کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6868866 1440037 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Data-driven kernel representations for sampling with an unknown block dependence structure under correlation constraints
ترجمه فارسی عنوان
نمایش داده های هسته ای با داده ها برای نمونه گیری با ساختار وابستگی بلوک ناشناخته تحت محدودیت های همبستگی
کلمات کلیدی
برآورد تراکم هسته، پهنای باند مطلوب، نمایش غیر پارامتری، نمونه برداری مبتنی بر داده ها،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
The multidimensional Gaussian kernel-density estimation (G-KDE) is a powerful tool to identify the distribution of random vectors when the maximal information is a set of independent realizations. For these methods, a key issue is the choice of the kernel and the optimization of the bandwidth matrix. To optimize these kernel representations, two adaptations of the classical G-KDE are presented. First, it is proposed to add constraints on the mean and the covariance matrix in the G-KDE formalism. Secondly, it is suggested to separate in different groups the components of the random vector of interest that could reasonably be considered as independent. This block by block decomposition is carried out by looking for the maximum of a cross-validation likelihood quantity that is associated with the block formation. This leads to a tensorized version of the classical G-KDE. Finally, it is shown on a series of examples how these two adaptations can improve the nonparametric representations of the densities of random vectors, especially when the number of available realizations is relatively low compared to their dimensions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 119, March 2018, Pages 139-154
نویسندگان
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