Acceptance Sampling Plans: Size Biased Lomax Model

Double Acceptance Sampling Plans Based on Truncated Life Tests for Marshall-Olkin Extended Lomax Distribution

Visualising and Assessing Acceptance Sampling Plans: The R Package AcceptanceSampling

This paper has been published in the Journal of Statistical Software (Kiermeier 2008) and describes the framework for creating and visualising acceptance sampling plans. Manufacturers and government agencies frequently use acceptance sampling to decide whether a lot from a supplier or exporting country should be accepted or rejected. International standards on acceptance sampling provide sampli...

A DEA-bases Approach for Multi-objective Design of Attribute Acceptance Sampling Plans

Acceptance sampling (AS), as one of the main fields of statistical quality control (SQC),involves a system of principles and methods to make decisions about accepting or rejecting alot or sample. For attributes, the design of a single AS plan generally requires determination ofsample size, and acceptance number. Numerous approaches have been developed foroptimally selection of design parameters...

Title : Design of Reliability Acceptance Sampling Plans Based on Accelerated Degradation Tests Design of Reliability Acceptance Sampling Plans Based on Accelerated Degradation Tests

Reliability acceptance sampling plans (RASPs) are used to determine the acceptability of a collection of units (e.g., a lot) with respect to lifetime. Most of the previous works on RASPs assume that life tests are conducted at the use or accelerated conditions. For high-reliability units, however, adopting life tests may easily turn out to be impractical in terms of the sample size and the amou...

Temporally-Biased Sampling for Online Model Management

To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily, with inclusion probabilities for a given data item decaying exponentially over time. We then periodically retrain the models on the current sample. This approach speeds up the training process relative to training on...