Journal of the Iranian Statistical Society
http://jirss@irstat.ir
Journal of The Iranian Statistical Society - Journal articles for year 2018, Volume 17, Number 2Yektaweb Collection - https://yektaweb.comen2018/12/10Generalized Family of Estimators for Imputing Scrambled Responses
http://jirss.irstat.ir/browse.php?a_id=441&sid=1&slc_lang=en
<p>When there is a high correlation between the study and the auxiliary variables, the rank of the auxiliary variable also correlates with the study variable. Then, the use of the rank as an additional auxiliary variable may be helpful to increase the efficiency of the estimator of the mean or total of the population. In the present study, we propose two generalized families of estimators for imputing scrambling response by using the variance and rank of the auxiliary variable. Expressions for bias and mean squared error are obtained up to the first order of approximation. A numerical study is carried out to observe the performance of estimators. </p>
Muhammad Umair SohailImproved Cramer-Rao Inequality for Randomly Censored Data
http://jirss.irstat.ir/browse.php?a_id=472&sid=1&slc_lang=en
As an application of the improved Cauchy-Schwartz inequality due to Walker (Statist. Probab. Lett. (2017) 122:86-90), we obtain an improved version of the Cramer-Rao inequality for randomly censored data derived by Abdushukurov and Kim (J. Soviet. Math. (1987) pp. 2171-2185). We derive a lower bound of Bhattacharya type for the mean square error of a parametric function based on randomly censored data.BLS PrakasaraoStochastic Models for Pricing Weather Derivatives using Constant Risk Premium
http://jirss.irstat.ir/browse.php?a_id=465&sid=1&slc_lang=en
<p style="margin: 0px;">‎Pricing weather derivatives is becoming increasingly useful‎, ‎especially in developing economies‎. ‎We describe a statistical model based approach for pricing weather derivatives by modeling and forecasting daily average temperatures data which exhibits long-range dependence‎. ‎We pre-process the temperature data by filtering for seasonality and volatility and fit autoregressive fractionally integrated moving average (ARFIMA) models‎, ‎employing the preconditioned conjugate gradient (PCG) algorithm for fast computation of the likelihood function‎. ‎We illustrate our approach using daily temperatures data from 1970 to 2008 for cities traded on the Chicago Mercantile Exchange (CME)‎, ‎which we employ for pricing degree days futures contracts‎. ‎We compare the statistical approach with traditional burn analysis using a simple additive risk loading principle for pricing‎, ‎where‎ ‎the risk premium is estimated by the method of least squares using data on observed prices and the corresponding estimate of prices from the best model we fit to the temperatures data.</p>
Nalini Ravishanker