Ӧ�ø���ͳ�� 2010, 26(3) 234-244 DOI:      ISSN: 1001-4268 CN: 31-1256

����Ŀ¼ | ����Ŀ¼ | ������� | �߼�����                                                            [��ӡ��ҳ]   [�ر�]
ѧ������
��չ����
������Ϣ
Supporting info
PDF(263KB)
[HTMLȫ��]
�����[PDF]
�����
�����뷴��
�ѱ����Ƽ�������
�����ҵ����
�������ù�����
����
Email Alert
���Ĺؼ����������
��Ԫ��̬�ֲ�
�����ֲ�
�����ֲ�
���������������
PubMed
��Ԫ��̬�ֲ���VDR��������Ŷȼ���
����,����
����������ѧ����ѧԺ,������ҵ��ѧӦ������ѧԺ
ժҪ��

�����Ԫ��̬��$\chi^{2}$����ͳ����.
��Ԫ��̬�ֲ�ת������$\mathbf{Y}_{d}=R\mathbf{V}_{d}$����Pearson
II�ͷֲ�, ֤����$R^{2}$���ӱ����ֲ�. ���ڱ����ֲ��͵�λ����ȷֲ�,
�õ���Ԫ��̬�Լ���ͳ����$\chi^{2}$�Ľ��������ֲ�. ��Чģ����ʾ,
$\chi^{2}$ͳ��������������Ҫ��Ԫ��̬�Լ���ͳ����.
��iris���ݶ�Ԫ��̬�Ե�����Ŷȼ���.

�ؼ����� ��Ԫ��̬�ֲ�   �����ֲ�   �����ֲ�  
VDR Conditional Tests for Multivariate Normality
Su Yan,Yang Zhenghai
School of Mathematics and Physics,
North China Electric Power University,College of Applied Science,
Beijing University of Technology
Abstract:

The $\chi^{2}$ conditional test for
multivariate normality is suggested. The transformed sample
$\mathbf{Y}_{d}=R\mathbf{V}_{d}$ from a $d$-variate normal
distribution has a symmetric multivariate Pearson type II
distribution, the result that $R^{2}$ has a beta distribution is
proved, the asymptotic Chi squared distribution of the statistic
$\chi^{2}$ based on beta distribution and sphere uniform
distribution is obtained. The Monte Carlo power study for
multivariate normality suggests that our test is a powerful
competitor to existing tests. The goodness-of-fit for multivariate
normality of iris data is analyzed.

Keywords: Multinormal distribution   beta distribution   Chi squared distribution.  
�ո����� 1900-01-01 �޻����� 1900-01-01 ����淢������  
DOI:
������Ŀ:

ͨѶ����: ����
���߼��:
����Email:

�ο����ף�
�������������

Copyright by Ӧ�ø���ͳ��