New classes of difference cum-ratio-type exponential estimators for a finite population variance in stratified random sampling.

The problem of estimating the variance of a finite population is an important issue in practical situations where controlling variability is difficult. During experiments conducted in the fields of agriculture and biology, researchers often face this issue, resulting in outcomes that appear uncontrollable for the desired results. Using auxiliary information effectively has the potential to enhance the precision of estimators.

Improved modified estimator for estimation of median using auxiliary information under simple random sampling.

This article aims to suggest an improved estimator for estimation of population median using auxiliary information under simple random sampling. The expression for the bias and mean square error are obtained up to first order approximation. We determine the MLE of the optimal values of the describing scalars.

Hybrid class of robust type estimators for variance estimation using mean and variance of auxiliary variable.

This study is completed for the estimation of unknown population variance for the variable of mean and variance of interest. To accomplish this task, a new generalized class of robust kind of variance estimators proposed utilizing known descriptives of auxiliary variable, for example, Mid-range, Hodges-Lehmann Mean, Tri-mean, deciles mean, coefficient of skewness, interquartile range, first quartile, coefficient of kurtosis, semi-interquartile average, inter decile range and Mean, etc. These conventional measures of auxiliary variable improve the accuracy of the suggested class under simple random sampling without replacement (SRSWOR) scheme.

Enhanced exponential ratio-cum-ratio estimator in ranked set sampling using transformed auxiliary information.

Estimation of population mean is a determined subject issue in sampling surveys and many efforts have been paid by various researchers to enhance the precision of the estimates by utilizing the correlated auxiliary information. In connection with this, we suggest an improved exponential ratio-cum-ratio estimator using transformed auxiliary variables under ranked set sampling scheme. Theoretical comparison between estimators is made in terms of mean square errors (), percentage relative efficiencies (), and percentage relative root mean squared error ().

On the development of new cosine-based probabilistic methods with applications to univariate and bivariate analyses of the wind speed energy.

So far in the literature, a number of probability distributions have been successfully implemented for analyzing the wind speed and energy data sets. However, there is no published work on modeling and analyzing the wind speed and energy data sets with probability distributions that are introduced using trigonometric functions. In the existing literature, there is also a lack of studies on implementing the bivariate trigonometric-based probability distributions for modeling the wind speed and energy data sets.

A new trigonometric modification of the Weibull distribution: Control chart and applications in quality control.

In the most recent era, the extensions of the probability models via trigonometry methods have received great attention. This paper also offers a novel trigonometric version of the Weibull model called a type-I cosine exponentiated Weibull (for short "TICE-Weibull") distribution. The identifiability properties for all three parameters of the TICE-Weibull model are derived.

A new modification of the flexible Weibull distribution based on power transformation: Monte Carlo simulation and applications.

Statistical modeling is a crucial phase for decision-making and predicting future events. Data arising from engineering-related fields have most often complex structures whose failure rate possesses mixed state behaviors (i.e.