Abstran In this paper we discuss the problem of estimating population means and ratios of population means using supplementary information on an auxiliary variable. Two classes of estimators are prcposed, depending on two parameters. The bias and mean square error of each of the involved estimators is obtained to the h t order of approximation. It is shown that, with a proper choice of the values for the parameters, the estimators are more efficient than the conventional estimators. Numerical examples are provided.

In a paper by SRIVENKATARAMANA and TRACY 141, four methods of estimating a population total Y with the use of an auxiliary variable were introduced, given a random sample without replacement from that population. These methods were "built around the idea that estimating the population total is essentially equivalent to estimating the total corresponding to the non-sample units, since that corresponding to the sample units is known once the sample is drawn and measurements are made on it." However, in the case of small sampling fractions the nonsample units constitute most of the population and no great improvement over the traditional estimators is to be expected. Therefore the methods are compared with tlie existing estimators and it turns out that they are special cases of the "mixing estimators", introduced in this paper. The latter estimators can be made asymptotically equivalent to tlic regression estimator and are therefore asymptotically superior to all other estimators. An exact comparison is carried out on the artificial example given in 141. The statement in this paper that "the proposed estimators are t o be prefcrred to the regression estimator for ... superiority of perforniance in tlie case of small samples" is evidently misleading. Finally a comparison is made with other "mixing-type" estimators, that can prove very useful in practice.

In this paper the coupling of a sensor to an acoustic medium is discussed. Based on the acoustic reciprocity theorem, an expression is derived for the motion of the sensor as a function of the undisturbed motion of the embedding medium. What is special here is that the sensor is the scattering object. The sensor coupling is affected by two factors: the ratio of the density of the sensor and of the embedding medium (ground), and a frequency-dependent factor depending on the geometry of the sensor.

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