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In this article, we have derived a new calibration estimator of the population mean in the stratified random sampling under the presence of non response. The proposed calibration estimator has been obtained by making some improvements in the Hansen and Hurwitz (1946) estimator. For this purpose, we have gainfully utilized the information on a single auxiliary variable to obtain a set of new calibrated weights that increase the precision of the estimates. The proposed calibration estimator has been derived using the chi-square type distance function subject to some calibration constraints based on the auxiliary information. The Taylor linearization technique has been used to derive the expression for the variance of the proposed calibration estimator. We have also performed an empirical study based on the hypothetically generated data and real data to study the performance of the proposed calibration estimator. The proposed calibration estimator has been found more efficient than the usual Hansen and Hurwitz (1946) estimator and the recent calibration estimator proposed by Dykes et al. (2015). [ABSTRACT FROM AUTHOR]

Biodiversity assessment in fisheries plays a crucial role in conserving marine ecosystem diversity. Accurate survey data serve as the foundation for ensuring the precision of quantitative analysis and the effectiveness of resource conservation measures. However, conducting fishery resource surveys in the ocean is costly and constrained by on-site conditions. Therefore, meticulous sampling design is key to maximizing data quality and survey efficiency. While increasing the number of sampling stations can enhance result accuracy, it escalates survey costs and may adversely affect the marine environment and ecosystems. Hence, sampling design must balance sample size and precision to meet management objectives and budget constraints. Optimizing fishery resource survey sampling station design is widely recognized as an effective approach to enhance survey precision, and numerous studies have been conducted on this topic both domestically and internationally. Recent studies have indicated that stratified sampling offers high precision, making it a preferred choice for optimizing fishery resource survey site selection. However, most research primarily focuses on optimizing either stratification or sample allocation and pays less attention to methods that simultaneously optimize both stratification and sample allocation. This study aims to provide valuable insights into the simultaneous optimization of stratification and sample allocation for biodiversity-focused fisheries surveys.In this study, we take Maoming as a case study and employ the R package "SamplingStrata" to optimize the sampling design through stratified sampling under a multivariate scenario. This package, based on a genetic algorithm, can determine the optimal stratification, sample size, and sample allocation to meet precision constraints in the presence of multiple stratification variables and multiple target variables. When using this package, it is essential to clearly define the stratification and target variables while predefining the precision requirements for the target variables. To maximize the efficiency of the samples, the selection of stratification variables should be based on their correlation with the target variables. Choosing stratification variables that are best correlated with the target variables can enhance the representativeness of the samples. Additionally, the precision requirements are expressed using the coefficient of variation (CV) for each target variable. The CV value reflects the magnitude of the sample estimate variance relative to its mean for each target variable.In this study, we use environmental data (dissolved oxygen, water temperature, pH, and salinity) and water depth as stratification variables and define the Shannon–Wiener diversity index (H′), Margalef richness index (D), Pielou evenness index (J′), and species number as target variables. A maximum CV value is set for the target variables at 0.2, 0.15, 0.1, 0.05, 0.04, 0.03, 0.02, and 0.01. Subsequently, samples are selected from the optimized stratification design, and the relative errors are calculated.The results show that biodiversity in the coastal waters of Maoming exhibits seasonal differences, with diversity indices being higher in autumn than in spring. Additionally, the number of required sampling stations increases as the maximum CV decreases. However, when the maximum CV decreases below 0.05, the number of sampling stations increases significantly with each reduction of 0.01 CV, requiring one or more additional stations. At CV = 0.05, when stratified by environmental factors, spring conditions require eight sampling stations. In contrast, autumn and non-seasonal conditions require 7 and 11 sampling stations, respectively. When stratified by water depth, spring requires 12 sampling stations, whereas autumn and non-seasonal conditions require 14 and 21 sampling stations, respectively. According to relative error analysis, when stratified by environmental factors and water depth, the mean relative errors for H′, D, and J′ are 2.38, 2.02, 2.85, and 0.43, 3.14, and 1.74, respectively, with stratification by water depth resulting in smaller mean relative errors for all indices except J′.Through stratified sampling and appropriate sample allocation, reducing the number of required sampling stations while maintaining data accuracy, thus minimizing adverse impacts on marine ecosystems, is possible. In the coastal waters of Maoming, setting the maximum CV equal to 0.05 for the data precision requirement and using depth as the stratified variable are shown to be suitable considerations. The number of sampling stations for spring, autumn, and non-season-specific surveys is 12, 14, and 21, respectively. This study optimizes fisheries resource survey station selection in the coastal waters of Maoming, offering an effective sampling station optimization method for fisheries resource surveys and providing guidance for future fisheries resource survey station design.

