Article
Statistics & Probability
Shashi Bhushan, Anoop Kumar, Saurabh Singh
Summary: This paper proposes efficient combined and separate classes of estimators for estimating population mean under stratified simple random sampling. The theoretical justification and simulation study demonstrate the superiority of the proposed estimators.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Mathematics, Applied
Tuba Koc, Haydar Koc
Summary: This study proposes ratio-type estimators of a population mean using the information on quantile regression for stratified random sampling, and the efficiency comparisons between the proposed estimators and classical estimators are presented. The results show that the proposed estimators outperform the classical estimators, and this finding is supported by a real data application.
Article
Social Sciences, Mathematical Methods
Zawar Hussain, Salman Arif Cheema, Ishtiaq Hussain
Summary: This article discusses the correction and improvement in the Tarray, Singh, and Zaizai model, particularly when using stratified random sampling. The suggested model combines the Mangat and Singh, Mangat, and Greenberg et al. models and incorporates optional randomized response technique. Numerical results demonstrate higher efficiency compared to the Kim and Warde models as well as the aforementioned models.
SOCIOLOGICAL METHODS & RESEARCH
(2022)
Article
Demography
Tolga Zaman
Summary: Population stratification aims to increase the precision of estimation, using an efficient exponential ratio estimator to estimate the population mean in stratified random sampling can reduce bias and mean square error. The proposed estimators perform more efficiently under stratified random sampling, with lower mean square error compared to ratio and exponential estimators.
MATHEMATICAL POPULATION STUDIES
(2021)
Article
Computer Science, Interdisciplinary Applications
Iram Saleem, Aamir Sanaullah
Summary: The study introduced two new estimators for mean estimation of a sensitive variable in stratified sampling and proved their higher efficiency in certain situations compared to existing estimators.
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Qi Zhang, Sadia Khalil, Sat Gupta
Summary: This study discusses mean estimation of sensitive variables in the presence of measurement errors and non-response using Stratified Random Sampling, and compares the proposed estimator with some existing estimators.
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2021)
Article
Statistics & Probability
G. N. Singh, D. Bhattacharyya, A. Bandyopadhyay
Summary: This paper addresses the issue of estimating the population variance of a study character in the presence of random non-response and measurement errors, and its application in estimating variations in biological data. The paper proposed a general class of estimators under a stratified two-phase sampling scheme, incorporating additional information from highly positively correlated auxiliary variables. The properties of these estimators were examined in terms of bias and mean square error. Simulation results using artificial and real data support the superiority of the proposed estimators compared to a contemporary estimator.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2023)
Article
Statistics & Probability
G. N. Singh, D. Bhattacharyya, A. Bandyopadhyay
Summary: This paper proposes a method for estimating the population variance of the study character in stratified successive sampling under random non-response. A logarithmic type estimator utilizing information from a highly positively correlated auxiliary variable is introduced and shown to be more efficient than the standard estimator. Calibration techniques are used to determine the optimal strata weight for the estimator. Empirical studies using both real and simulated data demonstrate the effectiveness of the proposed estimator over the standard estimator, providing valuable recommendations for survey statisticians in real-life applications.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2021)
Article
Statistics & Probability
Neha Singh, Gajendra K. Vishwakarma, Raj K. Gangele
Summary: This study proposes three classes of estimators for estimating the population variance of study variable in stratified random sampling under measurement errors. These estimators are shown to be more efficient compared to usual estimators, and their bias and mean square error can be easily derived.
REVSTAT-STATISTICAL JOURNAL
(2021)
Article
Multidisciplinary Sciences
Nasir Ali, Ishfaq Ahmad, Usman Shahzad, Nadia H. Al-Noor
Summary: In this research, new estimators of finite population mean were proposed using the transformations of coefficient of variation, kurtosis coefficient, and real numbers in stratified random sampling without replacement scheme. The proposed estimators were found to be more efficient than traditional estimators.
JOURNAL OF SCIENCE AND ARTS
(2022)
Article
Agriculture, Multidisciplinary
Shashi Bhushan, Anoop Kumar, Saurabh Singh
Summary: This study investigates combined and separate log type estimators for the population mean using stratified random sampling. The mean square error expressions of the proposed estimators are determined to the first order of approximation. Some real data sets are considered to assess the performance of the proposed estimators. The numerical results favor the proposition of the proposed estimators over its competitors.
INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES
(2022)
Article
Statistics & Probability
Shashi Bhushan, Anoop Kumar, Dushyant Tyagi, Saurabh Singh
Summary: This article proposes improved classes of estimators for population mean of a study variable using auxiliary attribute information under stratified simple random sampling. The suggested classes of estimators include the usual mean estimator, classical ratio estimator, classical product estimator, and classical regression estimator as special cases. The mean square error of these classes of estimators is studied up to the first order of approximation and their performance is compared with conventional and recent estimators. An empirical study using real data provides support for the theoretical results and demonstrates the relative efficiency of the proposed classes of estimators.
