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In this talk, we show that the available information about unknown parameters in rare events data is only tied to the relatively small number of cases, which justifies the usage of negative sampling. ...
In big data era, subsampling or sub-data selection techniques are often adopted to extract a fraction of informative individuals from the massive data. Existing subsampling algorithms focus mainly on ...
Linear mixed model is a popular and common modeling method in statistical analysis. It is computationally difficult to obtain parameter estimates in linear mixed model for big data. The current subsam...
An integral part of many algorithms for S-estimators of linear regression is random subsam-pling. For problems with only continuous predictors simplerandom subsampling is a reliable method to generate...
The aim of this article is to establish asymptotic distributions and consistency of subsampling for spectral density and for magnitude of coherence for non-stationary, almost periodically correlated t...
This paper provides extensions of the work on subsampling by Bertail et al. (2004) for strongly mixing case to weakly dependent case by application of the results of Doukhan and Louhichi (1999).We in...
The article deals with the special subclass of cp-mixing periodically correlated (PC) time series and the estimation of autocovariance through Fourier coefficients. The aim is to investigate whethe...
In a recent paper, we analyzed the properties of a new kind of spherical wavelets (called needlets)for statistical inference procedures on spherical random fields; the investigation was mainly motivat...

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