Two-phase Sampling Research Articles

EFFICIENCY OF LOGARITHMIC RATIO CUM LOGARITHMIC PRODUCT ESTIMATORS IN DOUBLE SAMPLING USING IMPUTATION TECHNIQUES

In literature, there are many estimators of finite population mean, some of which are superior to the others. In practical situations, all the information on sample units may not be available due to non-response in sample surveys. Thus, our objective in this study is to get more precise estimators of the finite population mean of the study variable under two-phase sampling in case of missing data. Three new logarithmic ratio cum logarithmic product type imputation methods and corresponding point estimators have been introduced and observed to be better under two-phase sampling, which adds contribution to the field of imputation techniques. The bias and mean square errors of the proposed estimators are calculated in terms of population parameters. The performance of the proposed estimators is compared theoretically and empirically as well with existing traditional estimators.

Adaptive sampling with tensor leverage scores for exact low-rank third-order tensor completion

Tensor completion aims at estimating the missing entries from the incomplete observation. Under the tensor singular value decomposition framework, the exact recovery of a low-tubal-rank third-order tensor could be achieved via convex optimization with high probability if the tensor satisfies the tensor incoherence condition. In this work, we show that, when the random selection of entries is made adaptive to a distribution which is dependent on the coherence structure of the tensor, any low-tubal-rank tensor of the size n×n×n with tubal-rank r can be exactly recovered with high probability from as few as O(rn2log2⁡(n)) randomly chosen entries. In practice, tensor leverage scores are not known a priori, and we design a two-phase adaptive sampling strategy to obtain the leverage scores. Numerical experiments on synthetic and real-world third-order tensor data sets are used to validate our theoretical results and illustrate that the tensor recovery performance of the proposed two-phase adaptive sampling scheme is better than that of the other state-of-the-art methods.

New Techniques for Estimating Finite Population Variance Using Ranks of Auxiliary Variable in Two-Stage Sampling

This article presents a new set of estimators designed to estimate the finite population variance of a study variable in two-phase sampling. These estimators utilize the information about extreme values and ranks of an auxiliary variable. Through a first-order approximation, we investigate the properties of these estimators, including biases and mean squared errors (MSEs). Furthermore, a comprehensive simulation study is conducted to assess their performance and validate our theoretical insights. Results demonstrate that our proposed class of estimators performs better in terms of percent relative efficiency (PRE) across various simulation scenarios compared to existing estimators. In addition, in the application section, we utilize three data sets to further validate the performance of our proposed estimators against conventional unbiased variance estimators, ratio and regression estimators, as well as other existing methods.

PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK.

Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit patients selectively, which may improve efficiency in downstream analyses. In this paper we propose a two-phase sampling design that leverages available information on auxiliary covariates in EHR data. A key challenge in using EHR data for multiphase sampling is the potential selection bias, because EHR data are not necessarily representative of the target population. Extending existing literature on two-phase sampling design, we derive an optimal two-phase sampling method that improves efficiency over random sampling while accounting for the potential selection bias in EHR data. We demonstrate the efficiency gain from our sampling design via simulation studies and an application evaluating the prevalence of hypertension among U.S. adults leveraging data from the Michigan Genomics Initiative, a longitudinal biorepository in Michigan Medicine.

Enhancing Precision in Population Variance Vector Estimation: A Two-Phase Sampling Approach with Multi-Auxiliary Information

To enhance precision in estimating unknown population parameters, an auxiliary variable is often used. However, in scenarios where required information on an auxiliary variable is partially or fully unavailable, two-phase sampling is commonly employed. The challenge of estimating the variance vector using multi-auxiliary variables is a less explored area in current literature. This paper addresses the estimation of vector of unknown population variances for multiple study variables by using an estimated vector of variances derived from multi-auxiliary information. This approach is particularly relevant when population variances for the multi-auxiliary variables are not known prior to the survey. The paper introduces a generalized variance and a vector of biases for the proposed multivariate estimator. Special cases of the proposed multivariate variance estimator are provided, accompanied by expressions for mean square errors. Theoretical mathematical conditions are discussed to guide the preference for the proposed estimator. Through the analysis of real-world application-based data, the applicability and efficiency of the proposed multivariate variance estimator are demonstrated, outperforming modified versions of multivariate variance estimators. Additionally, a simulation study validates the superior performance of the proposed estimator compared to its modified estimators.