JOURNAL OF RELIABILITY AND STATISTICAL STUDIES
(2022)
Article
Agriculture, Multidisciplinary
Neha Garg, Menakshi Pachori
Summary: This article introduces a logarithmic calibration estimator for population mean in stratified random sampling, which is further extended for stratified double sampling to estimate population parameters. A simulation study comparing the performance of the suggested estimator with that of Tracy et al. (2003) was performed on two real datasets.
INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES
(2021)
Article
Multidisciplinary Sciences
Khalid Ul Islam Rather, M. Iqbal Jeelani, Afshan Tabassum
Summary: This study examines the problem of using a separate exponential ratio estimator to estimate population mean, calculating the MSE and Bias of the suggested estimator. It is proven to be more efficient than those mentioned in literature under stratified random technique. The empirical investigation also verifies the remarkable percent relative efficiency of the proposed estimator.
JOURNAL OF SCIENCE AND ARTS
(2022)
Article
Mathematics, Applied
Manoj K. Chaudhary, Tulika Dutta
Summary: This paper presents the improvements in estimating the mean of a stratified population with scrambled response under non-response. A new calibration estimator, utilizing information from a single auxiliary variable, has been proposed. The expression for the MSE of the proposed calibration estimator has been derived, and a simulation study has shown its performance.
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS
(2023)
Article
Environmental Sciences
Andrea L. Popp, Alvaro Pardo-Alvarez, Oliver S. Schilling, Andreas Scheidegger, Stephanie Musy, Morgan Peel, Philip Brunner, Roland Purtschert, Daniel Hunkeler, Rolf Kipfer
Summary: This study introduces a new approach to understand the relationship between groundwater mixing ratios and travel times using in-situ noble gas analyses. The results from a groundwater pumping test conducted in Switzerland demonstrate the importance of interpreting age dating tracers and the influence of different water sources on estimated travel times.
WATER RESOURCES RESEARCH
(2021)
Article
Engineering, Environmental
Xavier Fernandez-Cassi, Andreas Scheidegger, Carola Banziger, Federica Cariti, Alex Tunas Corzon, Pravin Ganesanandamoorthy, Joseph C. Lemaitre, Christoph Ort, Timothy R. Julian, Tamar Kohn
Summary: The study found that wastewater monitoring can more accurately track the timing and shape of COVID-19 infection peaks, while confirmed cases provide a better estimate of the subsequent decline in infections. Under conditions of high test positivity rates, wastewater-based epidemiology provides critical information that complements clinical data in monitoring the pandemic trajectory.
Article
Engineering, Environmental
Michael A. Stravs, Christian Stamm, Christoph Ort, Heinz Singer
Summary: This study presents a transportable platform capable of tracking a wide range of chemicals in the aquatic environment with high temporal resolution over extended time periods. The system can quantify pollutants with high sensitivity and has demonstrated insights into pollutant dynamics and potential applications in pollutant management and epidemiology.
ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS
(2021)
Article
Infectious Diseases
Lea Caduff, David Dreifuss, Tobias Schindler, Alexander J. Devaux, Pravin Ganesanandamoorthy, Anina Kull, Elyse Stachler, Xavier Fernandez-Cassi, Niko Beerenwinkel, Tamar Kohn, Christoph Ort, Timothy R. Julian
Summary: This study adapts a rapid, high-throughput method to detect and quantify the relative frequency of characteristic deletions of the Alpha, Beta, and Gamma variants of SARS-CoV-2 in wastewater. The results provide insights into the transmission fitness advantage of the Alpha variant and demonstrate the potential of wastewater surveillance for real-time monitoring of VOCs.
Article
Environmental Sciences
Cristina Prieto, Nataliya Le Vine, Dmitri Kavetski, Fabrizio Fenicia, Andreas Scheidegger, Claudia Vitolo
Summary: This study introduces a Bayesian framework to identify specific hydrological model mechanisms in ungauged catchments. By formulating inference equations in the space of flow indices and accounting for parameter uncertainty, dominant model mechanisms can be identified. The proposed method is validated using real data and synthetic experiments, and limitations and opportunities for improvement in hydrological mechanism identification under data-scarce conditions are discussed.
WATER RESOURCES RESEARCH
(2022)
Article
Microbiology
Katharina Jahn, David Dreifuss, Ivan Topolsky, Anina Kull, Pravin Ganesanandamoorthy, Xavier Fernandez-Cassi, Carola Banziger, Alexander J. Devaux, Elyse Stachler, Lea Caduff, Federica Cariti, Alex Tunas Corzon, Lara Fuhrmann, Chaoran Chen, Kim Philipp Jablonski, Sarah Nadeau, Mirjam Feldkamp, Christian Beisel, Catharine Aquino, Tanja Stadler, Christoph Ort, Tamar Kohn, Timothy R. Julian, Niko Beerenwinkel
Summary: Genomic sequencing of wastewater samples can provide early detection and surveillance of SARS-CoV-2 variants. The COJAC bioinformatics method based on variant-specific signature mutations is a robust indicator of low-frequency variants. Analysis of multiple wastewater samples allows for estimation of variant prevalence and transmission fitness advantage.
