Two-phase Sampling Research Articles

New efficient class of estimators of population mean using two-phase sampling

New efficient class of estimators of population mean using two-phase sampling

Study of imputation based generalized classes of dual to product cum dual to ratio estimators for missing data in two-phase sampling

Study of imputation based generalized classes of dual to product cum dual to ratio estimators for missing data in two-phase sampling

EFFECTIVENESS OF TYPOLOGY AND LEARNING ENVIRONMENT IN DEVELOPING ENTREPRENEURIAL COMPETENCIES: A COMPARATIVE STUDY

The introduction of entrepreneurship course as a core module in higher education institutions (HEIs) under the Malaysia Education Blueprint (MEB) 2015–2025 has not increased entrepreneurship initiatives. This is partly attributed to ineffective implementation of entrepreneurship skills in the HEI curriculum. Although attempts were made to delineate the central phenomena of entrepreneurship learning to develop entrepreneurial competencies and intention, there was no consistent outcome. This comparative study used empirical data to investigate the contribution of multidisciplinary learning environment and the use of “Through” methodology as an entrepreneurial education pedagogy to develop entrepreneurial competencies and intention among students. A two-phase stratified single-stage cluster sampling approach was adopted that involved stratifying the entire population of 260 second-year degree students into learning environment strata and partitioning the strata into mutually exclusive entrepreneurial education pedagogy clusters. Data was collected from every single subject within the clusters. Descriptive analytical statistics was used in gauging the effectiveness of the research learning environment and typology pedagogy. The findings revealed that even though entrepreneurial intention remains a challenge, multidisciplinary learning environment that involves students from different disciplinary programs doing entrepreneurial ventures such as experiential learning and taking calculated business risks are most effective in inculcating entrepreneurial competencies. This study has shown that having the appropriate learning environment and typology pedagogy, including consideration of the psychological appeal of students are essential in nurturing entrepreneurial competencies among students. The outcome of this study provides a better understanding for both, HEI academicians and the Ministry of Higher Education (MOHE) in making entrepreneurship learning more relevant to produce students with entrepreneurial competencies.

Two Novel Algorithms for Low-Rank Matrix Completion Problem

Low-rank matrix completion has been exploited in many signal processing problems over the past few years. It has been shown that nuclear norm minimization can retrieve such low-rank matrices via uniform sampling under coherence conditions. However, in many applications such as the Netflix problem, the low-rank matrix is not coherent, hence nuclear norm minimization is unable to solve the problem because of uniform sampling. To overcome this issue, we propose two algorithms to recover both coherent and incoherent low-rank matrices. In the first algorithm, it is assumed that we have a limited sampling budget that is associated with the available number of samples. Using a portion of the sampling budget, we propose an Uncertainty Information (UI) metric to quantify the uncertainty that we have for each element and consider the elements with the most uncertainty as the most informative elements to sample. Finally, the simulations results are performed to investigate the performance of the proposed algorithms and show their superiority to conventional approaches such as nuclear norm minimization Candes and Recht, 2009 and two-phase sampling algorithm Chen et al. 2015.

Exponential ratio and product type estimators of the mean in stratified two-phase sampling

In this study, we propose exponential ratio estimators in the stratified two-phase sampling utilizing an auxiliary attribute. The expressions for the mean squared error of these exponential-type estimators under two different cases are derived and theoretical comparisons are made with competing estimators. We show that the proposed estimators have a lower mean square error than the simple mean estimator, usual stratified two-phase sampling ratio, and product estimators, usual exponential ratio and product estimators for the stratified two-phase sampling under the obtained conditions in theory. In addition, these theoretical results are supported with the aid of a numerical example.

Two-phase analysis and study design for survival models with error-prone exposures.

