6,725 research outputs found
A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics
Efron (1979) introduced the bootstrap method for independent data but it cannot be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is related to the block size which is difficult to select. In the moving block bootstrap method also the variance estimator is underestimated. In this paper, first the semi-parametric bootstrap is used to estimate the precision measures of estimators in spatial data analysis. In the semi-parametric bootstrap method, we use the estimation of the spatial correlation structure. Then, we compare the semi-parametric bootstrap with a moving block bootstrap for variance estimation of estimators in a simulation study. Finally, we use the semi-parametric bootstrap to analyze the coal-ash data
Computer-intensive rate estimation, diverging statistics and scanning
A general rate estimation method is proposed that is based on studying the
in-sample evolution of appropriately chosen diverging/converging statistics.
The proposed rate estimators are based on simple least squares arguments, and
are shown to be accurate in a very general setting without requiring the choice
of a tuning parameter. The notion of scanning is introduced with the purpose of
extracting useful subsamples of the data series; the proposed rate estimation
method is applied to different scans, and the resulting estimators are then
combined to improve accuracy. Applications to heavy tail index estimation as
well as to the problem of estimating the long memory parameter are discussed; a
small simulation study complements our theoretical results.Comment: Published in at http://dx.doi.org/10.1214/009053607000000064 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Stable marked point processes
In many contexts such as queuing theory, spatial statistics, geostatistics
and meteorology, data are observed at irregular spatial positions. One model of
this situation involves considering the observation points as generated by a
Poisson process. Under this assumption, we study the limit behavior of the
partial sums of the marked point process , where X(t) is a
stationary random field and the points t_i are generated from an independent
Poisson random measure on . We define the sample
mean and sample variance statistics and determine their joint asymptotic
behavior in a heavy-tailed setting, thus extending some finite variance results
of Karr [Adv. in Appl. Probab. 18 (1986) 406--422]. New results on subsampling
in the context of a marked point process are also presented, with the
application of forming a confidence interval for the unknown mean under an
unknown degree of heavy tails.Comment: Published at http://dx.doi.org/10.1214/009053606000001163 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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