We introduce an unbiased two-parameter estimator based on prior information and two-parameter estimator proposed by Özkale and Kaçıranlar, 2007. Then we discuss its properties and our results show that the new estimator is better than the two-parameter estimator, the ordinary least squares estimator, and explain the almost unbiased two-parameter estimator which is proposed by Wu and Yang, 2013....

We consider the problem of estimating the variance of a population using judgment post-stratification. By conditioning on the observed vector of ordered instratum sample sizes, we develop a conditionally unbiased nonparametric estimator that outperforms the sample variance except when the rankings are very poor. This estimator also outperforms the standard unbiased nonparametric variance estima...

We investigate a one-parameter family of probability densities (related to the Pareto distribution, which describes many natural phenomena) where the Cramér-Rao inequality provides no information. 1. Cramér-Rao Inequality One of the most important problems in statistics is estimating a population parameter from a finite sample. As there are often many different estimators, it is desirable to be...

Gene diversity, or expected heterozygosity (H), is a common statistic for assessing genetic variation within populations. Estimation of this statistic decreases in accuracy and precision when individuals are related or inbred, due to increased dependence among allele copies in the sample. The original unbiased estimator of expected heterozygosity underestimates true population diversity in samp...

 This paper considers the problem of estimating the population mean Ybar of the study variate Y using information on different parameters such as population mean $(bar{X})$, coefficient of variation $(C_x)$, kurtosis  $beta_{2(x)}$, standard deviation $(S_x)$ of the auxiliary variate x and on the correlation coefficient, $rho$, between the study variate $Y$ and the auxiliary variate $...

We derive mean-unbiased estimators for the structural parameter in instrumental variables models where the sign of one or more first stage coefficients is known. In the case with a single instrument, the unbiased estimator is unique. For cases with multiple instruments we propose a class of unbiased estimators and show that an estimator within this class is efficient when the instruments are st...