Increasingly, medical research is dependent on data collected for non-research purposes, such as electronic health records data. Health records data and other large databases can be prone to measurement error in key exposures, and unadjusted analyses of error-prone data can bias study results. Validating a subset of records is a cost-effective way of gaining information on the error structure, which in turn can be used to adjust analyses for this error and improve inference. We extend the mean score method for the two-phase analysis of discrete-time survival models, which uses the unvalidated covariates as auxiliary variables that act as surrogates for the unobserved true exposures. This method relies on a two-phase sampling design and an estimation approach that preserves the consistency of complete case regression parameter estimates in the validated subset, with increased precision leveraged from the auxiliary data. Furthermore, we develop optimal sampling strategies which minimize the variance of the mean score estimator for a target exposure under a fixed cost constraint. We consider the setting where an internal pilot is necessary for the optimal design so that the phase two sample is split into a pilot and an adaptive optimal sample. Through simulations and data example, we evaluate efficiency gains of the mean score estimator using the derived optimal validation design compared to balanced and simple random sampling for the phase two sample. We also empirically explore efficiency gains that the proposed discrete optimal design can provide for the Cox proportional hazards model in the setting of a continuous-time survival outcome.

Students Impression About Using Mobile Phones in Classroom

Mobile phones are widely used by university students and there is a controversial topic whether students should be able to use them freely during lectures. A survey was distributed seeking student opinions on using mobile phones in classroom. We used two-phase sampling method to reduce the nonresponse bias. There were 392 valid responses. To test the effect of different factors on students’ opinions a number of statistical techniques were utilized. The results showed Qatar University students marginally favored using mobile in classroom but using mobile phone distracted some students who are less in favor of using phone during lectures. The proportion of students who believe that student should decide to use or not use mobile in the classes is 0.635. Apparently, students expected implementation of a policy that controls the use of mobile phones in class. Recommendations include a blanket policy for the University on the use of mobile phones during class, mobile phone breaks given by lecturers during class, utilizing mobile phone as an education tool, with social media distractions blocked through the university system.

A general class of calibration estimators under stratified random sampling in presence of various kinds of non-sampling errors

This paper addresses the issue of estimating the population variance of a study character in the joint presence of random non-response and measurement errors and its application for estimating variations in biological data. Additional information on two highly positively correlated auxiliary variables has been incorporated to develop a general class of estimators under stratified two-phase sampling scheme. Its properties, in terms of bias and mean square error, have been examined. Optimum strata weights have been determined by employing suitable calibration techniques. Simulations using artificial data, as well as real data involving the variation in prostate specific antigen in different age groups when information about prostrate cancer volume and prostate weight is available, demonstrate the performance of the proposed class of estimators with respect to a contemporary estimator. Relevant R codes have been provided as Appendix.

Optimal two-phase sampling for estimating the area under the receiver operating characteristic curve.

Statistical methods are well developed for estimating the area under the receiver operating characteristic curve (AUC) based on a random sample where the gold standard is available for every subject in the sample, or a two-phase sample where the gold standard is ascertained only at the second phase for a subset of subjects sampled using fixed sampling probabilities. However, the methods based on a two-phase sample do not attempt to optimize the sampling probabilities to minimize the variance of AUC estimator. In this paper, we consider the optimal two-phase sampling design for evaluating the performance of an ordinal test in classifying disease status. We derived the analytic variance formula for the AUC estimator and used it to obtain the optimal sampling probabilities. The efficiency of the two-phase sampling under the optimal sampling probabilities (OA) is evaluated by a simulation study, which indicates that two-phase sampling under OA achieves a substantial amount of variance reduction with an over-sample of subjects with low and high ordinal levels, compared with two-phase sampling under proportional allocation (PA). Furthermore, in comparison with an one-phase random sampling, two-phase sampling under OA or PA have a clear advantage in reducing the variance of AUC estimator when the variance of diagnostic test results in the disease population is small relative to its counterpart in nondisease population. Finally, we applied the optimal two-phase sampling design to a real-world example to evaluate the performance of a questionnaire score in screening for childhood asthma.

New improved calibration estimator based on two auxiliary variables in stratified two-phase sampling

This paper considers the problem of estimating the population mean of the study variable when auxiliary information is not available and proposes new calibration approach alternative to the recent existing calibration estimators for estimating population mean of the study variable using two auxiliary variables in stratified two-phase sampling. The theory of new calibration estimation is given and optimum weights are derived under two-phase sampling approach. A simulation study is carried out to performance of the proposed calibration estimator with other existing calibration estimators. The results demonstrate that the proposed calibration estimator is more efficient than other existing calibration estimators of the population mean in stratified two-phase sampling.

