scholarly journals Stochastic comparisons of stratified sampling techniques for some Monte Carlo estimators

Bernoulli ◽  
2011 ◽  
Vol 17 (2) ◽  
pp. 592-608 ◽  
Author(s):  
Larry Goldstein ◽  
Yosef Rinott ◽  
Marco Scarsini
Keyword(s):  
Monte Carlo ◽  
Stratified Sampling ◽  
Sampling Techniques ◽  
Stochastic Comparisons

scholarly journals Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2328
Author(s):  
Mohammed Alzubaidi ◽  
Kazi N. Hasan ◽  
Lasantha Meegahapola ◽  
Mir Toufikur Rahman
Keyword(s):  
Monte Carlo ◽  
Stability Analysis ◽  
Power Systems ◽  
Large Scale ◽  
Voltage Stability ◽  
Sampling Technique ◽  
Coefficient Of Determination ◽  
Sampling Techniques ◽  
Quasi Monte Carlo ◽  
Efficient Sampling

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

An application of stratified sampling techniques for research data

1994 ◽  
Vol 3 (4) ◽  
pp. 237-247 ◽  
Author(s):  
Mary Schroeder Doucet ◽  
David A. Bridge ◽  
Richard A. Grimlund ◽  
Adil E. Shamoo
Keyword(s):  
Research Data ◽  
Stratified Sampling ◽  
Sampling Techniques

scholarly journals Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1906 ◽  
Author(s):  
Mohamed Ibrahim ◽  
Saad Al-Sobhi ◽  
Rajib Mukherjee ◽  
Ahmed AlNouss
Keyword(s):  
Monte Carlo ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Average Error ◽  
Illustrative Case ◽  
Sampling Techniques ◽  
Latin Hypercube ◽  
Process Data ◽  
Process Simulator

Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input–output data obtained from a process simulator. To enhance the model robustness, proper sampling techniques are required to cover the entire domain of the process variables uniformly. In the present work, Monte Carlo with pseudo-random samples as well as Latin hypercube samples and quasi-Monte Carlo samples with Hammersley Sequence Sampling (HSS) are generated. The sampled data obtained from the process simulator are fitted to neural networks for generating a surrogate model. An illustrative case study is solved to predict the gas stabilization unit performance. From the developed surrogate models to predict process data, it can be concluded that of the different sampling methods, Latin hypercube sampling and HSS have better performance than the pseudo-random sampling method for designing the surrogate model. This argument is based on the maximum absolute value, standard deviation, and the confidence interval for the relative average error as obtained from different sampling techniques.

scholarly journals Investigating the construct validity of an electronic in-basket exercise using bias-corrected bootstrapping and Monte Carlo re-sampling techniques

2019 ◽  
Vol 45 ◽  
Author(s):  
Jurgen Becker ◽  
Deon Meiring ◽  
Jan H. Van der Westhuizen
Keyword(s):  
Monte Carlo ◽  
Construct Validity ◽  
Model Parameters ◽  
Sampling Techniques ◽  
Convenience Sample ◽  
Use Of Technology ◽  
Ex Post ◽  
Managerial Implications ◽  
Computer Based ◽  
Value Add

Orientation: Technology-based simulation exercises are popular assessment measures for the selection and development of human resources.Research purpose: The primary goal of this study was to investigate the construct validity of an electronic in-basket exercise using computer-based simulation technology. The secondary goal of the study was to investigate how re-sampling techniques can be used to recover model parameters using small samples.Motivation for the study: Although computer-based simulations are becoming more popular in the applied context, relatively little is known about the construct validity of these measures.Research approach/design and method: A quantitative ex post facto correlational design was used in the current study with a convenience sample (N = 89). The internal structure of the simulation exercise was assessed using a confirmatory factor analytical approach. In addition, bias-corrected bootstrapping and Monte Carlo simulation strategies were used to assess the confidence intervals around model parameters.Main findings: Support was not found for the entire model, but only for one of the dimensions, namely, the Interaction dimension. Multicollinearity was found between most of the dimensions that were problematic for factor analyses.Practical/managerial implications: This study holds important implications for assessment practitioners who hope to develop unproctored simulation exercises.Contribution/value-add: This study aims to contribute to the existing debate regarding the validity and utility of assessment centres (ACs), as well as to the literature concerning the use of technology-driven ACs. In addition, the study aims to make a methodological contribution by demonstrating how re-sampling techniques can be used in small AC samples.

EFFECT OF TAX PLANNING ON PERFORMANCE OF NIGERIAN FOODS AND BEVERAGE FIRMS

2021 ◽  
pp. 8-16
Author(s):  
Jacinta Ogochukwu ◽  
Obada Paradise
Keyword(s):  
Regression Analysis ◽  
Firm Performance ◽  
Tax Planning ◽  
Stratified Sampling ◽  
Sampling Techniques ◽  
Effective Tax Rate ◽  
Tax Rate ◽  
Ex Post ◽  
Ex Post Facto

The study examined the effect of tax planning on performance of Nigerian corporate firms. This study employed ex post facto design. The population for this study is directed to foods and beverage firms in Nigeria. Simple and stratified sampling techniques were employed to select six foods and beverage firms in Nigeria. Regression analysis was used to test the hypothesis with aid of E-view 9.0. From the result of the analysis, Effective Tax Rate (ETR) has no significant effect on performance of Nigerian foods and beverage firms. This means that effective tax plan to generate firm performance; firms with high tax planning value perform better. On this note, the researchers recommended that Nigerian firms should engage the services of tax consultants in managing their tax computations and remittances.

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