When direct survey are about sensitive characteristics such as addiction to drugs, alcoholism, proneness to tax invasion and sexual violence, nonresponse bias and response bias become serious problems because people oftentimes do not wish to give tru...
The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or...
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...
One of the most useful indicators of relative dispersion is the coefficient of variation. The characteristics of the coefficient of variation have contributed to its widespread use in most scientific and academic disciplines, with real life applicati...
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 propose...
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 (GlobeL...
This paper proposes a generalized estimator of finite population variance using the auxiliary information under stratified random sampling. The expressions for bias and mean square error equations of the proposed estimator up to the first degree of a...
This research article addresses an efficient separate and combined class of estimators for the population mean estimation based on stratified random sampling (StRS). The first order approximated expressions of bias and mean square error of the propos...
To estimate the finite population variance of the study variable, this paper proposes an improved class of efficient estimators using different transformations. When both the minimum and maximum values of the auxiliary variable are known and the rank...
This pioneering investigation introduces two innovative estimators crafted to evaluate the finite population distribution function of a study variable, employing auxiliary variables within the framework of stratified random sampling and post-stratifi...
Data-driven flood susceptibility modeling is an efficient way to map the spatial distribution of flood likelihood. The quality of the flood susceptibility model relies on the learning technique and the data used for learning. The performance of learn...
Objectives: The design of cancer clinical trials incorporating biomarkers depends on various factors, including the trial phase, the type of biomarker and whether its role has been validated. This article aims to present a method for designing and an...
In survey sampling, the presence of non-response and measurement error often leads to biased and inefficient estimates, particularly in stratified random sampling designs. This study introduces a new calibration estimation technique for stratified sa...
In the modern age of data enrichment, it has become necessary to incorporate adaptive inference processes into survey-based estimation systems in order to achieve efficient and consistent population summaries. In this work, a new type of data-adaptiv...
The paper presents a methodology for analyzing time series of gene expression data collected from the leaves of potato virus Y (PVY) infected and non-infected potato plants, with the aim to identify significant differences between the two sets of pot...
The study is devoted to measuring the impact of the element changes on the bias and variance of the estimator of the total in a sample business survey. Stratified simple random sampling is usually used in business surveys. Enterprises may join, split...
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...
Thermal Infrared (TIR) remote sensing images of urban environments are increasingly available from airborne and satellite platforms. However, limited access to high-spatial resolution (H-res: ~1 m) TIR satellite images requires the use of TIR airborn...
This study assessed the accuracy of land cover change (2000–2018) maps compiled from Landsat images with either automated digital processing or with visual interpretation for a tropical forest area in Indonesia. The accuracy assessment used a two-sta...
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, ph...
Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful...
Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless,...
Urbanisation is a global trend rapidly transforming the biophysical and socioeconomic structures of metropolitan areas. To better understand (and perhaps control) these processes, more interdisciplinary research must be dedicated to the rural–urban i...
The ability to monitor sugarcane expansion in Brazil, the world’s largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of...
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 natio...
Monitoring environmental changes over time requires images with extensive historical depth. However, high spatial resolution images often lack such depth. This study investigates the impact of spatial resolution on image classification. Thus, Landsat...
11 October 2022
Health management and health education are two important tasks in the national basic public health service project with a wide audience, large service volume, and high accessibility. From 2009 to 2018, the Inner Mongolia Autonomous Region of China la...
The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving...
Validation data are often used to evaluate the performance of a trained neural network and used in the selection of a network deemed optimal for the task at-hand. Optimality is commonly assessed with a measure, such as overall classification accuracy...
The calibration method is used in stratified random sampling in the presence of measurement error to achieve optimum strata weights for better precision. In this study we attempt to analyze the behaviour of calibration estimators with different types...
17 April 2019
Background: Turkey passed a law banning smoking in all indoor public places in 2008. In response to the indoor smoking restriction, many smokers may have relocated to outdoor areas of venues. The aim of this study was to evaluate air pollution relate...
Shihmen Reservoir watershed is vital to the water supply in Northern Taiwan but the reservoir has been heavily impacted by sedimentation and soil erosion since 1964. The purpose of this study was to explore the capability of machine learning algorith...
Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their per...
High-quality training samples are essential for accurate land cover classification. Due to the difficulties in collecting a large number of training samples, it is of great significance to collect a high-quality sample dataset with a limited sample s...
Accurate and timely access to building rooftop information is very important for urban management. The era of big data brings new opportunities for rooftop extraction based on deep learning and high-resolution satellite imagery. However, collecting r...
The current study investigates the performance of various sampling designs in providing accurate estimates for crucial maize yield traits (intended for silage) including plant height, fresh/dry/ear weight, number of maize ears per plant, and total ea...
According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020...
High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classificatio...
The relationships between accountants’ environmental, social and governance (ESG) awareness and their perceptions of organisational risk are examined in this study. The emphasis is on the operational, strategic, financial and compliance risks o...
It is important to achieve estimates at the minimum cost, with no greater uncertainty than that which is appropriate for the objectives of the inventory. The aim of this study was to estimate the forest volume efficiently and accurately by sampling a...
Image Change Detection (ICD) methods are widely adopted to update large area land use/cover products. Uncertainty problems, however, are well known in such techniques, and a transparent assessment is necessary. In this study, a framework was proposed...
Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as relia...
Several reviews and meta-analyses have examined the effects of iodine on mental development. None focused on young children, so they were incomplete in summarizing the effects on this important age group. The current systematic review therefore exami...
This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, a...
Satellite and UAV-based remote sensing have been widely used for agricultural systems monitoring jointly. How to quantitatively optimize the efficiency of integrating these two techniques remains largely understudied. To address this gap, we, for the...
21 October 2016
Socioeconomic inequalities associated with multiple disabilities have not been explored in China. This is the first study to explore changes in multiple disabilities among persons aged 15–64 years in China. Data were derived from the 1987 and 2006 Ch...
In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process...
Field sampling is an important way of collecting soil information for the modeling and evaluation steps during digital soil mapping (DSM). However, some predesigned samples may not be accessible in the field due to natural or anthropogenic reasons. S...
With the rapid development of soil moisture estimation techniques involving remote sensing technology, the sampling designs used in soil moisture research are very important. To estimate the rational sample number for measuring near-surface soil mois...
This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial becaus...
of 18