AbstractIn the present study, we suggested a chi-square-type distance function that works well for non-response situations and we have derived a new calibration-based estimator using such a kind of distance function, which is used to estimate the mean of a stratified population. The proposed estimator is derived through the implementation of a reweighting technique referred to as the calibration approach, aimed at improving upon Hansen and Hurwitz’s (1946) estimator. Typically, in situations where some units do not respond, researchers adjust the stratum weights to obtain an estimator for the mean of a stratified population using the calibration approach. In this study, the weights given to the responding units in the sample and the units in the sub-sample taken from the non-responding sample were calibrated to derive the calibration estimator. We assume that the unit non-response problem impacts both study and auxiliary variables. To examine the performance of the proposed calibration estimator in scenarios involving non-response, we have carried out empirical studies. [ABSTRACT FROM AUTHOR]

This paper offers an improved memory-type ratio estimator in stratified random sampling under linear and non-linear cost functions. The issue is given as all integer non-linear programming problems (AINLPPs). The sampling properties mainly the bias and the mean squared error of the introduced estimator are derived up to the first order of approximation. The optimum value of the characterizing scalar is obtained by the Lagrange method of maxima–minima. The least value of the MSE of the suggested estimator is also obtained for this optimum value of the charactering constant. The suggested estimator is compared both theoretically and empirically with the competing estimators. Under this setup, the optimum allocation with mean square error of the suggested estimator is attained, and the estimator is compared to other comparable estimators. The AINLPP is solved using the genetic programming approach, which is applied to both actual and simulated data sets from a bivariate normal distribution. [ABSTRACT FROM AUTHOR]

BackgroundThe early years of life are critical for gross motor development (GMD). This study utilized decision tree modeling to examine the influences on gross motor development in children under the age of 3 years and to rank the key factors affecting their development.MethodsBased on randomized stratified sampling, 9,507 children aged 0–3 years in Shenzhen were included in this study. The Ages and Stages Questionnaires (ASQ) were utilized for the assessment of gross motor development. The chi-square test was used to compare groups, and variables were screened using univariable and multivariable regression analyses. Decision tree modeling was employed to rank the importance of statistically significant variables.ResultsThe research found a prevalence of gross motor developmental delay of 1.41% among the respondents. The accuracy of the decision tree model is 70.96%. The results demonstrated a strong correlation between seven variables affecting the gross motor development of children, which were ranked based on importance: age, whether to provide supplementary food, average time spent interacting with children, family type, feeding method, mode of delivery, and birth order.ConclusionThe risk of gross motor developmental delay increases with age. Furthermore, supplementary food and interacting with other children are critical factors in improving children’s GMD delay. It is therefore imperative to enhance the monitoring of children’s gross motor skills through regular developmental assessments that detect potential GMD delays. Moreover, family type, feeding method, mode of delivery, and birth order were also predictive factors of GMD delay.

In the realm of survey data analysis, encountering substantial variance relative to bias is a common occurrence. In this study, we present an innovative strategy to tackle this issue by introducing slightly biased variance estimators. These estimators incorporate a constant c within the range of 0 to 1, which is determined through the minimization of Mean Squared Error (MSE) for [Formula: see text]. This research builds upon the foundation laid by Kourouklis (2012, A new estimator of the variance based on minimizing mean squared error. The American Statistician, 66(4), 234–236) and extends their work into the domain of survey sampling. Extensive simulation studies are conducted to illustrate the superior performance of the adjusted variance estimators when compared to standard variance estimators, particularly in terms of MSE. These findings underscore the efficacy of our proposed approach in enhancing the precision of variance estimation within the context of survey data analysis.

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. [ABSTRACT FROM AUTHOR]

Baseline information about declining North American shorebird populations is essential to determine the effects of global warming at low-lying coastal areas of the Arctic and subarctic, where numerous taxa breed, and to assess population recovery throughout their range. We estimated population sizes on the Yukon–Kuskokwim Delta (YKD) in western Alaska on the eastern edge of the Bering Sea. We conducted ground-based surveys during 2015 and 2016 at 589 randomly selected plots from an area of 35,769 km2. We used stratified random sampling in 8 physiographic strata and corrected population estimates using detection ratios derived from double sampling on a subset of plots. We detected 11,110 breeding individuals of 21 taxa. Western Sandpiper (Calidris mauri), Red-necked Phalarope (Phalaropus lobatus), Dunlin (subspecies C. alpina pacifica), and Wilson's Snipe (Gallinago delicata) were the most abundant taxa. We estimated that ~7 million individual shorebirds were breeding on the entire YKD in 2015 and 2016. Our surveys of this region provided robust population estimates (coefficient of variations ≤ 0.35) for 14 species. Our results indicate that the YKD supports a large proportion of North America's breeding populations of the Pacific Golden-Plover (Pluvialis fulva), the western population of a Whimbrel subspecies (Numenius phaeopus hudsonicus), a Bar-tailed Godwit subspecies (Limosa lapponica baueri), Black Turnstone (Arenaria melanocephala), a Dunlin subspecies (C. alpina pacifica), and Western Sandpiper. Our study highlights the importance of breeding shorebirds of this relatively pristine but climatically sensitive deltaic system. Estuaries and deltaic systems worldwide are rapidly being degraded by anthropogenic activities. Our population estimates can be used to refine prior North American population estimates, determine the effects of global warming, and evaluate conservation success by measuring population change over time. [ABSTRACT FROM AUTHOR]

