Two Stage Inverse Adaptive Cluster Sampling With Stopping Rule Depends upon the Size of Cluster

Latpate, Raosaheb; Kshirsagar, Jayant

doi:10.1007/s13571-018-0177-y

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…ACS is relatively new and its theory is being developed [22,23,24,25,26] therefore the use of multi-auxiliary variables and different functions like log and exponential under different transformations have not been extensively studied to develop efficient estimators. Thus in this article we have combined all these ideas and proposed wider classes of estimators based on single and dual auxiliary variables for estimating the finite population mean of the survey variable so that the properties like bias and mean squared errors of various member estimators using several functions like log and exponential under different transformations would be known in advance.…”

Section: Introductionmentioning

confidence: 99%

Wider Classes of Estimators in Adaptive Cluster Sampling

Singh,

Mishra

2023

AJPAS

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Aims/ Objectives: Various efficient estimators using single and dual auxiliary variables with different functions including log and exponential have been developed in the SRSWOR design. Since the Adaptive cluster sampling (ACS) design is relatively new, estimators using functions like log and exponential with single and dual auxiliary variables have not been explored much. Therefore in this article, we propose two wider classes of estimators using single and dual auxiliary variables respectively so that the properties like bias and mean squared errors of various estimators using functions like log and exponential or any other function which belong to the proposed wider classes and have not been developed and studied yet would be known in advance. Formulae of the bias and mean squared error have been derived and presented. Further, since log type estimators have not been studied extensively in the ACS design we have developed new log type classes from each of the proposed wider classes and developed and studied some new log type member estimators. To examine the performance of these new developed log-type estimators over some competing estimators simulation studies have been conducted and all the estimators are further applied to a real data to estimate the average number of Mules in the Indian state of Assam. The studies show that the developed log-type estimators perform better.

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Section: Introductionmentioning

confidence: 99%

Wider Classes of Estimators in Adaptive Cluster Sampling

Singh,

Mishra

2023

AJPAS

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“…Such a variable is called an auxiliary variable. Researchers in sample surveys have been utilising auxiliary variables for a long time and many transformed estimators utilising them have been proposed (PRS INDIA, 2021 ; Acharya et al 2000 ; Latpate and Kshirsagar, 2020 ; WHO, 2022 ). Using a single auxiliary variable, one of the most widely used and efficient estimator is the ratio estimator.…”

Section: Introductionmentioning

confidence: 99%

“…Singh and Mishra ( 2022 ) proposed improved exponential ratio estimators for estimating cases of COVID-19 and studied its efficiency. Latpate and Kshirsagar ( 2020 ) proposed their two stage inverse ACS design.…”

Section: Introductionmentioning

confidence: 99%

Ratio-cum-product Type Estimators for Rare and Hidden Clustered Population

Singh

Mishra

2022

Sankhya B

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The use of multi-auxiliary variables helps in increasing the precision of the estimators, especially when the population is rare and hidden clustered. In this article, four ratio-cum-product type estimators have been proposed using two auxiliary variables under adaptive cluster sampling (ACS) design. The expressions of the mean square error (MSE) of the proposed ratio-cum-product type estimators have been derived up to the first order of approximation and presented along with their efficiency conditions with respect to the estimators presented in this article. The efficiency of the proposed estimators over similar existing estimators have been assessed on four different populations two of which are of the daily spread of COVID-19 cases. The proposed estimators performed better than the estimators presented in this article on all four populations indicating their wide applicability and precision.

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“…Mohammadi [ 9 ] has developed a bootstrap confidence intervals for inverse sampling. Latpate and Kshirsagar [ 10 ] introduced two-stage inverse adaptive cluster sampling with a stopping rule that depends on cluster size to control the final sample size.…”

Section: Introductionmentioning

confidence: 99%

Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations

Salehi

Smith

2021

PLoS ONE

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Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.

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Two Stage Inverse Adaptive Cluster Sampling With Stopping Rule Depends upon the Size of Cluster

Cited by 5 publications

References 15 publications

Wider Classes of Estimators in Adaptive Cluster Sampling

Wider Classes of Estimators in Adaptive Cluster Sampling

Ratio-cum-product Type Estimators for Rare and Hidden Clustered Population

Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations

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