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

scholarly journals Efficient Sampling Design in Audit Data

2021 ◽  
Vol 3 (3) ◽  
pp. 213-222
Author(s):  
Yan Liu ◽  
Mary Batcher ◽  
Fritz Scheuren
Keyword(s):  
Sampling Design ◽  
Audit Data ◽  
Efficient Sampling

Sampling Designs and Estimators for Monitoring Vehicle Characteristics Under Limited Inspection Capacity

1997 ◽  
Vol 1581 (1) ◽  
pp. 72-81
Author(s):  
J. G. Kinateder ◽  
N. J. McMillan ◽  
J. E. Orban ◽  
B. O. Skarpness ◽  
D. Wells
Keyword(s):  
Metropolitan Area ◽  
Road Network ◽  
Sampling Design ◽  
Accurate Estimate ◽  
Statistical Problem ◽  
Motor Vehicles ◽  
Limited Resources ◽  
The Road ◽  
Efficient Sampling ◽  
On The Road

Estimating characteristics of motor vehicles and their occupants based on sampling a small portion of vehicles on the road is inherently a statistical problem. The problem has two components: construction of an efficient sampling design and application of an appropriate estimator to collected data. Constructing the sampling design should involve optimal utilization of limited resources to maximize information collected. The selected estimator should provide the most precise and accurate estimate of the characteristic of interest. Four sampling designs and two estimators for determining the proportion of vehicles with a particular characteristic are compared. A simulation example comparing these designs and estimators is provided. The illustration is based on a hypothetical road network typical of the primary transportation system in a metropolitan area of 500,000 to 1 million persons. Some suggestions for choosing appropriate sampling designs and estimators for other vehicle categorization problems are provided.

scholarly journals Introducing an efficient sampling method for national surveys with limited sample sizes: application to a national study to determine quality and cost of healthcare

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahboubeh Parsaeian ◽  
Mahdi Mahdavi ◽  
Mojdeh Saadati ◽  
Parinaz Mehdipour ◽  
Ali Sheidaei ◽  
...  
Keyword(s):  
Data Mining ◽  
Sampling Method ◽  
Sampling Design ◽  
Small Sample ◽  
Face Validity ◽  
National Study ◽  
Clustering Method ◽  
Country Level ◽  
Efficient Sampling ◽  
Entire Country

Abstract Background Sampling a small number of participants from an entire country is not straightforward. In this case, researchers reluctantly sample from a single setting or few settings, which limits the generalizability of findings. Therefore, there is a need to design efficient sampling method for small sample size surveys that can produce generalizable results at the country level. Methods Data comprised of twenty proxy variables to measure health services demands, structures, and outcomes of 413 districts of Iran. We used two data mining methods (hierarchical clustering method (HCM) and model-based clustering method (MCM)) to create homogenous groups of districts, i.e., strata based on these variables. We compared the internal and stability validity of the methods by statistical indices. An expert group checked the face validity of the methods, particularly regarding the total number of strata and the combination of districts in each stratum. The efficiency of selected method, which is measured by the inverse of variance, was compared with a simple random sampling (SRS) through simulation. The sampling design was tested in a national study in Iran, which aimed to evaluate the quality and costs of medical care for eight selected diseases by only recruiting 300 participants per disease at the country level. Results MCM and HCM divided the districts into eight and two clusters, respectively. The measures of internal and stability validity showed that clusters created by MCM were more separated, compact, and stable, thus forming our optimum strata. The probability of death from stroke, chronic obstructive pulmonary disease, and in-hospital mortality rate were the most important indicators that distinguished the eight strata. Based on the simulation results, MCM increased the efficiency of the sampling design up to 1.7 times compared to SRS. Conclusions The use of data mining improved the efficiency of sampling up to 1.7 times greater than SRS and markedly reduced the number of strata to eight in the entire country. The proposed sampling design also identified key variables that could be used to classify districts in Iran for sampling from these target populations in the future studies.

scholarly journals Sampling Design Method of Fast Optimal Latin Hypercube

2019 ◽  
Vol 37 (4) ◽  
pp. 714-723 ◽  
Author(s):  
Pengcheng Ye ◽  
Guang Pan ◽  
Shan Gao
Keyword(s):  
Large Scale ◽  
Sampling Design ◽  
Design Method ◽  
Design Methods ◽  
Small Scale ◽  
Optimal Sampling ◽  
Latin Hypercube Design ◽  
Latin Hypercube ◽  
Computation Efficiency ◽  
Optimal Sampling Design

In engineering design optimization, the optimal sampling design method is usually used to solve large-scale and complex system problems. A sampling design (FOLHD) method of fast optimal Latin hypercube is proposed in order to overcome the time-consuming and poor efficiency of the traditional optimal sampling design methods. FOLHD algorithm is based on the inspiration that a near optimal large-scale Latin hypercube design can be established by a small-scale initial sample generated by using Successive Local Enumeration method and Translational Propagation algorithm. Moreover, a sampling resizing strategy is presented to generate samples with arbitrary size and owing good space-filling and projective properties. Comparing with the several existing sampling design methods, FOLHD is much more efficient in terms of the computation efficiency and sampling properties.

scholarly journals Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yingli Pan ◽  
Songlin Liu ◽  
Yanli Zhou ◽  
Guangyu Song
Keyword(s):  
Large Scale ◽  
Sampling Design ◽  
Design Method ◽  
Real Data ◽  
Finite Sample ◽  
Additive Hazards ◽  
Hazards Model ◽  
Additive Hazard Model ◽  
Sample Property ◽  
Insight Into

This paper provides a new insight into an economical and effective sampling design method relying on the outcome-dependent sampling (ODS) design in large-scale cohort research. Firstly, the importance and originality of this paper is that it explores how to fit the covariate-adjusted additive Hazard model under the ODS design; secondly, this paper focused on estimating the distortion function through nonparametric regression and required observation of the covariate on the confounding factors of distortion; moreover, this paper further calibrated the contaminated covariates and proposed the estimators of the parameters by analyzing the calibrated covariates; finally, this paper established the large sample property and asymptotic normality of the proposed estimators and conducted many more simulations to evaluate the finite sample performance of the proposed method. Empirical research demonstrates that the results from both artificial and real data verified good performance and practicality of the proposed ODS method in this paper.

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