Optimization of Two-Phase Sampling Designs With Application to Naturalistic Driving Studies

Naturalistic driving studies (NDS) generate tremendous amounts of traffic data and constitute an important component of modern traffic safety research. However, analysis of the entire NDS database is rarely feasible, as it often requires expensive and time-consuming annotations of video sequences. We describe how automatic measurements, readily available in an NDS database, may be utilized for selection of time segments for annotation that are most informative with regards to detection of potential associations between driving behavior and a consecutive safety critical event. The methodology is illustrated and evaluated on data from a large naturalistic driving study, showing that the use of optimized instance selection may reduce the number of segments that need to be annotated by as much as 50%, compared to simple random sampling.

Errors in multiple variables in human immunodeficiency virus (HIV) cohort and electronic health record data: statistical challenges and opportunities.

Objectives: Observational data derived from patient electronic health records (EHR) data are increasingly used for human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) research. There are challenges to using these data, in particular with regards to data quality; some are recognized, some unrecognized, and some recognized but ignored. There are great opportunities for the statistical community to improve inference by incorporating validation subsampling into analyses of EHR data.Methods: Methods to address measurement error, misclassification, and missing data are relevant, as are sampling designs such as two-phase sampling. However, many of the existing statistical methods for measurement error, for example, only address relatively simple settings, whereas the errors seen in these datasets span multiple variables (both predictors and outcomes), are correlated, and even affect who is included in the study.Results/Conclusion: We will discuss some preliminary methods in this area with a particular focus on time-to-event outcomes and outline areas of future research.

Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials.

The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. Despite this success, effective vaccines to prevent many high-burden diseases, including human immunodeficiency virus (HIV), have been slow to develop. Vaccine development can be aided by the identification of immune response markers that serve as effective surrogates for clinically significant infection or disease endpoints. However, measuring immune response marker activity is often costly, which has motivated the usage of two-phase sampling for immune response evaluation in clinical trials of preventive vaccines. In such trials, the measurement of immunological markers is performed on a subset of trial participants, where enrollment in this second phase is potentially contingent on the observed study outcome and other participant-level information. We propose nonparametric methodology for efficiently estimating a counterfactual parameter that quantifies the impact of a given immune response marker on the subsequent probability of infection. Along the way, we fill in theoretical gaps pertaining to the asymptotic behavior of nonparametric efficient estimators in the context of two-phase sampling, including a multiple robustness property enjoyed by our estimators. Techniques for constructing confidence intervals and hypothesis tests are presented, and an open source software implementation of the methodology, the txshift R package, is introduced. We illustrate the proposed techniques using data from a recent preventive HIV vaccine efficacytrial.

Seroconversion of hepatitis B surface antigen among those with previously successful immune response in Southern China

ABSTRACT Recommendations promoted worldwide have suggested a period of protection lasting more than 20 years against hepatitis B (HB) following primary immunization. Starting in 1987, universal HB vaccinations were carried out in Long An County, Guangxi Province, one of the earliest counties in which plasma-derived HB vaccine was delivered to newborns across China. Data collection targeted toward understanding the long term (26–33 years since primary immunization) immune effects of the plasma-derived HB vaccine was conducted in 2015; a second data collection was carried out in 2019 to assess seroconversion in the same cohort. This study qualitatively compared positive vs negative results and quantitatively assessed HB biomarkers – HB surface antigen (HBsAg), antibody to HBsAg (anti-HBs), HB e-antigen (HBeAg), antibody to HBeAg (anti-HBe), and antibody to HB core antigen (anti-HBc) – in serum 26–33 years after the full initial course of HB vaccination, then analyzed anti-HBs seroconversion using the two-phase sampling method in the same cohort and calculated the anti-HBs seroconversion rate from 2015 to 2019. The protective sero-conversion rate (anti-HBs ≥10mIU/mL) was 37.6% (192/511); the HBsAg-positive rate was 5.3% (27/511); the anti-HBs mean geometric titer (GMT) was 11.1 mIU/mL. Among the 143 participants involved in both 2015 and 2019 data collections, the seroconversion rate was 3.5% (5/143); two individuals had protective anti-HBs levels in 2015. These findings indicate that anti-HBs status can be seroconverted to a protective concentration level 4 years earlier in a high HBV epidemic region. The role of genomic mutations and the disappearance of immune memory and seroconversion should be investigated.

