Taylor linearization is a popular method of variance estimation for complex statistics such as ratio and regression estimators and logistic regression coefficient estimators. It is generally applicable to any sampling design that permits unbiased variance estimation for linear estimators, and it is computationally simpler than a resampling method such as the jackknife. However, it can lead to m...

The negative hypergeometric distribution is of interest in applications of inverse sampling without replacement from a finite population where a binary observation is made on each sampling unit. Thus, sampling is performed by randomly choosing units sequentially one at a time until a specified number of one of the two types is selected for the sample. Assuming the total number of units in the p...

This study develops a Gaussian mixture rainfall-rate estimator (GMRE) for polarimetric radar-based rainfall-rate estimation, following a general framework based on the Gaussian mixture model and Bayes least squares estimation for weather radar–based parameter estimations. The advantages of GMRE are 1) it is a minimum variance unbiased estimator; 2) it is a general estimator applicable to differ...

We consider the problem of the estimation of the invariant distribution function of an ergodic diffusion process when the drift coefficient is unknown. The empirical distribution function is a natural estimator which is unbiased, uniformly consistent and efficient in different metrics. Here we study the properties of optimality for an other kind of estimator recently proposed. We consider a cla...

COMPARING THE STRUCTURAL COMPONENTS VARIANCE ESTIMATOR AND USTATISTICS VARIANCE ESTIMATOR WHEN ASSESSING THE DIFERENCE BETWEEN CORRELATED AUCs WITH FINITE SAMPLES By Anna Leigh Bosse, M.S. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2017 Major Director: Dr. Le Kang, Assoc...

The classical unbiasedness condition utilized e.g. by the best linear unbiased estimator (BLUE) is very stringent. By softening the ”global” unbiasedness condition and introducing component-wise conditional unbiasedness conditions instead, the number of constraints limiting the estimator’s performance can in many cases significantly be reduced. In this paper we extend the findings on the compon...

In this paper, we present that how much the variances of the classical estimators, namely, maximum likelihood estimator and moment estimator deviate from the minimum variance bound while estimating for the Maxwell distribution. We also sketch this difference for the negative integer moment estimator. We note the poor performance of the negative integer moment estimator in the said consideration...