High dimensional data analysis using multivariate generalized spatial quantiles
نویسندگان
چکیده
منابع مشابه
High dimensional data analysis using multivariate generalized spatial quantiles
High dimensional data routinely arises in image analysis, genetic experiments, network analysis, and various other research areas. Many such datasets do not correspond to well-studied probability distributions, and in several applications the data-cloud prominently displays non-symmetric and non-convex shape features. We propose using spatial quantiles and their generalizations, in particular, ...
متن کاملGeneralized Multivariate Rank Type Test Statistics via Spatial U-Quantiles
The classical univariate sign and signed rank tests for location have been extended over the years to the multivariate setting, including recent robust rotation invariant “spatial” versions. Here we introduce a broad class of rotation invariant multivariate spatial generalized rank type test statistics. For a given inference problem not restricted to location, the test statistics are linked thr...
متن کاملQuantile tomography: using quantiles with multivariate data
Directional quantile envelopes—essentially, depth contours—are a possible way to condense thedirectional quantile information, the information carried by the quantiles of projections. In typi-cal circumstances, they allow for relatively faithful and straightforward retrieval of the directionalquantiles, offering a straightforward probabilistic interpretation in terms of the tang...
متن کاملMethods for regression analysis in high-dimensional data
By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...
متن کاملMultivariate quantiles in hydrological frequency analysis
2 Several hydrological phenomena are described by two or more correlated characteristics. 3 These dependent characteristics should be considered jointly to be more representative of the 4 multivariate nature of the phenomenon. Consequently, probabilities of occurrence cannot be 5 estimated on the basis of univariate frequency analysis (FA). The quantile, representing the value 6 of the variable...
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: 2011
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2010.12.002