A Novel Quota Sampling Algorithm for Generating Representative Random Samples given Small Sample Size

2013 ◽  
Vol 2 (1) ◽  
pp. 97-113 ◽  
Author(s):  
Ahmed M. Fouad ◽  
Mohamed Saleh ◽  
Amir F. Atiya
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Sampling Method ◽  
Optimization Problems ◽  
Small Sample Size ◽  
Probability Distributions ◽  
Small Sample ◽  
Sampling Algorithm ◽  
Quasi Monte Carlo ◽  
Quota Sampling

In this paper, a novel algorithm is proposed for sampling from discrete probability distributions using the probability proportional to size sampling method, which is a special case of Quota sampling method. The motivation for this study is to devise an efficient sampling algorithm that can be used in stochastic optimization problems -- when there is a need to minimize the sample size. Several experiments have been conducted to compare the proposed algorithm with two widely used sample generation methods, the Monte Carlo using inverse transform, and quasi-Monte Carlo algorithms. The proposed algorithm gave better accuracy than these methods, and in terms of time complexity it is nearly of the same order.

Uncertainty Evaluation Study of CMM Dynamic Measurement Based on Quasi Monte Carlo Method

2011 ◽  
Vol 103 ◽  
pp. 366-371 ◽  
Author(s):  
Wei Hong Zhong ◽  
Xiu Shui Ma ◽  
Ying Dao Li ◽  
Yuan Li
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Small Sample Size ◽  
Dynamic Measurement ◽  
Small Sample ◽  
Uncertainty Evaluation ◽  
Effective Diameter ◽  
Large Sample Size ◽  
Quasi Monte Carlo ◽  
Measuring Machine

In a contact measurement process, the coordinate measuring machine（CMM）probe will bring dynamic measurement error, therefore, dynamic calibration of the probe tip effective diameter should to be done at different probing speeds, and calibration uncertainty should to be given. There are some problems, slow convergence and unstable, using Monte Carlo (MC) method in uncertainty. In this paper, Quasi Monte Carlo (QMC) method is presented in the probe tip effective diameter uncertainty evaluation. At a certain positioning speed and distance approximation, probe tip effective diameter experimental tests are done with changing probing speeds. MC and QMC methods are used on uncertainty evaluation respectively, and the results are compared and analyzed. The simulation shows that QMC can be used on dynamic uncertainty evaluation of CMM probe tip. Compared with MC, QMC obtains a better stability and precision in small sample size and gains higher computing speed in large sample size.显示对应的拉丁字符的拼音 字典名词 assessment动词 assessevaluatepass judgment

Design Curve Construction Based on Monte Carlo Simulation

2013 ◽  
Author(s):  
Zhigang Wei ◽  
Limin Luo ◽  
Burt Lin ◽  
Dmitri Konson ◽  
Kamran Nikbin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sample Size ◽  
Test Data ◽  
Confidence Level ◽  
Small Sample Size ◽  
Test Sample ◽  
Small Sample ◽  
Tolerance Limit ◽  
Design Curve

Good durability/reliability performance of products can be achieved by properly constructing and implementing design curves, which are usually obtained by analyzing test data, such as fatigue S-N data. A good design curve construction approach should consider sample size, failure probability and confidence level, and these features are especially critical when test sample size is small. The authors have developed a design S-N curve construction method based on the tolerance limit concept. However, recent studies have shown that the analytical solutions based on the tolerance limit approach may not be accurate for very small sample size because of the assumptions and approximations introduced to the analytical approach. In this paper a Monte Carlo simulation approach is used to construct design curves for test data with an assumed underlining normal (or lognormal) distribution. The difference of factor K, which measures the confidence level of the test data, between the analytical solution and the Monte Carlo simulation solutions is compared. Finally, the design curves constructed based on these methods are demonstrated and compared using fatigue S-N data with small sample size.

Efficient Sampling Methods for Tradeoff Studies Under Uncertainty

2012 ◽  
Author(s):  
Christopher S. Bily ◽  
Richard J. Malak
Keyword(s):  
Monte Carlo ◽  
Probability Distributions ◽  
Uncertainty Propagation ◽  
Small Sample ◽  
Design Criteria ◽  
Quasi Monte Carlo ◽  
Multi Criteria Evaluation ◽  
Design Alternatives ◽  
Efficient Sampling ◽  
Minimum Number

Tradeoff studies help designers better understand how different design considerations relate to one another and to make decisions. Generally a tradeoff study involves a systematic multi-criteria evaluation of various alternatives for a particular system or subsystem. After evaluating these alternatives, designers eliminate those that perform poorly using the Pareto dominance criterion and explore more carefully those that remain. An analogous procedure is possible when design criteria are uncertain. This approach is based on stochastic dominance principles that involve comparisons of probability distributions defined in the design criteria space. Although this is well-founded mathematically, the procedure can be computationally expensive because it typically entails a sampling-based uncertainty propagation method (e.g. Monte Carlo or quasi-Monte Carlo methods) for each alternative being considered. In this paper we describe a statistically sound method which allows designers to sample the minimum number of samples necessary to eliminate dominated design alternatives under uncertainty. Dominance is evaluated using the appropriate hypothesis testing with specified confidence for a small sample, and the sample incrementally increased until dominance conditions can be determined. The method is demonstrated in the context of a tradeoff study for an automobile transmission.

Application of Ultrasound Elastography for Assessing Intestinal Fibrosis in Inflammatory Bowel Disease: Fiction or Reality?

2020 ◽  
Vol 21 ◽  
Author(s):  
Roberto Gabbiadini ◽  
Eirini Zacharopoulou ◽  
Federica Furfaro ◽  
Vincenzo Craviotto ◽  
Alessandra Zilli ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Sample Size ◽  
Bowel Disease ◽  
Small Sample Size ◽  
Shear Wave Elastography ◽  
Small Sample ◽  
Ultrasound Elastography ◽  
Invasive Technique ◽  
Intestinal Fibrosis ◽  
Inflammatory Bowel

Background: Intestinal fibrosis and subsequent strictures represent an important burden in inflammatory bowel disease (IBD). The detection and evaluation of the degree of fibrosis in stricturing Crohn’s disease (CD) is important to address the best therapeutic strategy (medical anti-inflammatory therapy, endoscopic dilation, surgery). Ultrasound elastography (USE) is a non-invasive technique that has been proposed in the field of IBD for evaluating intestinal stiffness as a biomarker of intestinal fibrosis. Objective: The aim of this review is to discuss the ability and current role of ultrasound elastography in the assessment of intestinal fibrosis. Results and Conclusion: Data on USE in IBD are provided by pilot and proof-of-concept studies with small sample size. The first type of USE investigated was strain elastography, while shear wave elastography has been introduced lately. Despite the heterogeneity of the methods of the studies, USE has been proven to be able to assess intestinal fibrosis in patients with stricturing CD. However, before introducing this technique in current practice, further studies with larger sample size and homogeneous parameters, testing reproducibility, and identification of validated cut-off values are needed.

Sign in / Sign up

Export Citation Format

Share Document