Sampling Error Estimation in Stratified Surveys

Estimation of Fuzzy Error Matrix Accuracy Measures Under Stratified Random Sampling

A fuzzy error matrix may be used to summarize accuracy assessment information when both the map and reference data are labelled using a soft classification. Accuracy measures analogous to the familiar overall, user’s, and producer’s accuracies of a hard classification can be derived from a fuzzy error matrix. The formulas for estimating the fuzzy error matrix and accompanying accuracy measures ...

Mathematical Programming on Multivariate Calibration Estimation in Stratified Sampling

Calibration estimation is a method of adjusting the original design weights to improve the survey estimates by using auxiliary information such as the known population total (or mean) of the auxiliary variables. A calibration estimator uses calibrated weights that are determined to minimize a given distance measure to the original design weights while satisfying a set of constraints related to ...

Machine Estimation of Timber Volumes for Use in Sampling Surveys

Replication Variance Estimation for Two-Phase Stratified Sampling

In two-phase sampling, the second-phase sample is often a stratified sample based on the information observed in the first-phase sample. For the total of a population characteristic, either the double-expansion estimator or the reweighted expansion estimator can be used. Given a consistent first-phase replication variance estimator, we propose a consistent variance estimator that is applicable ...

Progressive Sampling for Association Rules Based on Sampling Error Estimation

We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. SEE has two advantages over previous works in the literature. First, SEE is highly efficient because an appropriate sample size can be determined without the need of executing association rules. Second, the identified ...