Assessing Etiologic Heterogeneity for Multinomial Outcome with Two-Phase Outcome-Dependent Sampling Design.

Etiologic heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily appropriately adjust for these sources of bias, or allow for formal comparisons of effects across different subtypes. Herein, using inverse probability weighting (IPW) to fit a multinomial model is shown to yield valid inference with this sampling design for subtype-specific exposure effects and contrasts thereof. The IPW approach is compared to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study.

Hey Siri! does he love me? Unveiling the phenomena of emotional detachmentamong seafarers

Purpose The challenges seafarers face are unique due to the nature of their work. Individuals in this profession face unique stressors such as limited interaction with people, isolation from society, confined workspaces, unpredictable weather conditions and exposure to hazardous situations. Quantitative methods used in studies on seafarers’ well-being fail to capture the individual experiences, emotions and motivations that make up their rich tapestry. The prevailing reliance on close-ended questionnaires is a hindrance to seafarers’ expression of subjective experiences and difficulties. It is essential to prioritize their well-being, both from a humanitarian standpoint and an economic perspective. Design/methodology/approach For this study, the authors coted qualitative data using open-ended semi-structured questionnaires from 35 (35) merchant navy sailors. The drive of the study was clearly stated to the respondents by the researchers. The study focuses on the emotional detachment of seafarers and follows a two-phase sampling process: criterion sampling and snowball sampling. The sampling methods are chosen in accordance with the purpose of the study. Findings The in-depth qualitative inquiry and detailed analysis of interview excerpts generated three broad themes that explain the occurrence of emotional detachment among seafarers. The qualitative study advances the knowledge base of the intricate phenomena of mariner’s emotional detachment. The themes illuminate the causes and outcomes of emotional detachment, also necessitating the need for intervention. Originality/value This study highlights the issue of seafarers at work, which tends to affect their personal and professional lives. The lived experiences of seafarers help us understand the issue of emotional detachment from a better and more in-depth perspective. This certainly helps companies and policymakers customize their interventions to seafarers’ needs.

Optimal two-phase sampling for comparing correlated areas under the ROC curves of two screening tests in the presence of verification bias

ABSTRACT The accuracy of a screening test is often measured by the area under the receiver characteristic (ROC) curve (AUC) of a screening test. Two-phase designs have been widely used in diagnostic studies for estimating one single AUC and comparing two AUCs where the screening test results are measured for a large sample (Phase one sample) while the disease status is only verified for a subset of Phase one sample (Phase two sample) by a gold standard. In this paper, we consider the optimal two-phase sampling design for comparing the performance of two ordinal screening tests in classifying disease status. Specifically, we derive an analytical variance formula for the AUC difference estimator and use it to find the optimal sampling probabilities that minimize the variance formula for the AUC difference estimator. According to the proposed optimal two-phase design, the strata with the levels of two tests far apart from each other should be over-sampled while the strata with the levels of two tests close to each other should be under-sampled. Simulation results indicate that two-phase sampling under optimal allocation (OA) achieves a substantial amount of variance reduction, compared with two-phase sampling under proportional allocation (PA). Furthermore, in comparison with a one-phase random sampling, two-phase sampling under OA or PA has a clear advantage in reducing the variance of AUC difference estimator when the variances of the two screening test results in the disease population differ greatly from their counterparts in non-disease population.

Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia

To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R2 = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R2 = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands.