Estimating the rubber production in Thailand, the world’s leading rubber supplier, can help the Thai government to prepare for rubber cultivation in policy planning. A transformation technique can be used to improve the efficiency of estimating the average rubber yield by reducing the biases and mean square error. A group of population mean estimators has been suggested under stratified random sampling utilizing a transformed auxiliary variable. The biases and mean square errors of the proposed estimators are investigated. Simulation studies and an application to rubber production data in Thailand have been applied to assess their performances under stratified random sampling where the yield of rubber varies depending upon the region. The results show that the estimates of rubber yields with the proposed estimators had small biases and mean square errors. The best estimator gave an estimated rubber production of 1,140 kilogram/hectare, which is close to the population mean of the yields of rubber [ABSTRACT FROM AUTHOR]

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets. [ABSTRACT FROM AUTHOR]

In this research, a logarithmic-type estimator was formulated for estimating the finite population variance in stratified random sampling. By ensuring that the sampling process is symmetrically conducted across the population, biases can be minimized, and the sample is more likely to be representative of the population as a whole. We conducted a comprehensive numerical study and simulation study to evaluate the performance of the proposed estimator. The mean squared error values were computed for both our proposed estimator and several existing ones, including the standard unbiased variance estimator, difference-type estimator, and other considered estimators. The results of the numerical study and simulation study demonstrated that the proposed log-type estimator outperforms the other considered estimators in terms of MSE and percentage relative efficiency. Graphical representations of the results are also provided to illustrate the efficiency of the proposed estimator. Based on the findings of this study, we conclude that the proposed log-type estimator is a valuable addition to the existing literature on variance estimation in stratified random sampling. It provides a more efficient and accurate estimate of the population variance, which can be beneficial for various statistical applications. [ABSTRACT FROM AUTHOR]

In this paper, Randomized Response technique (RRT) is used to propose some separate and combined variance estimators for a sensitive variable using stratified random sampling. The performances of the proposed estimators are examined using a unifed measure of respondent privacy and estimator efficiency.

In the contemporary era of information technology, copious amounts of data are ubiquitous, generated across various sectors on a daily basis. Analyzing every unit of data is impractical due to constraints such as limited resources in terms of time, labor, and cost. In such scenarios, survey sampling becomes a recommended approach for extracting information about population parameters. The primary goal of this study is to devise an estimation method for acquiring information about population parameters. We propose an optimal estimator for an improved estimation of the population mean in stratified random sampling by leveraging the information from two auxiliary attributes. The proposed estimator's bias, mean squared error (MSE), and minimum mean squared error are determined up to the first-order approximation. It is demonstrated that, under the derived conditions, the proposed estimator theoretically outperforms existing estimators. Four population are utilized to evaluate both the performance and applicability of the proposed estimator. The percentage relative efficiency (PRE) of proposed estimator for all the populations is 178.389, 142.881, 181.383, and 152.679 respectively. The suggested estimator superior to existing estimators, as demonstrated by the numerical examples.

Litter decomposition represents a major path for atmospheric carbon influx into Arctic soils, thereby controlling below‐ground carbon accumulation. Yet, little is known about how tundra litter decomposition varies with microenvironmental conditions, hindering accurate projections of tundra soil carbon dynamics with future climate change. Over 14 months, we measured landscape‐scale decomposition of two contrasting standard litter types (Green tea and Rooibos tea) in 90 plots covering gradients of micro‐climate and ‐topography, vegetation cover and traits, and soil characteristics in Western Greenland. We used the tea bag index (TBI) protocol to estimate relative variation in litter mass loss, decomposition rate (k) and stabilisation factor (S) across space, and structural equation modelling (SEM) to identify relationships among environmental factors and decomposition. Contrasting our expectations, microenvironmental factors explained little of the observed variation in both litter mass loss, as well as k and S, suggesting that the variables included in our study were not the major controls of decomposer activity in the soil across the studied tundra landscape. We use these unexpected findings of our study combined with findings from the current literature to discuss future avenues for improving our understanding of the drivers of tundra decomposition and, ultimately, carbon cycling across the warming Arctic. [ABSTRACT FROM AUTHOR]

