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We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials. Subjects arrive iteratively and are either ran...
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
An efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non-synchronous observations under high...
In some socio-economic surveys, data are collected on sensitive or stigmatizing issues such as tax evasion, criminal conviction, drug use, etc. In such surveys, direct questioning of respondents is no...
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to...
We estimate linear functionals in the classical deconvolution problem by kernel esti-mators. We obtain a uniform central limit theorem with √n–rate on the assumption that the smoothness of the functio...
We construct integral and supremum type goodness-of-fit tests for the family of power distribution functions. Test statistics are functionals of U−empirical processes and are based on the classi...
It is proved that the information divergence statistic is infinitely more Bahadur efficient than the power divergence statistics of the orders > 1 as long as the sequence of alternatives is conti...
Ordinary differential equations (ODEs) are commonly used to model dynamic behavior of a system. Because many parameters are unknown and have to be estimated from the observed data, there is growing...
There is a growing interest in the literature for adaptive Markov Chain Monte Carlo methods based on sequences of random transition kernels {Pn} where the kernel Pn is allowed to have an invariant d...
In the paper, a sequential confidence set based on an estimation process of a multivariate parameter is constructed. Under the assumption that the estimation process scaled by an increasing positiv...
Asymptotic Efficiency of Method of Moments Estimators Under Null Intercept Measurement Error Regression Models。
We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an ``optimal'' target process via a learning procedure. We show, under appropriate conditions, that the ...
This paper is concerned with parameter estimation in the presence of nuisance parameters. Usually, an estimator with known nuisance parameters is better than that with unknown nuisance parameters in r...
Barndorff-Nielsen and Cox (1994, p.319) modify an estimative prediction limit to obtain an improved prediction limit with better coverage properties. Kabaila and Syuhada (2008) present a simulation-...

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