A stratified estimation for sensitive variable using correlated scrambling variables

In this article, when the population is composed of several strata, we deal with the problem of stratified estimation for sensitive variables by applying stratified sampling to Murtaza et al.’s model using correlated scrambling variables. When the size of each stratum is accurately known, the sensitive variable is estimated by stratification, and the proportional and optimal allocations are examined as a method of allocating samples to each stratum. Also, in the case of not knowing the size of each stratum, a sensitive variable is estimated by using two-phase sampling, and the method of allocating samples to each stratum is also examined. Also, the efficiency between the proposed stratified model of Murtaza et al.’s and the existing model of Murtaza et al.’s is compared.

Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used “robust” variance estimate of Barlow (Biometrics 50:1064–1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.

Efficient generalized estimators of population mean in the presence of non-response and measurement error

Researchers frequently encounter significant challenges in sampling surveys, particularly related to non-response and measurement errors. These challenges pose substantial obstacles that can undermine the precision and dependability of survey outcomes. In this study, we introduced population mean estimators that explicitly address non-response and measurement error, employing both single-phase and two-phase sampling techniques. We derived the bias and mean square error expressions of the proposed estimators up to the first order of approximation. To evaluate the efficiency of the proposed estimators tS1 and tS2, the percent relative efficiencies have been computed. The percent relative efficiency results of population 1 in single-phase sampling for the proposed estimator are 612.258, 610.837, and 609.061, while for two-phase sampling, the results are 576.823, 581.090, and 586.541. These results show the superior performance of the proposed estimators when compared to tSP, tMA1, tMS and tTB, as reflected in Tables 4 and 5.

Addressing age measurement errors in fish growth estimation from length-stratified samples.

Fish growth models are crucial for fisheries stock assessments and are commonly estimated using fish length-at-age data. This data is widely collected using length-stratified age sampling (LSAS), a cost-effective two-phase response-selective sampling method. The data may contain age measurement errors (MEs). We propose a methodology that accounts for both LSAS and age MEs to accurately estimate fish growth. The proposed methods use empirical proportion likelihood methodology for LSAS and the structural errors in variables methodology for age MEs. We provide a measure of uncertainty for parameter estimates and standardized residuals for model validation. To model the age distribution, we employ a continuation ratio-logit model that is consistent with the random nature of the true age distribution. We also apply a discretization approach for age and length distributions, which significantly improves computational efficiency and is consistent with the discrete age and length data typically encountered in practice. Our simulation study shows that neglecting age MEs can lead to significant bias in growth estimation, even with small but non-negligible age MEs. However, our new approach performs well regardless of the magnitude of age MEs and accurately estimates SEs of parameter estimators. Real data analysis demonstrates the effectiveness of the proposed model validation device. Computer codes to implement the methodology are provided.

Estimation of a Population Total Under Nonresponse Using Follow-up

Abstract In this article, we propose methods to minimize bias due to unit nonresponse. We consider a two-phase sampling design where the second phase is a probability subsample of nonrespondents from the first phase. In this context, we propose three weighting procedures to estimate the total when not all units in the subsample respond. The weighting is based on the response homogeneity group (RHG) model. Given the RHG model, theoretical results on bias and variance estimation are obtained for all estimators. In a simulation study, we evaluate the empirical properties of the three estimators as well as of estimators based on two commonly used procedures to handle unit nonresponse in single-phase sampling design. These two procedures include: (i) nonresponse calibration weighting, also known as the one-step approach, and (ii) nonresponse probability weighting followed by calibration, also known as the two-step approach. Our results indicate that when there is significant deviation from the assumed RHG model, the nonresponse follow-up estimators perform better in terms of bias and coverage.

A chain ratio-type exponential estimator for population mean in double sampling

In this paper we have proposed an efficient ratio-type exponential estimator for estimating the population mean of the study variable, by incorporating two auxiliary variables in twophase (double) sampling. The bias and the mean square error of the proposed estimator have been obtained up to the first order of approximation. The newly proposed estimator offers more precision in comparison to other competing estimators, theoretically as well as empirically, by considering a known value of some population parameter.

