All a b c d e f g h i j k l m n o p q r s t u v w x y z Other

Previous Page 5 Next

Displaying 81 – 100 of 246

Showing per page

Information inequalities for the minimax risk of sequential estimators (with applications)

Lesław Gajek, B. Mizera-Florczak (1998)

Applicationes Mathematicae

Information inequalities for the minimax risk of sequential estimators are derived in the case where the loss is measured by the squared error of estimation plus a linear functional of the number of observations. The results are applied to construct minimax sequential estimators of: the failure rate in an exponential model with censored data, the expected proportion of uncensored observations in the proportional hazards model, the odds ratio in a binomial distribution and the expectation of exponential...

Inverse sampling and triangular sequential designs to compare a small proportion with a reference value.

Víctor Moreno, Isaac Martín, Ferrán Torres, Manuel Horas, José Ríos, Juan R. González (2002)

Qüestiió

Inverse sampling and formal sequential designs may prove useful in reducing the sample size in studies where a small population proportion p is compared with a hypothesized reference proportion p0. These methods are applied to the design of a cytogenetic study about chromosomal abnormalities in men with a daughter affected by Turner's syndrome. First it is shown how the calculated sample size for a classical design depends on the parameterization used. Later this sample size is compared with the...

Las medidas de f*-divergencia en el diseño secuencial de experimentos en un contexto bayesiano.

Domingo Morales, Leandro Pardo, Vicente Quesada (1986)

Trabajos de Estadística

Se presenta un método de selección secuencial de un número fijo de experimentos a partir de las medidas de f*-divergencia introducidas por Csiszar (1967). Este trabajo es similar al desarrollado por De Groot (1970) con funciones de incertidumbre; sin embargo, no sólo se considera el problema de espacio paramétrico finito, sino que se estudia además el caso de espacio paramétrico infinito.

Learning extremal regulator implementation by a stochastic automaton and stochastic approximation theory

Ivan Brůha (1980)

Aplikace matematiky

There exist many different approaches to the investigation of the characteristics of learning system. These approaches use different branches of mathematics and, thus, obtain different results, some of them are too complicated and others do not match the results of practical experiments. This paper presents the modelling of learning systems by means of stochastic automate, mainly one particular model of a learning extremal regulator. The proof of convergence is based on Dvoretzky's Theorem on stochastic...

Minimum variance importance sampling via Population Monte Carlo

R. Douc, A. Guillin, J.-M. Marin, C. P. Robert (2007)

ESAIM: Probability and Statistics

Variance reduction has always been a central issue in Monte Carlo experiments. Population Monte Carlo can be used to this effect, in that a mixture of importance functions, called a D-kernel, can be iteratively optimized to achieve the minimum asymptotic variance for a function of interest among all possible mixtures. The implementation of this iterative scheme is illustrated for the computation of the price of a European option in the Cox-Ingersoll-Ross model. A Central Limit theorem as well...

Note on stability estimation in sequential hypothesis testing

E. Gordienko, J. Ruiz de Chávez, A. García (2013)

Applicationes Mathematicae

We introduce a quantitative measure Δ of stability in optimal sequential testing of two simple hypotheses about a density of observations: f=f₀ versus f=f₁. The index Δ represents an additional cost paid when a stopping rule optimal for the pair (f₀,f₁) is applied to test the hypothesis f=f₀ versus a "perturbed alternative" f=f̃₁. An upper bound for Δ is established in terms of the total variation distance between f₁(X)/f₀(X) and f̃₁(X)/f₀(X) with X∼f₀.

Currently displaying 81 – 100 of 246