| Title:
|
Kendall's tau-type rank statistics in genome data (English) |
| Author:
|
Kang, Moonsu |
| Author:
|
Sen, Pranab K. |
| Language:
|
English |
| Journal:
|
Applications of Mathematics |
| ISSN:
|
0862-7940 (print) |
| ISSN:
|
1572-9109 (online) |
| Volume:
|
53 |
| Issue:
|
3 |
| Year:
|
2008 |
| Pages:
|
207-221 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
High-dimensional data models abound in genomics studies, where often inadequately small sample sizes create impasses for incorporation of standard statistical tools. Conventional assumptions of linearity of regression, homoscedasticity and (multi-) normality of errors may not be tenable in many such interdisciplinary setups. In this study, Kendall's tau-type rank statistics are employed for statistical inference, avoiding most of parametric assumptions to a greater extent. The proposed procedures are compared with Kendall's tau statistic based ones. Applications in microarray data models are stressed. (English) |
| Keyword:
|
dimensional asymptotics |
| Keyword:
|
genomics |
| Keyword:
|
multiple hypotheses testing |
| Keyword:
|
microarray data model |
| Keyword:
|
nonparametrics |
| Keyword:
|
U-statistics |
| MSC:
|
62G10 |
| MSC:
|
62G99 |
| MSC:
|
62H20 |
| MSC:
|
62P10 |
| MSC:
|
62P99 |
| MSC:
|
92C40 |
| MSC:
|
92D10 |
| idZBL:
|
Zbl 1195.62097 |
| idMR:
|
MR2411125 |
| DOI:
|
10.1007/s10492-008-0005-1 |
| . |
| Date available:
|
2010-07-20T12:17:06Z |
| Last updated:
|
2020-07-02 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/140316 |
| . |
| Reference:
|
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