Two-Phase Adaptive Kriging Model Based Importance Sampling Method for Estimating Time-Dependent Failure Probability

To improve the computational efficiency of the time-dependent failure probability (TDFP) to system with dynamic loads, materials degeneration and other time-varying factors, a two-phase adaptive Kriging model-based importance sampling (IS) method is proposed. The main contribution of this article is that a single Kriging model is established and adaptively refined to gradually approach the optimal IS probability density function (PDF) and estimate TDFP in two sequential phases. In the first phase, a generally approximate IS PDF is constructed by Kriging model based on its prediction characteristic and subsequently updated until convergence. Then, a simple rejection sampling technique is used to robustly generate IS samples for the estimation of TDFP. In the second phase, current Kriging model is subsequently refined for accurately identifying the classification of the IS samples generated in the last phase. Finally, the TDFP can be estimated using the products obtained in the above two phases. As the IS PDF established by Kriging model is used for directly approximating the optimal one, the proposed method has the capacity to deal with the complicated problem containing multiple failure regions. Additionally, since IS procedure can dramatically improve the sampling efficiency, the proposed method is an effective technique to the time-consuming TDFP analysis problems.

A general class of improved population variance estimators under non-sampling errors using calibrated weights in stratified sampling.

This paper proposes a new calibration estimator for population variance within a stratified two-phase sampling design. It takes into account random non-response and measurement errors, specifically applying this method to estimate the variance in Gas turbine exhaust pressure data. The study integrates additional information from two highly positively correlated auxiliary variables to develop a general class of estimators tailored for the stratified two-phase sampling scheme. The properties of these estimators, in terms of their biases and mean square errors, have been thoroughly examined and extensively analyzed through numerical and simulation studies. Furthermore, the calibrated weights of the strata are derived. The proposed estimators outperform the natural estimator of population variance. Finally, suitable recommendations have been made for survey statisticians intending to apply these findings to real-life problems.

Estimating A Finite Population Mean Under Two-Phase Sampling Using Exponential Ratio Estimator

Estimation of population parameters such as population mean, and population total has been a major concern in Sample Survey Theory. In sampling theory, researchers employ auxiliary information to improve precision and validity of estimators. This study applies two-phase sampling in estimation of a finite population mean using modified exponential ratio estimator. Two-phase sampling was used since it leverages on the information collected in the first phase, along with auxiliary variables, to guide the selection of a more targeted and efficient second-phase sample, resulting in increased precision for the estimates of interest. Study results showed that the proposed modified exponential ratio estimator produced a smaller Bias and MSE than Shabbir and Gupta (2007), and Singh and Solanki (2013). Further, the proposed estimator produced a higher relative efficiency as compared to Shabbir and Gupta (2007), and Singh and Solanki (2013).

Calibration-based approach in estimation of population mean under stratified two-phase sampling scheme

Calibration-based approach in estimation of population mean under stratified two-phase sampling scheme

Quantify the Impact of Non-Response and Measurement Error of Sensitive Variable(s) under Two-Phase Sampling employing ORRT Models

Throughout this article, a two-phase sampling (TPS) technique is employed to estimate the population mean of the sensitive variable. The current article endeavours to develop a chain ratio type estimator for the estimation of sensitive variable(s) in the presence of non-response and measurement error simultaneously by utilizing ORRT models under a two-phase sampling technique. The significant aspects associated with the suggested estimator characterized by bias and mean squared error have been evaluated. Besides this, the utterance for the minimum mean squared error for the optimal values has also been identified. The supremacy of the proposed estimator has been compared with the modified existing estimators under the TPS scheme by using two sensitive auxiliary variables. To clarify the theoretical findings, a simulation study along with a hypothetical generated population and a real population which is based on abortion rates from Statistical Abstract of the United States: 2011 are also addressed in this study.