Historically, 23 freshwater mussel species have been documented from the Buffalo National River (BNR), a 246 km, free-flowing river in northern Arkansas. The potential threats to BNR include land use/land cover changes, eutrophication, recreation, physical habitat changes, and various climate change-related effects. Twelve quantitative mussel bed sites were established and then sampled using a stratified random sampling protocol to evaluate the long-term changes between 2006 and 2020–2021 in population and assemblage characteristics. We compared (1) overall mussel bed persistence, sampling confidence levels and study-wide relative abundances, and compared species' size and size-frequency distributions; (2) 10 overall site assemblage variables using paired t-tests; (3) site-level mean density, richness, and diversity indices using pair-wise Mann–Whitney U statistics; and (4) assemblage composition using Non-Metric Multidimensional Scaling. The major findings included the following: (1) sampling efforts based on a targeted 80% confidence level appears relatively robust, (2) BNR mussel assemblage composition and structure were relatively stable (however, small mussel bed persistence is a concern), (3) 7 of 23 sites were outliers based on freshwater mussel composition and habitat characteristics, and (4) assemblage composition changed with three species declining (Actinonaias ligamentina, Lasmigona costata, and Ptychobranchus occidentalis) and four species increasing (Cambarunio hesperus, Cyclonaias tuberculata, Eurynia dilatata, and Venustaconcha pleasii) between monitoring events. [ABSTRACT FROM AUTHOR]

With the development of remote sensing technology, a number of fine-resolution (30-m) global/national land cover (LC) products have been developed. However, accuracy assessments for the developed LC products are commonly conducted at global and national scales. Due to the limited availability of representative validation observations and reference data, knowledge relating to the accuracy and applicability of existing LC products on a regional scale is limited. Since Xinjiang, China, exhibits diverse surface cover and fragmented urban landscapes, existing LC products generally have high classification uncertainty in this region. This makes Xinjiang suitable for assessing the accuracy and consistency of exiting fine-resolution land cover products. In order to improve knowledge of the accuracy of existing fine-resolution LC products at the regional scale, Xinjiang province was selected as the case area. First, we employed an equal-area stratified random sampling approach with climate, population density, and landscape heterogeneity information as constraints, along with the hexagonal discrete global grid system (HDGGS) as basic sampling grids to develop a high-density land cover validation dataset for Xinjiang (HDLV-XJ) in 2020. This is the first publicly available regionally high-density validation dataset that can support analysis at a regional scale, comprising a total of 20,932 validation samples. Then, based on the generated HDLV-XJ dataset, the accuracies and consistency among three widely used 30-m LC products, GLC_FCS30, GlobeLand30, and CLCD, were quantitatively evaluated. The results indicated that the CLC_FCS30 exhibited the highest overall accuracy (88.10%) in Xinjiang, followed by GlobeLand30 (with an overall accuracy of 83.58%) and CLCD (81.57%). Moreover, through a comprehensive analysis of the relationship between different environmental conditions and land cover product performance, we found that GlobeLand30 performed best in regions with high landscape fragmentation, while GLC_FCS30 stood out as the most outstanding product in areas with uneven proportions of land cover types. Our study provides a novel insight into the suitability of these three widely-used LC products under various environmental conditions. The findings and dataset can provide valuable insights for the application of existing LC products in different environment conditions, offering insights into their accuracies and limitations. [ABSTRACT FROM AUTHOR]

This study focuses on standardizing sampling techniques and comparing various methods of sample allocation to effectively estimate apple area and production in the Himalayan region of India. We investigate different stratification tools,in formulating a sampling plan using information gathered from select orchardists in the locale during the 2016-17 period, it becomes essential to explore diverse methodologies to define the most suitable stratum boundaries, ascertain the requisite number of strata, and identify the optimal sample size. The stratification process, underpinned by the ''Area under Apple'' variable, which demonstrates a pronounced association with apple production, assumes a central role in this endeavor Several methods are utilized to construct strata, such as equalizing strata totals, cumulative equalization, equalization of ½{r(x) + f(x)} and equalization of f(x) . We assess their efficiencies in estimating total apple production in the study district. The combination of the ''Cum f(x)'' of Neyman allocation demonstrates the lowest variance and the highest efficiency within a range of 2–4 strata, coupled with an increase in sample size from 10 to 40. Consequently, it can be inferred that the ''Cum f(x)'' method, particularly with L > 2, is preferable for estimating apple production in the Himalayan region of India.

Recently, several memory-type mean estimators (including ratio, product, and logarithmic) have been developed. These estimators rely on exponentially weighted moving average (EWMA), which incorporate both historical and present sample data. In this article, we propose EWMA type calibrated estimators under single and double stratified random sampling (StRS). Because calibration method enhances the estimates by modifying the stratification weight, taking advantage of supplementary information. To evaluate the performance of estimators, various real-world time-scaled data sets pertaining to stock market and weather are taken into account. Additionally, we also conduct a simulation study using a bivariate symmetric data set. The numerical results show the superiority of proposed estimators (y¯TM,y¯TaM) over the adapted ones (y¯PM,y¯PaM).

