A Sometimes-Pool Estimator of the Mean Life

The paper deals with the problem of estimating the mean life θ in an exponential model \documentclass{article}\pagestyle{empty}\begin{document}$f(x/\theta ) = \frac{1}{\theta }e - x/\theta$\end{document}. It is assumed that in addition to the current ordered sample, we have a sample collected sometime in the recent past when the mean life might have been β. We have proposed a Sometimes—Pool procedure which is based on the outcome of a preliminary test of H₀: θ=β and obtained the expressions of the bias and MSE. An attempt has been made to locate that region in the parameter space in which the proposed estimator does better (in MSE sense) than the usual estimator based only on the current sample.     

An Improved Class of Estimators for Finite Population Mean in Sample Surveys

This paper proposes a class of estimators for estimating the finite population mean -Y of a study variate y using information on two auxiliary variates, one of which is positively and the other negatively correlated with the study variate y. An “asymptotically optimum estimator” (AOE) in the class is identified with its bias and mean square error formulae. It is observed that the proposed AOE is more efficient than Srivastava (1965), Srivastava (1974), Prasad (1989) and Gandge , Varghese , and Prabhu-Ajgaonkar (1993) estimators.     

Edge-corrected Estimators for the Reduced Second Moment Measure of Point Processes

A new edge-corrected estimator is proposed for the second moment cumulative function K(t) introduced by Ripley (1977, Journal of the Royal Statistical Society, Series B 39, 172–212). This new estimator is compared by simulation methods with existing edge-corrected estimators in the context of both K(t) and L(t) functions which are used to study point patterns. The results of the simulation study suggests that the new estimator provides almost unbiased estimates of K(t) and L(t) and has a smaller mean squared error than its predecessors.     

On Estimation of Relative Risk in Case Control Studies

An estimator of relative risk in a case control study has been proposed in terms of observed cell frequencies and the probability of disease. The bias of the usual estimator i.e odds ratio as compared to the new estimator has been workedout. The expression of Mean Square Error of proposed estimator has been derived in situations where probability of disease is exactly known and when it is estimated through an independent survey. It has been observed that there is a serious error using odds ratio as an estimate of relative risk when probability of disease is not negligible. In such situations the proposed estimator can be used with advantage.     

On blocking rules for the bootstrap with dependent data

We address the issue of optimal block choice in applicationsof the block bootstrap to dependent data. It is shown that optimalblock size depends significantly on context, being equal ton^1/3, n^1/4 and n^1/5 in the cases of variance or bias estimation,estimation of a onesided distribution function, and estimationof a two-sided distribution function, respectively. A clearintuitive explanation of this phenomenon is given, togetherwith outlines of theoretical arguments in specific cases. Itis shown that these orders of magnitude of block sizes can beused to produce a simple, practical rule for selecting blocksize empirically. That technique is explored numerically.