[7] developed an EWMA scheme using

ratio estimator to increase the effectiveness of typical EWMA chart in monitoring the location parameter.

A fundamental model for this approach is the use of the new semiparametric

ratio estimator (SPRE) as a single-subject statistical and small data science model to define, analyze, graph, and predict occupational therapy data to provide a pathway to meaningful treatment.

The results showed that the proposed exponential

ratio estimator was more efficient compared to the usual sample variance estimator and the proposed ratio type variance estimators in adaptive cluster sampling, assuming given conditions.

A random sample with a

ratio estimator (or an estimator that uses the relationship between two variables) was determined to be preferable to the blow-up estimator for most industries because it required a smaller sample size to achieve the same level of sampling error.

Using auxiliary information Sisodia and Dwivedi [2] proposed

ratio estimator using the known knowledge coefficient of variation of an auxiliary variable, Kadilar and Cingi [1] suggested a class of

ratio estimators for the finite population mean, including Upadhyaya and Singh [3] , Singh [4], Singh and Tailor [5], Singh et al.

To estimate the total gas sales and deliveries for the State, a

ratio estimator is applied to the reported volumes in each State for the sampled companies.

Taking into account non-normality in the returns, especially skewness and kurtosis, can have significant effect on the standard error of the Sharpe

ratio estimator. The tables below contain the percentage variation in the standard error of the Sharpe ratio compared to normality (bold number at SK=0 and K=3) given by (5):

A

ratio estimator is applied to the volumes reported in each State by the sampled companies to estimate the total gas sales and deliveries for the State.

Because bycatch is typically discarded at sea, total bycatch is usually estimated by extrapolating from an observed bycatch sample to the entire fleet with either mean-per-unit or

ratio estimators, Using both field observations of commercial shrimp trawlers and computer simulations, I compared five methods for generating bycatch estimates that were used in past studies, a mean per-unit estimator and four forms of the

ratio estimator, respectively: 1) the mean fish catch per unit of effort, where unit effort was a proxy for sample size, 2) the mean of the individual fish to shrimp ratios, 3) the ratio of mean fish catch to mean shrimp catch, 4) the mean of the ratios offish catch per time fished (a variable measure of effort), and 5) the ratio of mean fish catch per mean time fished.

However, because of the prevalence of ratio estimation, we did examine the performance of confidence intervals based on the

ratio estimator and several alternative estimators of its sampling variance that have been proposed.

Loadings were calculated with the Beale

Ratio estimator (Ontario Ministry of the Environment 1986) or the NAQUADAT method, as referenced in the individual studies.

The mean square errors for the

ratio estimator YRd and product estimator Y are given respectively as