23 papers · ranked by Valyu relevance
Mohamed Elfil, Ahmed Negida
Clinical research usually involves patients with a certain disease or a condition. The generalizability of clinical research findings is based on multiple factors related to the internal and external validity of the research methods. The main methodological issue that influences the generalizability of clinical…
Stephen Tyrer, Bob Heyman
Surveys of people's opinions are fraught with difficulties. It is easier to obtain information from those who respond to text messages or to emails than to attempt to obtain a representative sample. Samples of the population that are selected non-randomly in this way are termed convenience samples as they are easy to…
Oliver Selmoni, Elia Vajana, Stéphane Joost
An increasing number of studies use landscape genomics to investigate local adaptation in wild and domestic populations. This method consists of simultaneously characterizing genetic and environmental variability across a landscape in order to depict how genotype frequencies are shaped by adaptive forces. The…
Diptarup Nandi, Megha Suswaram, Rohini Balakrishnan
Long-range communication signals play a central role in mate search and mate choice across a wide range of taxa. Among the different aspects of mate choice, the strategy an individual employ to search for potential mates (mate sampling) has been less explored despite its significance. Although analytical models of mate…
Abigail Hsiung, John M. Pearson, Jia-Hou Poh, Shabnam Hakimi + 2 more
To guide effective decision making in an uncertain world, humans must balance seeking relevant information with the costs of delaying choice. Optimal information sampling requires computationally expensive value estimations. Information sampling heuristics are computationally simple, but rigid. Efficient and flexible…
Luigi Sedda, Eric R. Lucas, Luc S. Djogbénou, Ako V.C. Edi + 6 more
Vector-borne disease control relies on efficient vector surveillance, mostly carried out using traps whose number and locations are often determined by expert opinion rather than a rigorous quantitative sampling design. In this work we first propose a framework for ecological sampling design which in its preliminary…
June Gorostidi, Adem Ait, Jordi Cabot, Javier Luis Izquierdo
Software repositories is one of the sources of data in Empirical Software Engineering, primarily in the Mining Software Repositories field, aimed at extracting knowledge from the dynamics and practice of software projects. With the emergence of social coding platforms such as GitHub, researchers have now access to…
Loan R. van Hoeven, Mart P. Janssen, Kit C. B. Roes, Hendrik Koffijberg
'Hendrik Koffijberg'] Background A ubiquitous issue in research is that of selecting a representative sample from the study population. While random sampling strategies are the gold standard, in practice, random sampling of participants is not always feasible nor necessarily the optimal choice. In our case, a selection…
David A. Rasmussen, Madeline G. Bursell, Frank Burkhart
Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet predicting what information will be gained from genomic data…
Célia Landmann Szwarcwald
This article aimed to present an overview of national health surveys, sampling techniques, and components of statistical analysis of data collected using complex sampling designs. Briefly, surveys aimed at assessing the nutritional status of Brazilians and maternal and child health care were described. Surveys aimed at…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Roger Hillson, Joel D. Alejandre, Kathryn H. Jacobsen, Rashid Ansumana + 5 more
'Rashid Ansumana' 'Alfred S. Bockarie' 'Umaru Bangura' 'Joseph M. Lamin' 'David A. Stenger' 'Maciej F. Boni'] There is a need for better estimators of population size in places that have undergone rapid growth and where collection of census data is difficult. We explored simulated estimates of urban population based on…
Giulio Barcaroli, Andrea Fasulo, Alessio Guandalini, Marco D. Terribili
'Marco D. Terribili'] R2BEAT ("R 'to' Bethel Extended Allocation for Two-stage sampling") is an R package for the allocation of a sample. Besides other software and packages dealing with the allocation problems, its peculiarity lies in facing properly allocation problems for complex sampling designs with multi-domain…
Yves Tillé, Matthieu Wilhelm
The aim of this paper is twofold. First, three theoretical principles are formalized: randomization, overrepresentation and restriction. We develop these principles and give a rationale for their use in choosing the sampling design in a systematic way. In the model-assisted framework, knowledge of the population is…
Authors not listed
Accurate and efficient calculation of alchemical free energies is a critical challenge in computational chemistry, frequently hindered by the inherent limitations of conventional Thermodynamic Integration (TI) methods. These limitations include poor phasespace overlap between discrete alchemical states, inefficient…
Benyamin Ghojogh, Hadi Nekoei, Aydin Ghojogh, Fakhri Karray + 1 more
'Mark Crowley'] This paper is a tutorial and literature review on sampling algorithms. We have two main types of sampling in statistics. The first type is survey sampling which draws samples from a set or population. The second type is sampling from probability distribution where we have a probability density or mass…
Jae Kwang Kim
This textbook on survey sampling has its origins in a set of lecture notes prepared for a course on survey sampling at Iowa State University. Over the years, these notes have been refined and expanded into the comprehensive volume you now hold. It is designed to serve both as an introductory text for students and as a…
Faizan Danish, Alejandro Botero Carvajal
This study investigates the determination of stratification points for two study variables within the framework of simple random sampling, with a focus on estimating the population mean using a closely related auxiliary variable. Employing a superpopulation model, the research aims to minimize overall variance by…
Zhimian Hao, Chonghuan Zhang, Alexei Lapkin
We propose a workflow for reduction in the time required for data generation during generation of statistical digital twins. This methodology is particularly relevant for real-world engineering problems when data generation is expensive. A prerequisite for building surrogates is sufficient input/output data, whereas…
Xiangke Pu, Ge Gao, Yubo Fan, Mian Wang + 1 more
Randomized response is a research method to get accurate answers to sensitive questions in structured sample survey. Simple random sampling is widely used in surveys of sensitive questions but hard to apply on large targeted populations. On the other side, more sophisticated sampling regimes and corresponding formulas…
Authors not listed
Developing generalizable machine learning models with minimal data remains a central challenge in materials informatics. Effective models can significantly reduce costly computational simulations and time-intensive experimentation by providing reliable predictions of material properties. In this work, we investigate…
Jonathan Mendelson, Michael R. Elliott
We develop a Bayesian optimal sample allocation approach for stratified sampling in heteroscedastic populations. Existing optimal allocation theory typically assumes knowledge of certain design parameters (e.g., strata variances) that may be unknown, leading practitioners to substitute in survey-based estimates when…
Riley Hickman, Malcolm Sim, Sergio Pablo-García, Ivan Woolhouse + 6 more
Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to…