The main objective of the current study is to suggest an enhanced family of log ratio-exponential type estimators for population distribution function (DF) using auxiliary information under stratified random sampling. Putting different choices in our suggested generalized class of estimators, we found some Specific estimators. The bias and MSE expressions of the estimators have been approximated up to the first order. By using the actual and simulated data sets, we measured the performance of estimators. Based on the results, the suggested estimators for DF show better performance as compared to the preliminary estimators considered here. The suggested estimators have a advanced efficiency than the other estimators examined with the estimators F‾ˆlogPR(st)2, and F‾ˆlogPR(st)4 for both the actual and simulated data sets. The magnitude of the improvement in efficiency is noteworthy, indicating the superiority of the proposed estimators in terms of MSE.

Historically, 23 freshwater mussel species have been documented from the Buffalo National River (BNR), a 246 km, free-flowing river in northern Arkansas. The potential threats to BNR include land use/land cover changes, eutrophication, recreation, physical habitat changes, and various climate change-related effects. Twelve quantitative mussel bed sites were established and then sampled using a stratified random sampling protocol to evaluate the long-term changes between 2006 and 2020–2021 in population and assemblage characteristics. We compared (1) overall mussel bed persistence, sampling confidence levels and study-wide relative abundances, and compared species’ size and size-frequency distributions; (2) 10 overall site assemblage variables using paired t-tests; (3) site-level mean density, richness, and diversity indices using pair-wise Mann–Whitney U statistics; and (4) assemblage composition using Non-Metric Multidimensional Scaling. The major findings included the following: (1) sampling efforts based on a targeted 80% confidence level appears relatively robust, (2) BNR mussel assemblage composition and structure were relatively stable (however, small mussel bed persistence is a concern), (3) 7 of 23 sites were outliers based on freshwater mussel composition and habitat characteristics, and (4) assemblage composition changed with three species declining (Actinonaias ligamentina, Lasmigona costata, and Ptychobranchus occidentalis) and four species increasing (Cambarunio hesperus, Cyclonaias tuberculata, Eurynia dilatata, and Venustaconcha pleasii) between monitoring events.

In today's world, by integrating remote sensing technology and modern state-of-the-art machine learning techniques, obtaining Land Use Land Cover (LULC) maps has become easier in comparison to traditional manual methods. The performance of a Machine Learning classifier is influenced by various factors. The objective of this study is to evaluate the impact of sampling design in rough complex terrain located in the Northern Himalayan region in Uttarakhand state, India, where reference data is often limited due to the geographical characteristics of the study area. Three sampling design strategies have been incorporated in this study, namely, stratified random sampling with a proportional number of samples (SRS)proportional, stratified random sampling with an equal number of samples (SRS)equivalent and stratified systematic sampling with an equal number of samples with a minimum distance of 10 m between the consecutive samples (SSS)D = 10 m for the LULC classification. In this study, Sentinel-2 data of 10 m spatial resolution for the study area of Dehradun district, Uttarakhand, India, has been selected. The following conclusions can be drawn from the results of this study (i) (SRS)proportional achieved the highest Overall Accuracy (OA) among all the three sampling techniques. The OA and kappa score (ka) using (SRS)proportional are OA = 90.25 and ka = 0.874 by Random Forest, OA = 88.84 and ka = 0.856 by Support Vector Machine and k Nearest Neighbours (kNN) obtained OA = 87.72 and ka = 0.842, respectively. (ii) It was found that in the case of (SRS)proportional, the majority classes like the deciduous forest, evergreen forest and cropland achieved higher recall and precision values in comparison to those obtained from the other two sampling strategies, i.e. (SRS)equivalent and (SSS)D = 10 m. (iii) The results showed that while switching from (SRS)proportional to (SRS)equivalent or from (SRS)proportional to (SSS)D = 10 m, there was a slight reduction in the precision and recall values for the majority classes and a slight increase for a few of the minority classes. [ABSTRACT FROM AUTHOR]

In this paper, we have described the development of an effective two-phase stratified random sampling estimation procedure in a scrambled response situation. Two different exponential, regression-type estimators were formed separately for different structures of two-phase stratified sampling schemes. We have studied the properties of the suggested strategy. The performance of the proposed strategy has been demonstrated through numerical evidence based on a data set of a natural population and a population generated through simulation studies. Taking into consideration the encouraging findings, suitable recommendations for survey statisticians are prepared for the application of the proposed strategy in real-life conditions. [ABSTRACT FROM AUTHOR]

This paper suggests a calibration ratio type estimator of the population mean in case of the stratified random sampling using a logarithmic mean as a calibration constraint. The result so obtained has been extended in case of stratified double sampling. The simulation study has also supported the performance of the suggested estimators over the existing estimators given by Singh et al. (1998) and Clement (2015) on the basis of two artificial data sets. [ABSTRACT FROM AUTHOR]

In this paper, two classes of estimators for the ratio of two population means utilizing auxiliary information in stratified random sampling in the presence of non-response with a known population mean of auxiliary variable have been proposed. For the suggested classes, the expressions for bias and mean square error have been obtained. The members of the suggested classes have been defined. With the help of a simulation study and a real data set, the comparison of the proposed classes of estimators with the relevant estimators has been explored which supports the proposed classes of estimators in comparison to the relevant estimators in terms of minimum mean square error (MSE) and high precision. [ABSTRACT FROM AUTHOR]

