This article develops a method of ratio estimate in fuzzy sense. By both the simple random sampling and stratified random sampling, we can obtain the ratio estimate in usual statistical sense. However, the sampling data may be ambiguous in some uncertain circumstance. To solve such kind of problem, we probe into the simple random sampling and stratified random sampling in fuzzy sense, obtain th...

Supporting sampling in the presence of joins is an important problem in data analysis. Pushing down the sampling operator through both sides of the join is inherently challenging due to data skew and correlation issues between output tuples. Joining simple random samples of base relations typically leads to results that are non-random. Current solutions to this problem perform biased sampling o...

The office practices of 918 physicians selected through stratified random sampling from the College of Physicians and Surgeons of Ontario (CPSO) registry were assessed by peers and the Peer Assessment Committee of the CPSO from 1981 to 1985. The sample comprised 662 general practitioners (GPs) and family physicians (FPs) and 256 specialists in 11 fields. Of the physicians 749 (82%) had neither ...

Systematic sampling is a commonly used technique due to its simplicity and ease of implementation. The drawback of this simplicity is that it is not possible to estimate the design variance without bias. There are several ways to circumvent this problem. One method is to suppose that the variable of interest has a random order in the population, so the sample variance of simple random sampling ...

I. Reconstruction functions: Demonstration code: 1. xout=ADMM WT(S,b,param) Demo ADMM WT.m 2. xout=ADMM WT CT(S,b,param) Demo ADMM WT CT.m 3. xout=ADMM outer(S,b) Demo Multiscale ADMM WT CT.m. II. Sampling functions: Demonstration code: 1. S = Oracle Random Sampling( x0, sp ) Demo Oracle Random Sampling.m 2. S = Oracle Random Sampling with PCA( x0, sp, Spilot ) Demo Oracle Random Sampling with ...

These methods include confidence intervals and contingency tests (including multinomial tests), whether computed by Fisher’s exact method or variants of log-likelihood, χ 2 , or the Wilson score interval (Wallis 2013). These methods are also at the core of others. The Normal approximation to the Binomial allows us to compute a notion of the variance of the distribution, and is to be found in li...

When properly applied, stereology is a very robust and efficient method to quantify a variety of parameters from biological material. A common sampling strategy in stereology is systematic random sampling, which involves choosing a random sampling [corrected] start point outside the structure of interest, and sampling relevant objects at [corrected] sites that are placed at pre-determined, equi...