A simulation-based scaled test statistic for assessing model-data fit in least-squares unrestricted factor-analysis solutions

​Rank based Least-squares Independent Component Analysis

  In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...

Scaled total least squares fundamentals

The standard approaches to solving overdetermined linear systems Bx ≈ c construct minimal corrections to the vector c and/or the matrix B such that the corrected system is compatible. In ordinary least squares (LS) the correction is restricted to c, while in data least squares (DLS) it is restricted to B. In scaled total least squares (STLS) [22], corrections to both c and B are allowed, and th...

Backward perturbation analysis for scaled total least-squares problems

The scaled total least-squares (STLS) method unifies the ordinary least-squares (OLS), the total leastsquares (TLS), and the data least-squares (DLS) methods. In this paper we perform a backward perturbation analysis of the STLS problem. This also unifies the backward perturbation analyses of the OLS, TLS and DLS problems. We derive an expression for an extended minimal backward error of the ST...

The least squares fit of a hyperplane to uncertain data

ANALYSIS OF COVARIANCE BASED ON FUZZY TEST STATISTIC

One-way analysis of covariance is a popular and common statisticalmethod, wherein the equality of the means of several random variables whichhave a linear relationship with a random mathematical variable, is tested. Inthis study, a method is presented to improve the one-way analysis of covari-ance when there is an uncertainty in accepting the statistical hypotheses. Themethod deals with a fuzzy...