This article is an attempt to develop an effective stratified successive sampling estimation procedure which may cope with the problems of simultaneous presence of scrambled response and random non-response in realistic surveys. A generalized class of estimators has been suggested using imputation tool for reducing negative impacts of non-responses in sample surveys. Several estimators are presented as members of the suggested family. We have studied the properties of the suggested strategy and also derived its optimum condition. The dominance of the proposed estimator over the conventional ones has been justified through numerical illustrations carried out over the data set of natural population and population generated through simulation studies using wide varieties of distribution function. Encouraged with the outcomes of the proposed strategy, suitable recommendations to the survey statisticians are also made. [ABSTRACT FROM AUTHOR]

This paper emphasizes the significance of probability sampling in surveys; particularly, in the context of cross-sectional study design. It showcases pre-election survey results that illustrate its importance in producing trustworthy results. To attribute the survey results to the sampling technique, the survey operation design controls the different types of errors, including frame error, nonresponse error, measurement error, and specification error. Ultimately, after minimizing other sources of error, sampling is the sole remaining major source of error. In sample size determination, stratified random sampling is used, and the 'samplingbook' package in R Programming Software is used to compute the sample. This study concludes that appropriate sampling is essential in ensuring reliable survey results. [ABSTRACT FROM AUTHOR]

In this research, a logarithmic-type estimator was formulated for estimating the finite population variance in stratified random sampling. By ensuring that the sampling process is symmetrically conducted across the population, biases can be minimized, and the sample is more likely to be representative of the population as a whole. We conducted a comprehensive numerical study and simulation study to evaluate the performance of the proposed estimator. The mean squared error values were computed for both our proposed estimator and several existing ones, including the standard unbiased variance estimator, difference-type estimator, and other considered estimators. The results of the numerical study and simulation study demonstrated that the proposed log-type estimator outperforms the other considered estimators in terms of MSE and percentage relative efficiency. Graphical representations of the results are also provided to illustrate the efficiency of the proposed estimator. Based on the findings of this study, we conclude that the proposed log-type estimator is a valuable addition to the existing literature on variance estimation in stratified random sampling. It provides a more efficient and accurate estimate of the population variance, which can be beneficial for various statistical applications.

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets.

The National Land Cover Database (NLCD), a product suite produced through the MultiResolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. Starting from a base year of 2001, NLCD releases a land cover database every 2–3-years. The recent release of NLCD2019 extends the database to 18 years. We implemented a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only, and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only, and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User’s accuracies (UA) for forest loss and grass gain were>70% when agreement included either the primary or alternate label, and UA was generally70% for grass loss and gain and water gain and generally

In this article, we suggest an enhanced estimator for the estimation of finite population variance using twofold auxiliary variable under stratified random sampling. The numerical expressions for the bias and MSE are determined up to the first order of approximation. In order to effectively validate the theoretical findings, three actual data sets are included. Additionally, the application of the suggested estimators is demonstrated using a simulation study. Results of an empirical comparison among the suggested and existing estimators were investigated. To determine how good the suggested estimator, in comparison to the preliminary estimators, the MSE criterion is used. The suggested estimator has a smaller MSE and better PRE than existing estimators, according to numerical results utilizing actual data sets and a simulation analysis.

For maximum precision in population parameter estimation under the Stratified sampling design, the optimum strata boundaries (OSB) could be constructed based on a continuous study variable rather than a set of categorical variables. If constructed optimally, the OSB results in homogenous units within each stratum leading to optimal stratum sample sizes (OSS) as well. The OSB and OSS may not remain optimum if the problem is considered in terms of a fixed total sample size, especially when a survey design involves a fixed budget. This article suggests a methodology for computing the OSB and OSS when the per unit stratum measurement costs for the survey or its probability density function are known. To plan for such a stratified survey, we demonstrate a design-based stratification empirically by using Wave 18 of the HILDA Survey general release dataset where we estimate the mean level of Gamma-distributed annual total disposable income in Australia, which could potentially be an important variable for policy decision-making. We also provide numerical illustrations for hypothetical study variables that follow exponential and right-triangular distributions respectively. The findings indicate that the suggested method is satisfactory in the sense that it is either more efficient or relatively comparable with other methods aimed at improving the accuracy of population parameter estimates. The proposed technique has been implemented in the updated stratifyR package.

