TY - JOUR
T1 - Uniform approximations for distributions of continuous random variables with application in dual STATIS method.
AU - Ramos, Luís Pedro Carneiro
AU - Lita da Silva, João Filipe
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The matrix S = [tr(W(i)QW(j)Q)] (i) ,(j=1) (,...,) (k) where Q is a symmetric positive definite matrix and W-i = X-i'DiXi, i = 1,..., k is formed by data tables X-i and diagonal matrices of weights D-i, plays a central role in dual STATIS method. In this paper, we approximate the distribution function of the entries of S, assuming data tables X-i given by U-i + E-i, i = 1,..., k with independent random matrices E-i representing errors, in order to obtain (approximately) the distribution of Sv, where v is the orthonormal eigenvector of S associated to the largest eigenvalue. To achieve this goal, we approximate uniformly the distribution of each entry of S. In general, our technique consists in to approximate uniformly the distribution sequence {g(V-n + mu(n)), n >= 1}, where g is some smooth function of several variables, {V-n, n >= 1} is a sequence of identically distributed random vectors of continuous type and {mu(n)} is a non-random vector sequence.
AB - The matrix S = [tr(W(i)QW(j)Q)] (i) ,(j=1) (,...,) (k) where Q is a symmetric positive definite matrix and W-i = X-i'DiXi, i = 1,..., k is formed by data tables X-i and diagonal matrices of weights D-i, plays a central role in dual STATIS method. In this paper, we approximate the distribution function of the entries of S, assuming data tables X-i given by U-i + E-i, i = 1,..., k with independent random matrices E-i representing errors, in order to obtain (approximately) the distribution of Sv, where v is the orthonormal eigenvector of S associated to the largest eigenvalue. To achieve this goal, we approximate uniformly the distribution of each entry of S. In general, our technique consists in to approximate uniformly the distribution sequence {g(V-n + mu(n)), n >= 1}, where g is some smooth function of several variables, {V-n, n >= 1} is a sequence of identically distributed random vectors of continuous type and {mu(n)} is a non-random vector sequence.
KW - uniform approximations
KW - dual STATIS method
M3 - Article
SN - 1645-6726
VL - 12
SP - 101
EP - 118
JO - REVSTAT: Statistical Journal
JF - REVSTAT: Statistical Journal
IS - 2
ER -