KOMPARASI ESTIMASI RERATA SKOR RESPONDEN ANTARA ITEM SAMPLING, EXAMINEE SAMPLING, DAN MULTIPLE MATRIX SAMPLING

Sampling of Multiple Variables Based on Partially Ordered Set Theory

We introduce a new method for ranked set sampling with multiple criteria. The method relaxes the restriction of selecting just one individual variable from each ranked set. Under the new method for ranking, units are ranked in sets based on linear extensions in partially order set theory with considering all variables simultaneously. Results willbe evaluated by a relatively extensive simulation...

Adaptive Multiple Importance Sampling

The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earlier adaptive importance sampling implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each iteration, following the technique of...

Sampling and Sampling Distributions

A sample is a group of objects or readings taken from a population for counting or measurement. We shall distinguish between two kinds of populations — finite populations and infinite populations. The sample must be random, meaning that all possible samples of a particular size must have equal probabilities of being chosen from the population. This will prevent bias in the sampling process. Mos...

Application of adaptive sampling in fishery part 1: Adaptive cluster sampling and its strip designs

Abstract:  The precision of conventional sampling designs is not usually satisfactory for estimating parameters of clump and rare populations. Many of fish species live in school and disperse all over a vast area like a sea so that they are rare compare to their habitats. Theory of a class of sampling designs called adaptive sampling designs has rapidly grown during last decade which solv...

Sampling Distributions Sampling Distributions