skip to main content
Ngôn ngữ:
Giới hạn tìm kiếm: Giới hạn tìm kiếm: Dạng tài nguyên Hiển thị kết quả với: Hiển thị kết quả với: Chỉ mục

Joint adaptive mean–variance regularization and variance stabilization of high dimensional data

Dazard, Jean-Eudes ; Sunil Rao, J

Computational Statistics and Data Analysis, July 2012, Vol.56(7), pp.2317-2333 [Tạp chí có phản biện]

ISSN: 0167-9473 ; E-ISSN: 1872-7352 ; DOI: 10.1016/j.csda.2012.01.012

Truy cập trực tuyến

Phiên bản sẵn có
Trích dẫn Trích dẫn bởi
  • Nhan đề:
    Joint adaptive mean–variance regularization and variance stabilization of high dimensional data
  • Tác giả: Dazard, Jean-Eudes ; Sunil Rao, J
  • Chủ đề: Bioinformatics ; Inadmissibility ; Regularization ; Shrinkage Estimators ; Normalization ; Variance Stabilization ; Bioinformatics ; Inadmissibility ; Regularization ; Shrinkage Estimators ; Normalization ; Variance Stabilization ; Mathematics
  • Là 1 phần của: Computational Statistics and Data Analysis, July 2012, Vol.56(7), pp.2317-2333
  • Mô tả: The paper addresses a common problem in the analysis of high-dimensional high-throughput “omics” data, which is parameter estimation across multiple variables in a set of data where the number of variables is much larger than the sample size. Among the problems posed by this type of data are that variable-specific estimators of variances are not reliable and variable-wise tests statistics have low power, both due to a lack of degrees of freedom. In addition, it has been observed in this type of data that the variance increases as a function of the mean. We introduce a non-parametric adaptive regularization procedure that is innovative in that (i) it employs a novel “similarity statistic”-based clustering technique to generate local-pooled or regularized shrinkage estimators of population parameters, (ii) the regularization is done jointly on population moments, benefiting from C. Stein’s result on inadmissibility, which implies that usual sample variance estimator is improved...
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 0167-9473 ; E-ISSN: 1872-7352 ; DOI: 10.1016/j.csda.2012.01.012

Đang tìm Cơ sở dữ liệu bên ngoài...