25 papers · ranked by Valyu relevance
Guillaume Chauvet
Multistage sampling is commonly used for household surveys when there exists no sampling frame, or when the population is scattered over a wide area. Multistage sampling usually introduces a complex dependence in the selection of the final units, which makes asymptotic results quite difficult to prove. In this work, we…
Sixia Chen, David Haziza, Zeinab Mashreghi
Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimation is a complex task as it relies on the availability of second-order inclusion…
Patricia Puerta, Lorenzo Ciannelli, Bethany Johnson, Xavier Harrison
Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary…
Weifei Gan, Xin Zhou, Wangyu Wu, Chang-An Xu + 1 more
Defect-rate uncertainty creates cascading operational challenges in multi-stage production, often driving inefficiency and misallocation of labor, materials, and capacity. To confront this, we develop a multi-stage Production Integrated Decision (MsPID) framework that unifies quality inspection and shop-floor…
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…
Zhihang Yuan, Xin Liu, Bingzhe Wu, Guangyu Sun
Dynamic inference is a feasible way to reduce the computational cost of convolutional neural network(CNN), which can dynamically adjust the computation for each input sample. One of the ways to achieve dynamic inference is to use multistage neural network, which contains a sub-network with prediction layer at each…
Mohsin Abbas, Muhammad Ahmed Shehzad, Mahwish Rabia, Haris Khurram + 2 more
'Muhammad Ijaz' 'Eugene Demidenko'] Accurate estimation of the finite population mean is a fundamental challenge in survey sampling, especially when dealing with large or complex populations. Traditional methods like simple random sampling may not always provide reliable or efficient estimates in such cases. Motivated…
Haoyang Lu, Li Yi, Hang Zhang
Information sampling can reduce uncertainty in future decisions but is often costly. To maximize expected gain, people need to balance sampling cost and information gain. Suboptimal information sampling is observed in many core symptoms of autism spectrum disorder (ASD) and may be a general impairment in ASD. Here we…
Matthew J. Spittal, John B. Carlin, Dianne Currier, Marnie Downes + 4 more
'Dallas R. English' 'Ian Gordon' 'Jane Pirkis' 'Lyle Gurrin'] Background The Australian Longitudinal Study on Male Health (Ten to Men) used a complex sampling scheme to identify potential participants for the baseline survey. This raises important questions about when and how to adjust for the sampling design when…
Jasper B. Yang, Bryan E. Shepherd, Thomas Lumley, Pamela A. Shaw
The R package optimall offers a collection of functions that efficiently streamline the design process of sampling in surveys ranging from simple to complex. The package's main functions allow users to interactively define and adjust strata cut points based on values or quantiles of auxiliary covariates, adaptively…
André Mendes, Julian Togelius, Leandro dos Santos Coelho
—In multi-stage processes, decisions occur in an ordered sequence of stages. Early stages usually have more observations with general information (easier/cheaper to collect), while later stages have fewer observations but more specific data. This situation can be represented by a dual funnel structure, in which the…
Jonathan Legare, Ping Yao, Victor S. Y. Lo
Many businesses conduct experiments to scientifically test, measure, and optimize decisions in areas like sales, marketing, and operations efficiency. While randomized controlled trials (RCTs) or A/B tests are the dominant method for conducting business experiments especially for business-to-consumer marketing…
Nikolaos Vasilakis, Konstantinos I. Papadimitriou, Hywel Morgan, Themistoklis Prodromakis
Fast, efficient and more importantly accurate serial dilution is a requirement for many chemical and biological microfluidic-based applications. Over the last decade, a large number of microfluidic devices has been proposed, each demonstrating either a different type of dilution technique or complex system…
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…
Martin Law, Michael J. Grayling, Adrian Mander
Existing multi-outcome designs focus almost entirely on evaluating whether all outcomes show evidence of efficacy or whether at least one outcome shows evidence of efficacy. While a small number of authors have provided multi-outcome designs that evaluate when a general number of outcomes show promise, these designs…
Collin Giguere, Harsh Vardhan Dubey, Vishal Kumar Sarsani, Hachem Saddiki + 2 more
Recently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested…
John P. Efromson, Shuai Li, Michael D. Lynch
Autosampling from bioreactors reduces error, increases reproducibility and offers improved aseptic handling when compared to manual sampling. Additionally, autosampling greatly decreases the hands-on time required for a bioreactor experiment and enables sampling 24 hrs a day. We have designed, built and tested a low…
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…
Authors not listed
As demand rises for scalable and sustainable energy storage, fast and low-cost diagnostics capable of identifying cell-to-cell variations are urgently needed, particularly for factory-produced sorting and second-life assessment. Electrochemical impedance spectroscopy (EIS) is widely used but remains slow, expensive and…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Yuji Kaiya, Ryo Tamura, Koji Tsuda
Kinetic models are widely used in simulating the relationship between the input space and the outcome space of a chemical process. Ignoring the computational cost, complete profiling, i.e., performing simulation at all grid points in the input space, would be the best way to understand the model, because it provides us…
Hongli Jiang, S. Hessam M. Mehr
The availability, portability, and low cost of electronic devices have made them a prime candidate for the rapid detection of chemical particles. Here we designed a chemical particle detection system based on a Raspberry Pi camera to detect micron droplets generated by ultrasonic atomizers. Through the analysis of…
Zenabu Suboi, Thomas J. Hladish, Wim Delva, C. Marijn Hazelbag
Complex models are often fitted to data using simulation-based calibration, a computationally challenging process. Several calibration methods to improve computational efficiency have been developed with no consensus on which methods perform best. We did a simulation study comparing the performance of 5 methods that…
Authors not listed
The Single-probe is a multifunctional device that can be coupled to mass spectrometry (MS) for molecular analysis of microscale samples, such as single cells, tissue slices, and multicellular spheroids, under ambient conditions. In Single-probe single cell MS (SCMS) studies, this technique leverages direct sampling and…
Marian Manciu
The traditional statistical methods are very powerful if the sampling number in an experiment meets at least the minimum power analysis requirement. However, at lower sampling numbers, they tend to dramatically underestimate the discoveries leading to a large percentage of type II errors. Sometimes, the minimum…