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Afijación del tamaño de muestra en estrategias π PXM-estratificadas bajo un modelo superpoblacional.

Carlos N. Bouza (1982)

Trabajos de Estadística e Investigación Operativa

El problema de la afijación de los tamaños de muestra en estratos es abordado. Los sistemas de probabilidades desiguales se basan en una variable auxiliar Xt. La existencia de un modelo superpoblacional permite el desarrollo de criterios de afijación óptima. Las propiedades de los tamaños de muestra obtenidos son similares a los clásicos.

Almost unbiased ratio and product-type estimators in systematic sampling.

R. Singh, H. P. Singh (1998)

Qüestiió

In this paper we have suggested almost unbiased ratio-type and product-type estimators for estimating the population mean Y of the study variate y using information on an auxiliary variate x in systematic sampling. The variance expressions of the suggested estimators have been obtained and compared with usual unbiased estimator y*, Swain's (1964) ratio estimator y*R and Shukla's product estimator y*p. It has been shown that the proposed estimators are more efficient than usual unbiased estimator...

Asymptotics of variance of the lattice point count

Jiří Janáček (2008)

Czechoslovak Mathematical Journal

The variance of the number of lattice points inside the dilated bounded set r D with random position in d has asymptotics r d - 1 if the rotational average of the squared modulus of the Fourier transform of the set is O ( ρ - d - 1 ) . The asymptotics follow from Wiener’s Tauberian theorem.

Chance constrained problems: penalty reformulation and performance of sample approximation technique

Martin Branda (2012)

Kybernetika

We explore reformulation of nonlinear stochastic programs with several joint chance constraints by stochastic programs with suitably chosen penalty-type objectives. We show that the two problems are asymptotically equivalent. Simpler cases with one chance constraint and particular penalty functions were studied in [6,11]. The obtained problems with penalties and with a fixed set of feasible solutions are simpler to solve and analyze then the chance constrained programs. We discuss solving both problems...

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