In simulation we often have to generate correlated random variables by giving a reference intercorrelation matrix, R or Q. The matrix R is positive definite and a valid correlation matrix. The matrix Q may appear to be a correlation matrix but it may be invalid (negative definite). With R(m,m)...

The nearest correlation matrix problem is to find a valid (positive semidefinite) correlation matrix, R(m,m), that is nearest to a given invalid (negative semidefinite) or pseudo-correlation matrix, Q(m,m); m larger than 2. In the literature on this problem, 'nearest' is invariably defined in...

In simulation we often have to generate correlated random variables by giving a reference intercorrelation matrix, R or Q. The matrix R is positive definite and a valid correlation matrix. The matrix Q may appear to be a correlation matrix but it may be invalid (negative definite). With R(m,m)...

The nearest correlation matrix problem is to find a valid (positive semidefinite) correlation matrix, R(m,m), that is nearest to a given invalid (negative semidefinite) or pseudo-correlation matrix, Q(m,m); m larger than 2. In the literature on this problem, 'nearest' is invariably defined in...

This paper demonstrates that if we intend to optimally rank order n objects (candidates) each of which has m attributes or rank scores awarded by m evaluators, then the ordinal ranking of objects by the conventional principal component based factor scores turns out to be suboptimal. Three...

Rank-ordering of individuals or objects on multiple criteria has many important practical applications. A reasonably representative composite rank ordering of multi-attribute objects/individuals or multi-dimensional points is often obtained by the Principal Component Analysis, although much...

This paper proposes a novel method of global optimization based on cuckoo-host co-evaluation. It also develops a Fortran-77 code for the algorithm. The algorithm has been tested on 96 benchmark functions (of which the results of 30 relatively harder problems have been reported). The proposed...

This paper proposes a novel method of global optimization based on cuckoo-host co-evaluation. It also develops a Fortran-77 code for the algorithm. The algorithm has been tested on 96 benchmark functions (of which the results of 30 relatively harder problems have been reported). The proposed...

The Two-Stage Least Squares (2-SLS) is a well known econometric technique used to estimate the parameters of a multi-equation (or simultaneous equations) econometric model when errors across the equations are not correlated and the equation(s) concerned is (are) over-identified or exactly...

This paper proposes a novel method of global optimization based on cuckoo-host co-evaluation. It also develops a Fortran-77 code for the algorithm. The algorithm has been tested on 96 benchmark functions (of which the results of 30 relatively harder problems have been reported). The proposed...