Bootstrapping the Studentized Sample Mean of Lattice Variables
نویسندگان
چکیده
منابع مشابه
Edgeworth Expansions for Spectral Density Estimates and Studentized Sample Mean
We establish valid Edgeworth expansions for the distribution of smoothed nonparametric spectral estimates, and of studentized versions of linear statistics such as the sample mean, where the studentization employs such a nonparametric spectral estimate+ Particular attention is paid to the spectral estimate at zero frequency and, correspondingly, the studentized sample mean, to reflect econometr...
متن کاملOptimum Block Size in Separate Block Bootstrap to Estimate the Variance of Sample Mean for Lattice Data
The statistical analysis of spatial data is usually done under Gaussian assumption for the underlying random field model. When this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. One of the crucial problems in this setting is specifying the block sizes. In this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...
متن کاملBootstrapping variables in algebraic circuits
We show that for the blackbox polynomial identity testing (PIT) problem it suffices to study circuits that depend only on the first extremely few variables. One only need to consider size-s degree-s circuits that depend on the first log◦c s variables (where c is a constant and we are composing c logarithms). Thus, hitting-set generator (hsg) manifests a bootstrapping behavior— a partial hsg aga...
متن کاملBootstrapping Errors-in-Variables Models
The bootstrap is a numerical technique, with solid theoretical foundations, to obtain statistical measures about the quality of an estimate by using only the available data. Performance assessment through bootstrap provides the same or better accuracy than the traditional error propagation approach, most often without requiring complex analytical derivations. In many computer vision tasks a reg...
متن کاملoptimum block size in separate block bootstrap to estimate the variance of sample mean for lattice data
the statistical analysis of spatial data is usually done under gaussian assumption for the underlying random field model. when this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. one of the crucial problems in this setting is specifying the block sizes. in this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 1993
ISSN: 0047-259X
DOI: 10.1006/jmva.1993.1037