This meta-analysis and systematic review of RCTs suggests that fewer early major and minor complications are associated with LVSG compared with LRYGB procedure. However, this does not translate into higher readmission rate, reoperation rate, or 30-day mortality for either procedure.
We provide asymptotic results for the distribution of weighted nonlinear functionals of Gaussian field with long-range dependence. We also show that integral functionals and the corresponding additive functionals have same distributions under certain assumptions. The result is applied to integrals over a multidimensional rectangle with a constant weight function.Date: 03/10/2017. 2000 Mathematics Subject Classification. Primary 60G60; Secondary 60G12, 60F05.
The paper studies the asymptotic behaviour of weighted functionals of long-range dependent data over increasing observation windows. Various important statistics, including sample means, high order moments, occupation measures can be given by these functionals. It is shown that in the discrete sampling case additive functionals have the same asymptotic distribution as the corresponding integral functionals for the continuous functional data case. These results are applied to obtain non-central limit theorems for weighted additive functionals of random fields. As the majority of known results concern the discrete sampling case the developed methodology helps in translating these results to functional data without deriving them again. Numerical studies suggest that the theoretical findings are valid for wider classes of long-range dependent data.
This article investigates general scaling settings and limit distributions of functionals of filtered random fields. The filters are defined by the convolution of non-random kernels with functions of Gaussian random fields. The case of long-range dependent fields and increasing observation windows is studied. The obtained limit random processes are non-Gaussian. Most known results on this topic give asymptotic processes that always exhibit non-negative auto-correlation structures and have the self-similar parameter H ∈ ( 1 2 , 1). In this work we also obtain convergence for the case H ∈ (0, 1 2 ) and show how the Hurst parameter H can depend on the shape of the observation windows. Various examples are presented.Keywords filtered random fields · long-range dependence · self-similar processes · non-central limit theorem · Hurst parameter Mathematics Subject Classification (2010) 60G60 · 60F17 · 60F05 · 60G35
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