Inter-Rater Agreement under Stratified Random Sampling Scheme

Using stratified random sampling scheme, we discuss three estimators in estimating the finite population interquartile range (IQR) by using the known values of the 1st and 3rd quartiles of the auxiliary variable. Expressions for bias and MSE are derived up to first order of approximation. We propose an allocation procedure where a non-linear cost function is considered to minimize the mean square error (MSE) of the estimators for a specified cost. We use the real data set to compare the performances of estimators under proportional allocation and proposed allocation.

This paper presents the results of a limited investigation which brings into focus the difficulties encountered in developing exact distribution-free methods for stratified simple random sampling. It is shown that exact distribution-free confidence intervals for a population median can be obtained from a stratified sample under very special circumstances. The principal result concerns the order statistics of the combined separate strata samples, where proportional allocation is employed. It is proved that a pair of symmetric order statistics provides a confidence interval for the population median whose confidence coefficient is not less than the confidence coefficient obtained from the corresponding order statistics of a random sample of the same total size drawn from the entire population. Furthermore, it is shown by means of a numerical example that this result necessarily holds only when proportional allocation is employed.

Two types of stratified regression estimators for the population mean, the separate and the combined estimators, are investigated using stratified random sampling scheme (SSRS) and stratified ranked set sampling (SRSS). We derived mean and variance of the proposed estimators. In addition, we compared the performance of the regression estimators using SRSS with respect to SSRS by simulation. Our derivations and simulations revealed that our proposed estimators are unbiased and using SRSS is more efficient than using SSRS. The procedure are illustrated by using the bilirubin levels in babies in a neonatal intensive care unit data.

In this paper, we propose a calibration estimator of population mean in stratified sampling using the known mean and variance information from multi-auxiliary variables. The problem of determining the optimum calibrated weights is formulated as a Nonlinear Programming Problem (NLPP) that is solved using the Lagrange multiplier technique. Numerical example with real data is presented to illustrate the computational details of the proposed estimator. A comparison study is also carried out using real and simulated data to evaluate the performance and the usefulness of the proposed estimator. The study reveals that the proposed estimator with multi-auxiliary information is more efficient estimator of the population mean as it provides least estimated variance and highest gain in relative efficiency (RE). References Jean Claude Deville and Carl Erik Sarndal. Calibration estimators in survey sampling. Journal of the American statistical Association, 87(418):376–382, 1992. doi:10.1080/01621459.1992.10475217 . Victor M Estevao and Carl Erik Sarndal. Survey estimates by calibration on complex auxiliary information. International Statistical Review, 74(2):127–147, 2006. doi:110.1111/j.1751-5823.2006.tb00165.x Patrick J Farrell and Sarjinder Singh. Model-assisted higher-order calibration of estimators of variance. Australian and New Zealand Journal of Statistics, 47(3):375–383, 2005. doi:10.1111/j.1467-842X.2005.00402.x Wolfram Research, Inc. Mathematica, Version 11.3. Champaign, IL, 2018. Jong Min Kim, Engin A Sungur, and Tae Young Heo. Calibration approach estimators in stratified sampling. Statistics and probability letters, 77(1):99–103, 2007. doi:10.1016/j.spl.2006.05.015 Phillip S Kott. Using calibration weighting to adjust for nonresponse and coverage errors. Survey Methodology, 32(2):133, 2006. Dinesh K Rao. Mathematical programing in stratified random sampling. PhD thesis, School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Fiji, February 2017. Dinesh K. Rao, Tokaua. Tekabu, and Mohammad G M Khan. New calibration estimators in stratified sampling. In Proceedings of Asia-Pacific World Congress on Computer Science and Engineering, pages 66–70. IEEE, 2016. Gurmindar K Singh, Dinesh K Rao, and Mohammed GM Khan. Calibration estimator of population mean in stratified random sampling. In Proceedings of Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), pages 1–5. IEEE, 2014. Sarjindar Singh, Stephen Horn, and Frank Yu. Estimation of variance of general regression estimator: Higher level calibration approach. Survey Methodology, 24:41–50, 1998. Sarjinder Singh. Advanced Sampling Theory With Applications: How Michael Selected Amy, volume I and II. Kluwer Academic Publishers, Netherlands, 2003. Sarjinder Singh. On the calibration of design weights using a displacement function. Metrika, 75(1):85–107, 2012. doi:10.1007/s00184-010-0316-6 Sarjinder Singh, Stephen Horn, Sadeq Chowdhury, and Frank Yu. Theory and methods: Calibration of the estimators of variance. Australian and New Zealand Journal of Statistics, 41(2):199–212, 1999. doi:10.1111/1467-842X.00074 D S Tracy, S Singh, and R Arnab. Note on calibration in stratified and double sampling. Survey Methodology, 29(1):99–104, 2003. Changbao Wu and Randy R Sitter. A model-calibration approach to using complete auxiliary information from survey data. Journal of the American Statistical Association, 96(453):185–193, 2001. doi:10.1198/016214501750333054

Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery

In this article, a combined general family of estimators is proposed for estimating finite population mean of a sensitive variable in stratified random sampling with non-sensitive auxiliary variable based on randomized response technique. Under stratified random sampling without replacement scheme, the expression of bias and mean square error (MSE) up to the first-order approximations are derived. Theoretical and empirical results through a simulation study show that the proposed class of estimators is more efficient than the existing estimators, i.e., usual stratified random sample mean estimator, Sousa et al (2014) ratio and regression estimator of the sensitive variable in stratified sampling.

This paper modifies the Bahl and Tuteja exponential ratio-type estimator for population median under simple random and stratified sampling schemes using calibration weight adjustment technique with supplementary information to vary the stratum weights. The bias and mean square error of the modified estimator were obtained up to the second-order approximation, which satisfies the necessary conditions for efficiency. The findings show that the new estimator surpasses existing estimators in efficiency gain. This suggests the appropriateness of calibration weight modification in boosting the efficiency of a population parameter estimator under stratified random sampling especially where the population parameter of the auxiliary variable is known and correlate with the variable of interest.

Since 1967, when the Polish State Forests were first inventoried, a continuous increase in the volume of growing stock has been recorded. This increase in timber resources is mainly the result of sustainable forest management. However, during this time period inventory methods have been changed a few times and this may have affected the estimates of of wood resources. Since 2011 new instructions have been in place for forest management plans in the Polish State Forests. Despite this, the method of taking forest inventories remained consistent with the previous guidelines. This should allow us to consider changes in the volume of growing stock in relation to inventory methods and their accuracy. In this paper, the results of growing stock estimations based on two assessments made using stratified random sampling are compared. After five years of inventories made for forest management plans, 422 sample plots in two forest districts were measured within 15 strata. Predicted age classes structure at the beginning of successive management plans was used to determine new strata. Data from two inventories were compared on the basis of age class. In addition, data from the State Forests Information System i.e. updated stand level inventory data (on felling and tree growth) were analyzed. Comparing data from the two inventories, in three of seven age classes there was a decrease in the volume of growing stock after a five year period. We found no rational explanation for this. The extent of felling in the analyzed stand was very low, as confirmed by data from State Forests database. However, the largest decrease in volume was 1.5%, whereas the accuracy of growing stock assessment of discussed age class was estimated at some 9% in 2006 and 8% in 2011. Hence, from statistical viewpoint there was no differences between the results of both inventories. However, the differences between volume of growing stock of examined age classes, updated in the Information System of the State Forests and results of assessments by stratified sampling are less than 95% confidence intervals for means of samples. Because of that, updated information could be used in the long term management planning. The results clearly suggest that changes of growing stock, estimated as a difference between two assessments, should be interpreted in conjunction with the accuracy of these inventories. The accuracy of sampling should be also taken into account when creating management plans.

Social and ethnic differences in attendance for antenatal care in England

Medical profession, a competitive and ever-updating field which requires great commitment, imposes a stressful environment for students. Our study aimed to find the prevalence and factors associated with psychological illness. A cross-sectional analytical study was done among medical students in Puducherry. A stratified random sampling strategy was incorporated to achieve a calculated sample size of 384. The presence of anxiety and depression, perceived stress and addiction to the phone were assessed using the Hospital Anxiety and Depression Scale, Perceived Stress Scale, and Smartphone Addiction Scale- short version. Prevalence of anxiety and depression was summarized as a proportion with a 95 % confidence interval (CI). The prevalence ratio (PR) for the factors associated was estimated using log-binomial regression. With the response from 383 students, the prevalence of anxiety and depression was 39.4 % (95 %CI: 34.5-44.3 %) and 26.6 % (22.2-31.1 %). Perceived stress was moderate in 68.2 % and high in 14 % of students. Higher age (aPR=1.49), female gender (aPR=1.22), tobacco or alcohol use (aPR=1.24), smartphone addiction (aPR=2.09) and high stress (aPR=1.93) were the predictors of anxiety among medical students in our study. Use of tobacco or alcohol (aPR=2.07), smartphone addiction (aPR=1.96) and high stress (aPR=1.72) were the predictors of depression. Anxiety was more prevalent than depression among the medical students. Use of tobacco or alcohol, smartphone addiction and stress increase the risk of psychological morbidity. Medical training should have a module on coping mechanisms to overcome psychological illness and have better mental well-being.