In this paper, we propose an efficient estimator for the estimation of population mean using the information of the two auxiliary attributes under stratified random sampling. The bias, mean squared error (MSE) and minimum mean squared error of the proposed estimator, up to the first order of approximation, have been derived. In theoretical comparison, the conditions have been deliberated under which the proposed estimator performs enhanced than the existing estimators. Numerical study has been demonstrated using four data sets to assess the performance as well as the applicability of all the estimators considered here. [ABSTRACT FROM AUTHOR]

Estimation of the population variance is of major concern in numerous examinations and ratio estimators are mainstream choices for it. It is a typical practice to utilize conventional central moments to construct variance estimators utilizing auxiliary information. However, conventional central moments give equal weight to each observation and hence highly affected by extreme values. For solving this issue, some recent developments are available in literature based on non-conventional/robust characteristics (inter-decile range, mid-range, etc.) of an auxiliary variable with conventional central moments under a simple random sampling scheme. This article initially, defines a family of ratio type variance estimators based on an adaptation of existing estimators with conventional central moments and non-conventional/robust characteristics of the auxiliary variable under a stratified random sampling scheme. After that, a novel family of variance estimators is proposed, in the presence of extreme values. The novel family of variance estimators is based on L-moments and calibration approach with some suitable constraints which can provide enhance the estimation of the population variance. We utilize L-moments characteristics (L-location, L-scale, and L-CV) for the purposes of the article. The variance expression of the novel family is also provided. Empirical illustrations are done using real and artificial data sets. The percentage relative efficiency is used for assessing the performances of adapted and novel families of estimators. [ABSTRACT FROM AUTHOR]

The purpose of this study was to study the relationship between pocket money and eating habits of students of the Culinary Study Program, Faculty of Engineering, Jakarta State University. The independent variable is pocket money and the dependent variable is the eating pattern of Catering students. This research was conducted online via the Google form application from August 2020 to January 2023. The method used in this study was a survey through a correlational study. The sample collection technique used simple random sampling, with a total of 112 students from the Culinary Study Program. The correlation coefficient is 0.156, meaning that it is included in the strong category, meaning that there is a relationship between pocket money and eating patterns in students of the Culinary Study Program, Faculty of Engineering, Jakarta State University. According to the results of the correlation test data, pocket money and diet have a value of 0.80, which means that the coefficient has a large or very strong relationship. Based on the results of the research, it is hoped that students of the Culinary Study Program will maintain a balanced diet so that they can fulfill a balanced health and nutrition [ABSTRACT FROM AUTHOR]

This article proposes two – stage stratified partial randomized response models based on Tracy and Osahan (1995) model and have large gain in precision. It is also shown that the proposed models are more efficient than Kim and Warde (2004) and Kim and Elam (2005) stratified randomized response models under the conditions presented in the case of completely truthful reporting and less than completely truthful reporting by the respondents. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

Quantile regression is one of the alternative regression techniques used when the assumptions of classical regression analysis are not met, and it estimates the values of the study variable in various quantiles of the distribution. This study proposes ratio-type estimators of a population mean using the information on quantile regression for stratified random sampling. The proposed ratio-type estimators are investigated with the help of the mean square error equations. Efficiency comparisons between the proposed estimators and classical estimators are presented in certain conditions. Under these obtained conditions, it is seen that the proposed estimators outperform the classical estimators. In addition, the theoretical results are supported by a real data application. [ABSTRACT FROM AUTHOR]

In this paper, we suggested generalized shrinkage regression, ratio and regression-cumratio estimators for population mean in multi-phase stratified systematic (MPSS) sampling design using multi-auxiliary information when information on all auxiliary variables is not available for population. The expressions of mean square error and bias are derived for suggested estimators. The extension of these estimators in bivariate and multivariate is also discussed and some important special cases are deduced from the general class. An empirical and simulation studies are conducted to assess the performance of proposed design and estimators and found suggested MPSS design perform better than estimators of multi-phase simple random sampling estimators. [ABSTRACT FROM AUTHOR]

The present study examines the effects of various communication styles and dimensions of diversity on employees' performance. The study was conducted through a survey of the paramedic staff in the hospitals of the National Capital Region (NCR) of India. A sample size of 690 paramedic staff was selected using a stratified random sampling technique. Four communication styles (Assertive Communication, Aggressive Communication, Passive Communication, and Passive-Aggressive Communication) and six dimensions of diversity (Age, Gender, Tenure, Educational Background, Work Experience, and Region) were used as independent variables, while employee performance was taken as the dependent variable. Multiple regression analysis using SEMAMOS was employed for data analysis and interpretation. The results of the study indicate a direct and positive impact of passive communication and assertive communication styles on employee performance. Additionally, the study reveals that gender, educational background, and tenure have a significant and positive relationship with employee performance. An assertive communication style along with age and region, has a greater impact on employee performance. [ABSTRACT FROM AUTHOR]

A stratified random sampling method is preferred for selecting varied populations with outliers. As opposed to plain random sampling, stratified sampling increases statistical precision by reducing estimator variance. Before reducing the estimator's variance, stratum boundary identification and data apportionment must be solved. In this study, a Neyman allocation strategy is used to address the stratum boundary determination issue in mixed populations. In addition to evaluating CSO on two groups of people, a comparison study was conducted using Kozak, GA, PSO, and Delanius and Hodge's approaches. Compared to previous algorithms, the numerical results indicate that the proposed technique can select the best-stratified boundaries for various standard populations and test functions. [ABSTRACT FROM AUTHOR]

