A stratified random sampling method is preferred for selecting varied populations with outliers. As opposed to plain sampling, increases statistical precision by reducing estimator variance. Before the estimator's variance, stratum boundary identification and data apportionment must be solved. In this study, a Neyman allocation strategy used address determination issue in mixed populations. add...

The study is investigated on an alternative method for the construction of strata using sensitivity level when samples are selected with simple random sampling replacement (SRSWR) and data collected by scrambled optional randomization technique sensitive characters. Thus, randomized response model , where k a variable having value 1 if 0 otherwise, was considered finding out Approximate Optimum...

One of the primary goals of imaging spectrometry in earth remote sensing applications is to determine identities and abundances of surface materials. In a recent study, an orthogonal subspace projection (OSP) was proposed for image classification. However, it was developed for an a priori linear spectral mixture model which did not take advantage of a posteriori knowledge of observations. In th...

These are some notes on a very simple comparative introduction to four basic approaches of statistical inference—Fisher, Neyman–Pearson, Fisher/Neyman–Pearson hybrid, and Bayes—from a course on Quantitative & Statistical Reasoning at OU in Fall 2016. In particular, I hope to give a rough understanding of the differences between the frequentist and Bayesian paradigms, though they are not entirel...

A statistically significant research finding should not be defined as a P-value of 0.05 or less, because this definition does not take into account study power. Statistical significance was originally defined by Fisher RA as a P-value of 0.05 or less. According to Fisher, any finding that is likely to occur by random variation no more than 1 in 20 times is considered significant. Neyman J and P...