A new method for sampling from a finite population that is spread in one, two or more dimensions is presented. Weights are used to create strong negative correlations between the inclusion indicators of nearby units. The method can be used to produce unequal probability samples that are well spread over the population in every dimension, without any spatial stratification. Since the method is v...

At the beginning of 20th century, there was an active debate about random selection units versus purposive groups for survey samples. Neyman’s (1934) paper tilted balance strongly towards varieties probability sampling combined with design-based inference, and most national statistical offices have adopted this method their major surveys. However, nonprobability has remained in widespread use m...

In this study, we propose an efficient numerical simulation method for structural systems with both epistemic and aleatory uncertainties to evaluate the effect of epistemic uncertainty on the failure probability measured by variance-based sensitivity analysis. The direct evaluation of this effect requires a ‘‘triple-loop’’ crude sampling procedure, which is time consuming. To circumvent the dif...

1. What is a Survey? 2. Probability sampling 3. Common probability sampling designs 3.1. Simple Random Sampling 3.2. Stratified Sampling 3.3. Cluster Sampling 3.4. Unequal Probability Sampling 3.5. Systematic Sampling 3.6. Stratified Multistage Sampling 4. Survey estimates and standard errors 5. Nonsampling errors 6. Sampling rare populations 7. Issues in Survey Design Acknowledgments Glossary ...

Abstract Sample designs are typically developed to estimate summary statistics such as means, proportions and prevalences. Analytical outputs may also be a priority but there fewer methods results on how efficiently design samples for the fitting estimation of statistical models. This paper develops general approach determining efficient sampling probability-weighted maximum likelihood estimato...

1 Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barr...