NATURE MICROBIOLOGY
(2022)
Article
Engineering, Environmental
Marina Celia Campos-Manas, Natan Van Wichelen, Adrian Covaci, Alexander L. N. van Nuijs, Christoph Ort, Frederic Been, Sara Castiglioni, Felix Hernandez, Lubertus Bijlsma
Summary: Wastewater analysis of THC biomarkers provides important information on cannabis consumption trends. Analyzing both the liquid and solid phases yields more accurate results.
Article
Water Resources
Katharina Tondera, Elodie Brelot, Fanny Fontanel, Frederic Cherqui, Jesper Ellerbaek Nielsen, Thomas Brueggemann, Iain Naismith, Marcel Goerke, Joaquin Suarez Lopez, Joerg Rieckermann, Joao P. Leitao, Francois H. L. R. Clemens-Meyer, Antonio Moreno-Rodenas, Simon Tait, Jose Anta
Summary: Transitioning urban drainage systems to serve water-smart societies involves multiple disciplines and stakeholders, who have diverse visions and needs for the process, varying between regions and countries. Understanding these needs is crucial for proposing practical adaptation strategies. A study collected evidence from policy papers and legislation in seven European countries, and gathered opinions from knowledgeable individuals in the urban drainage community. The results showed diverse visions on how to transition, indicating the need for more interaction between stakeholders to develop consensus. Additionally, organizational and legislative structures often hinder the necessary change processes.
URBAN WATER JOURNAL
(2023)
Article
Engineering, Environmental
Manuel Regueiro-Picallo, Jose Anta, Acacia Naves, Alejandro Figueroa, Joerg Rieckermann
Summary: This study presents a novel methodology to detect and assess bed deposits in urban drainage systems based on temperature monitoring. The thickness of sediment could be estimated from the time evolution of temperature differences, and a 1D heat transfer model was used to simulate sediment accumulation scenarios. The study concludes that temperature measurements and heat transfer model analysis can be used to approximate and monitor sediments in urban drainage systems.
ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY
(2023)
Article
Ecology
Michelle Viswanathan, Andreas Scheidegger, Thilo Streck, Sebastian Gayler, Tobias K. D. Weber
Summary: Plant phenology models are crucial for assessing the impact of climate change on food production. This study calibrated a phenology model using a Bayesian multi-level approach, taking into account various factors such as ecological, weather, and year effects, as well as the hierarchical classification of cultivars. The results showed that considering all these factors improved the calibration quality of the model.
ECOLOGICAL MODELLING
(2022)
Article
Geosciences, Multidisciplinary
Marvin Hoege, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, Fabrizio Fenicia
Summary: Deep learning methods have shown better performance than conceptual hydrologic models in rainfall-runoff modelling. However, the internal workings of these deep learning models and their relationships with input and output are not fully understood. In this study, the authors propose hydrologic neural ordinary differential equation (ODE) models that combine the interpretability of conceptual models with the power of deep learning. The models are tested on 569 catchments in the continental United States, and their predictive performance and internal dynamics are analyzed.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Environmental Sciences
Federica Cariti, Alex Tunas Corzon, Xavier Fernandez-Cassi, Pravin Ganesanandamoor, Christoph Ort, Timothy R. Julian, Tamar Kohn
Summary: Wastewater-based epidemiology (WBE) has been proven effective in monitoring the spread of SARS-CoV-2 during the COVID-19 pandemic. A study conducted in Ticino, Switzerland, using WBE revealed the spatiotemporal evolution of the virus in the canton. This highlights the potential of WBE as a versatile tool for monitoring the introduction and spread of infectious agents.
Article
Geosciences, Multidisciplinary
Anna Spackova, Vojtech Bares, Martin Fencl, Marc Schleiss, Joel Jaffrain, Alexis Berne, Joerg Rieckermann
Summary: Commercial microwave links can provide relevant information for remote sensing of environmental variables, with the CoMMon field experiment mainly focusing on rainfall observations. The data quality is generally satisfactory and potentially problematic measurements are flagged for further analysis.
EARTH SYSTEM SCIENCE DATA
(2021)
Correction
Environmental Sciences
Yuanfang Zheng, Lena Mutzner, Christoph Ort, Ralf Kaegi, Fadri Gottschalk
Article
Chemistry, Analytical
Jonas Wielinski, Francesco Femi Marafatto, Alexander Gogos, Andreas Scheidegger, Andreas Voegelin, Christoph R. Mueller, Eberhard Morgenroth, Ralf Kaegi
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
(2020)