On improved class of difference type estimators for population median in survey sampling

We propose two new improved class of difference type estimators for population median under simple random sampling (SRS) and two-phase sampling (TPS). Expressions for bias and MSE are derived up to first order of approximation. We make a comparison of proposed estimators with all other commonly known estimators in literature. Numerical findings show that the proposed class of estimators perform better as compared to all other estimators considered here.

Mean estimation with generalized scrambling using two-phase sampling

Sousa et al. and Gupta et al. presented ratio and regression estimators of population mean of a sensitive variable using auxiliary information in simple random sampling without replacement. In this article, we propose a generalized randomized response technique (RRT) model and use it to develop some exponential estimators in two-phase sampling. We also discuss the privacy protection level of the proposed RRT model. Theoretical and empirical results are presented to examine the performance of the proposed mean estimators in relation to other mean estimators.

Effects of measurement and response errors on estimation based on centre sampling and two-phase centre sampling for elusive populations

Accurate survey data is important for planning and decision making. The presence of measurement and response errors in survey data can result in biased estimates as well as estimates with large standard errors, affecting the power of the statistical test to be carried out. It is important to investigate the effects of response errors when computing survey data so as to obtain reliable information for use by statisticians and policy makers. This paper investigates the effects of measurement and response errors in elusive surveys where the population has capture problems. The technique of centre sampling (CS) which entails collecting data on especially migrant populations is studied. In the technique, observations are made on those subjects who visit specified centres within the survey time period. This paper also investigates the technique of two-phase Centre Sampling (CS). A simulation study is carried out to establish the effect of response and measurement errors in centre sampling (CS) and two-phase Centre Sampling (CS). Using profiles at different levels of multiplicities, it was found that the errors affect the statistical estimates, even in small proportions to true values resulting in large estimation errors in both the mean and the variance. It is therefore recommended that both measurement and response errors be put into consideration when designing and carrying out a survey for more accurate results.

Some Exponential Type Predictive Estimators of Finite Population Mean in Two-Phase Sampling

This study is designed for predictive estimation of finite population mean in two phase sampling using two auxiliary variables. Two phase exponential ratio type estimator and exponential chain ratio type estimator are proposed under predictive approach suggested by Bahl & Tuteja (1991). The expressions of bias and mean square error of both suggested estimators have been carried out for theoretical comparison. The numerical study on real life data sets as well as simulation study has been conducted to examine the performance of suggested estimators. Finally, it is demonstrated that the suggested exponential ratio estimators are more efficient than competitive estimators in support of numerical and simulation study as well.

Probabilistic sampling and estimation for large-scale assessment of poplar plantations in Northern Italy

In the recent decades, growing demand for wood products, combined with efforts to conserve natural forests, has supported a steady increase in the global extent of planted forests. In this paper, a two-phase sampling strategy for large-scale assessment of hybrid poplar plantations in Northern Italy was implemented. The first phase was performed by means of tessellation stratified sampling on high-resolution remotely sensed imagery, covering the survey area by a grid of regular polygons of equal size and randomly and independently selecting one point per quadrat. All the plantations spotted by at least one sample point were selected. In the second phase, we randomly chosen a subset of plantations by stratified sampling that were visited on the ground to collect qualitative and quantitative attributes. The resulting estimates were reliable, and the survey demonstrated relatively easy to be implemented and replicated. These considerations support the use of the proposed sampling strategy to frequently update information on fast-growing forest plantations within agricultural farms, like hybrid poplar crops. Moreover, the results of the case study here presented highlight the relevance of hybrid poplar plantations in Italy, in the context of sustainable development strategies under a green economy perspective.

A modified efficient difference-type estimator for population mean under two-phase sampling design

A modified efficient difference-type estimator for population mean under two-phase sampling design