Behaviour change interventions are fundamental to achieve sustainability in communities through the identification of new, and strengthening the existing positive practices. It is considered central to the quest for a sustainable future and solves multidimensional community problems, which require large scale shifts in human behaviour with regard to their health, socio-physical activities and long held habits. In this context, the study examines the role of integrated behaviour change interventions for sustainable community development by exploring the direct and mediating impacts through knowledge attitude and practice (KAP) of water, sanitation and hygiene (WASH). The primary data was collected from district Gujrat (Pakistan) through stratified random sampling to explore the impacts of KAP of WASH and its relationship with child health and sustainable community development. The hypotheses of the study were tested with smartPLS software. Findings of the overall sample of the study have revealed that integrated behaviour change interventions have significant impacts on KAP, WASH practices in daily life, child health (CH) and sustainable community development (SCD). Furthermore, the relationships among variables are positively significant and have direct and mediating impacts on sustainable community development. HIGHLIGHTS Integrated behaviour change interventions regarding water, sanitation, and hygiene for the improvement of child health and sustainable community development.; Knowledge, attitudes, and practices of mothers on a domestic level in daily life and its relationship with community development.; Water, sanitation, and hygiene work as mediation variables for achieving sustainable community development.;

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Over the past decades, benefiting from the development of computing capacity and the free access to Landsat and Sentinel imagery, several fine-resolution global land cover (GLC) products (with a resolution of 10 m or 30 m) have been developed (GlobeLand30, FROM-GLC30, GLC_FCS30, FROM-GLC10, European Space Agency (ESA) WorldCover and ESRI Land Cover). However, there is still a lack of consistency analysis or comprehensive accuracy assessment using a common validation dataset for these GLC products. In this study, a novel stratified random sampling GLC validation dataset (SRS_Val) containing 79,112 validation samples was developed using a visual interpretation method, significantly increasing the number of samples of heterogeneous regions and rare land-cover types. Then, we quantitatively assessed the accuracy of these six GLC products using the developed SRS_Val dataset at global and regional scales. The results reveal that ESA WorldCover achieved the highest overall accuracy (of 70.54% ± 9%) among the global 10 m land cover products, followed by FROM-GLC10 (68.95% ± 8%) and ESRI Land Cover (58.90% ± 7%) and that GLC_FCS30 had the best overall accuracy (of 72.55% ± 9%) among the global 30 m land cover datasets, followed by GlobeLand30 (69.96% ± 9%) and FROM-GLC30 (66.30% ± 8%). The mapping accuracy of the GLC products decreased significantly with the increased heterogeneity of landscapes, and all GLC products had poor mapping accuracies in countries with heterogeneous landscapes, such as some countries in Central and Southern Africa. Finally, we investigated the consistency of six GLC products from the perspective of area distributions and spatial patterns. It was found that the area consistencies between the five GLC products (except ESRI Land Cover) were greater than 85% and that the six GLC products showed large discrepancies in area consistency for grassland, shrubland, wetlands and bare land. In terms of spatial patterns, the totally inconsistent pixel proportions of the 10 m and 30 m GLC products were 23.58% and 14.12%, respectively, and these inconsistent pixels were mainly distributed in transition zones, complex terrains regions, heterogeneous landscapes, or mixed land-cover types. Therefore, the SRS_Val dataset well supports the quantitative evaluation of fine-resolution GLC products, and the assessment results provide users with quantitative metrics to select GLC products suitable for their needs. [ABSTRACT FROM AUTHOR]

Low teacher salaries have been prevalent since the mid-1900s, and teacher strikes, like the one that occurred in Chicago in 2019, have resulted in attempts to raise these salaries. This study analyzes whether raising teacher salaries could have benefits to other groups besides teachers themselves. Through a correlational analysis, the relationship between average teacher salaries and student performance on standardized tests (measured with SAT scores, math proficiency rates, and ELA proficiency rates) was examined among thirty randomly selected high schools in eight northern New Jersey counties. The findings show that average teacher salaries were positively correlated with all three metrics, but only those with SAT scores and ELA proficiency rates were significant correlations. These results do not suggest that raising teacher salaries will increase these metrics of student performance; they are solely an acknowledgment that such causation should be explored in order to properly inform policy on this issue.

The current investigation considers the query of assessment of estimators of population mean through calibration technique. We proposed new multi-variable calibrated estimator of mean in stratified sampling by employing the g multiple auxiliary variables. We introduce new variance function of the study variable in replacement to chi-square distance function under the assumption of known population variance of the study variable by some previous knowledge or past study as in case of Neyman allocation. It has been shown through simulation and numerical studies that the resultant estimators are much proficient than the usual combined mean estimator as well as combined ratio and regression estimators. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]