentioning

confidence: 99%

“…In all these applications, batch testing has two main objectives; first is the identification of positive individuals in a large population [1,2,3]. Second is estimation of the proportion of character or trait of interest [4,5,6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies in batch testing have concentrated on the second objective by generalizing [4] and [5] studies. The various scholars have done this by considering; unequal pool sizes [11,12], multistage pooling schemes [3,7,8], imperfect tests [9,13] and [10] introduced the idea of blockers.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Estimation of Proportion of a Trait by Batch Testing Model in a Quality Control Process

Wanyonyi¹

2015

AJTAS

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Batch testing involves testing items in a group rather than testing the items individually for resource saving purposes. Estimation of proportion of a trait of interest using batch testing model insulates individuals of a population against stigma. In this paper, an estimator of the unknown proportion of a trait in batch testing model based on a quality control process is constructed and its properties discussed. In quality control, a batch is rejected if constituent defective members are greater than l, the cut off value. It is observed that if l = 0, then the obvious batch testing strategy is obtained. Hence when l > 0, the batch testing strategy is generalized. The proposed model is superior to the existing models when the proportion of a trait is relatively high. The application of the model on Genetically Modified Organisms contamination is carried out.

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Section: Asymptotic Variancementioning

confidence: 89%

Section: Bias Of the Estimatormentioning

confidence: 97%

“…This might not be the case in practice as assumed by [4] and [21] among others and hence we invoke Monte Carlo simulations.…”

Section: Bias Of the Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Proportion of a Trait by Batch Testing Model in a Quality Control Process

Wanyonyi¹

2015

AJTAS

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Group Testing for Identification

Bilder

2019

Wiley StatsRef: Statistics Reference Online

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Grouping (pooling) items together and testing them as one unit for a binary trait is the process known concurrently as group testing and pooled testing. As long as trait prevalence is small, group testing can lead to significant reductions in the number of tests when compared to testing each item individually. The purpose of this article is to outline commonly used group‐testing algorithms and describe how to calculate their corresponding operating characteristics. Focus is on using group testing to identify whether an item has a particular trait rather than estimating the probability an item has this trait.

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Empirical Bayesian Estimation of the Disease Transmission Probability in Multiple‐Vector‐Transfer Designs

Bilder

Tebbs

2005

Biometrical J

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Plant disease is responsible for major losses in agriculture throughout the world. Diseases are often spread by insect organisms that transmit a bacterium, virus, or other pathogen. To assess disease epidemics, plant pathologists often use multiple-vector-transfers. In such contexts, groups of insect vectors are moved from an infected source to each of n test plants that will then be observed for developing symptoms of infection. The purpose of this paper is to present new estimators for p, the probability of pathogen transmission for an individual vector, motivated from an empirical Bayesian approach. We specifically investigate four such estimators, characterize their small-sample properties, and propose new credible intervals for p. These estimators remove the need to specify hyperparameters a priori and are shown to be easier to compute than the classical Bayes estimators proposed by Chaubey and Li (1995, Journal of Official Statistics 11, 1035-1046) and Chick (1996, Biometrics 52, 1055-1062). Furthermore, some of these estimators are shown to have better frequentist properties than the commonly used maximum likelihood estimator and to provide a smaller Bayes risk than the estimator proposed by Burrows (1987, Phytopathology 77, 363-365).

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Estimation of the Proportion of Vectors in a Natural Population of Insects

Cited by 98 publications

References 3 publications

Estimation of Proportion of a Trait by Batch Testing Model in a Quality Control Process

Estimation of Proportion of a Trait by Batch Testing Model in a Quality Control Process

Group Testing for Identification

Empirical Bayesian Estimation of the Disease Transmission Probability in Multiple‐Vector‐Transfer Designs

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