scholarly journals Incorporating the sampling design in weighting adjustments for panel attrition

2015 ◽  
Vol 34 (28) ◽  
pp. 3637-3647 ◽  
Author(s):  
Qixuan Chen ◽  
Andrew Gelman ◽  
Melissa Tracy ◽  
Fran H. Norris ◽  
Sandro Galea
Keyword(s):  
Sampling Design ◽  
Panel Attrition ◽  
Weighting Adjustments

55. An Efficient Sampling Design Method to Determine Task-Based Exposure Distribution

1999 ◽  
Author(s):  
N.A. Esmen ◽  
M.L Phillips ◽  
N. Rainey
Keyword(s):  
Sampling Design ◽  
Design Method ◽  
Efficient Sampling ◽  
Exposure Distribution

Coastal Ocean Observing System Modeling: Data Assimilation and Adaptive Sampling Design

2007 ◽  
Author(s):  
John Wilkin ◽  
Julia C. Levin ◽  
Hernan G. Arango
Keyword(s):  
Data Assimilation ◽  
Adaptive Sampling ◽  
Sampling Design ◽  
System Modeling ◽  
Coastal Ocean ◽  
Modeling Data ◽  
Ocean Observing

Sampling design in literacy survey

1950 ◽  
Vol 2 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Chikio Hayashi ◽  
Fumiyuki Maruyama ◽  
Masatsugu D. Ishida ◽  
Setsuko Takakura ◽  
Masako Taguma ◽  
...  
Keyword(s):  
Sampling Design ◽  
Literacy Survey

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

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 performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

scholarly journals A strategic sampling design revealed the local genetic structure of cold-water fluvial sculpin: a focus on groundwater-dependent water temperature heterogeneity

Heredity ◽  
2021 ◽  
Author(s):  
Souta Nakajima ◽  
Masanao Sueyoshi ◽  
Shun K. Hirota ◽  
Nobuo Ishiyama ◽  
Ayumi Matsuo ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Water Temperature ◽  
Sampling Design ◽  
Cold Water

Effects of non-representative sampling design on multi-scale habitat models: flammulated owls in the Rocky Mountains.

2021 ◽  
Vol 450 ◽  
pp. 109566
Author(s):  
Luca Chiaverini ◽  
Ho Yi Wan ◽  
Beth Hahn ◽  
Amy Cilimburg ◽  
Tzeidle N. Wasserman ◽  
...  
Keyword(s):  
Rocky Mountains ◽  
Sampling Design ◽  
Habitat Models ◽  
Representative Sampling ◽  
Multi Scale

scholarly journals SAMPLE BIAS RELATED TO HOUSEHOLD ROLE

2020 ◽  
Author(s):  
MARCIN HITCZENKO
Keyword(s):  
Sampling Design ◽  
Sampling Procedure ◽  
Bootstrap Analysis ◽  
Electronic Payments ◽  
Household Finances ◽  
Sample Bias ◽  
Household Members ◽  
Survey Responses ◽  
True Values ◽  
Estimation Of Distributions

Abstract This article develops a two-stage statistical analysis to identify and assess the effect of a sample bias associated with an individual’s household role. Survey responses to questions about the respondent’s role in household finances and a sampling design in which some households have all members take the survey enable the estimation of distributions for each individual’s share of household responsibility. The methodology is applied to the 2017 Survey of Consumer Payment Choice. The distribution of responsibility shares among survey respondents suggests that the sampling procedure favors household members with higher levels of responsibility. A bootstrap analysis reveals that population mean estimates of monthly payment instrument use that do not account for this type of sample misrepresentation are likely biased for instruments often used to make household purchases. For checks and electronic payments, our analysis suggests that it is likely that unadjusted estimates overstate true values by 10–20 percent.

Habitat selection and sampling design for ecological assessment of heterogeneous ponds using macroinvertebrates

2009 ◽  
Vol 19 (7) ◽  
pp. 786-796 ◽  
Author(s):  
Cristina Trigal-Domínguez ◽  
Camino Fernández-Aláez ◽  
Francisco García-Criado
Keyword(s):  
Habitat Selection ◽  
Sampling Design ◽  
Ecological Assessment
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