We propose a jackknife variance estimator for the population average from two, two-phase samples after imputation. The jackknife method has long been used to estimate and reduce bias, but has now become a valuable tool for variance estimation. We apply two different sampling methods, (simple random sampling and stratified random sampling) to derive jackknife variance estimators for the twosampl...

This study proposes ratio estimators by adapting the estimators type ofRay and Singh [J. Ind. Stat. Assoc. 19 (1981) 147] to traditional and the other ratio-type estimators in simple random sampling in literature. Theoretically, mean square error (MSE) equations of all proposed ratio estimators are obtained and compared with each other. By these comparisons the conditions, which make each propo...

We present a simple algorithm for estimating the cardinality of a set I, based on a RandomSample(I) primitive that returns an element of I uniformly at random. Our algorithm with probability (1−perr) returns an estimate of |I| accurate within a factor (1 ± δerr) invoking RandomSample(I) at most O (

Sampling is a fundamental operation for the auditing and statistical analysis of large databases. It is not well supported in existing relational database management systems. We discuss how to obtain samples from the results of relational queries without first performing the query. Specifically, we examine simple random sampling from selections, projections, joins, unions, and intersections. We...

Recent research advocates applying sampling to accelerate microarchitecture simulation. Simple random sampling offers accurate performance estimates (with a high quantifiable confidence) by taking a large number (e.g., 10,000) of short performance measurements over the full length of a benchmark. Simple random sampling does not exploit the often repetitive behaviors of benchmarks, collecting ma...