Auxiliary variable is commonly used in survey sampling to improve the precision of estimates. Whenever there is auxiliary information available, the researchers want to utilize it in the method of estimation to obtain the most efficient estimator. Stratified simple random sampling (SSRS) is used in certain types of surveys because it combines the conceptual simplicity of simple random sampling (SRS) with potentially significant gains in efficiency. It is a convenient technique to use whenever we wish to ensure that our sample is representative of the, population and also to obtain separate estimates for parameters of each sub domain of the population. Stratified Ranked Set Sampling combines the advantages of Stratification and Ranked set sampling (RSS) to obtain an unbiased estimator for the population mean, with potentially significant gains in efficiency. Under Stratified ranked set sampling scheme, we have suggested two general estimators using power transformation to estimate the population mean of the study variable. These methods are highly beneficial to the estimation based on Stratified Simple Random Sampling (SSRS). The first order approximation to the bias and mean square error (MSE) of the proposed estimators are obtained. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in Stratified simple random sampling. A numerical illustration is also included to demonstrate the merits of the proposed estimator using SRSS over the corresponding estimators in SSRS.

Based on margin sampling (MS) strategy, an active learning approach was introduced for proposed sample selection from large quantities of labeled samples using a Landsat-7 ETM+ image to solve remote sensing image classification problems for large number of training samples. As a breakthrough from conventional random sampling and stratified systematic sampling methods, this approach ensures classification of only using a few hundred training samples to be as effective as that of using several thousand and even tens of thousands of samples by conventional methods, thereby avoiding enormous calculations, substantially reducing operating time and improving training efficiency. The test results of the proposed approach was compared with those of random sampling and stratified systematic sampling, and the effects of training samples on classification under optimized and non-optimized selection conditions was analyzed.

An improved ratio-type calibrated estimator was developed using the logarithmic mean in the calibration constraint for the stratified random sampling scheme. The proposed estimator was extended in the case of stratified double sampling and compared with the estimators given by Tracy et al. (2003) together its ratio-type estimators, as well as Nidhi et al. (2017) and Khare et al. (2022). A simulation study was also carried out on both a real and artificial dataset in order to evaluate the performance of the proposed estimator compared to the existing estimators.

We aimed to identify potential risk factors for recurrent respiratory tract infection among Chinese preschool-aged children, and further to construct a nomogram prediction model. This is a cross-sectional survey conducted in Beijing. Utilizing a stratified cluster random sampling strategy, a total of 7222 children from 20 kindergartens were enrolled. Data are analyzed by STATA software and R language. Five independent factors were identified to be significantly associated with recurrent respiratory tract infection risk overall and by pathogenic sites. The significant odds of recurrent respiratory tract infection was 8.31 (95% confidence interval [CI]: 5.69-12.12, P < 0.001), 2.31 (2.06-2.58, P < 0.001), 1.72 (1.48-1.99, P < 0.001), 1.24 (1.08-1.43, P = 0.002), and 1.19 (1.09-1.31, P < 0.001) for asthma, allergy, initial use of antibiotics <6 months, breastfeeding duration <6 months, and maternal body mass index, respectively. Besides the leading role played by asthma, allergy, initial use of antibiotics, and breastfeeding might exert a graded, dose-dependent effect on recurrent respiratory tract infection susceptibility. We have identified five potential risk factors for the risk of recurrent respiratory tract infection from 7222 preschool-aged Chinese children. Notably, asthma plays a leading role, and allergy, initial use of antibiotics, and breastfeeding might exert a graded, dose-dependent effect on recurrent respiratory tract infection susceptibility. This is the first report of examining the joint contribution of multiple potential risk factors to recurrent respiratory tract infection among Chinese preschool-aged children. We have identified five potential risk factors for the risk of recurrent respiratory tract infection via analyzing survey data from 7222 preschool-aged Chinese children. Asthma plays a leading role, and allergy, initial use of antibiotics, and breastfeeding might exert a graded, dose-dependent effect on recurrent respiratory tract infection susceptibility.

In stratified random sampling, Ahsan et al. [Mixed allocation in stratified sampling. Aligarh J Stat. 2005;25:87–97] introduced the concept of ‘Mixed allocation’ for fixed cost by minimizing the variance of the stratified sample mean which was based on the work of Clark and Steel [Optimum allocation of sample to strata and stages with simple additional constraints. Statistician. 2000;49(2):197–207] for two-stage univariate sampling design. The present manuscript studies the problem of obtaining a ‘compromise mixed allocation’ in multivariate stratified sampling using Fuzzy Programming Technique. To exhibit the application of the defined approach, a numerical illustration is considered and solved using Lingo 13.0. In order to demonstrate the superiority of proposed approach, the obtained results have been compared with four other existing approaches.