Results 11 to 20 of about 428 (69)
A note on a Mar\v{c}enko-Pastur type theorem for time series [PDF]
, 2011 In this note we develop an extension of the Mar\v{c}enko-Pastur theorem to time series model with temporal correlations. The limiting spectral distribution (LSD) of the sample covariance matrix is characterised by an explicit equation for its Stieltjes ...
Bai +10 more
core +2 more sources
Bayesian method for solving the problem of multicollinearity in regression
Afrika Statistika, 2018 The popular method of estimation in regression, Ordinary Least Squares (OLS) often displays inefficiency especially with large variances and wide confidence intervals thereby making precise estimate difficult when there is strong multicollinearity ...
A. Adepoju, Oluwadare O. Ojo
semanticscholar +1 more source
Gaussian approximation of Gaussian scale mixture [PDF]
, 2019 For a given positive random variable $V>0$ and a given $Z\sim N(0,1)$ independent of $V$, we compute the scalar $t_0$ such that the distance between $Z\sqrt{V}$ and $Z\sqrt{t_0}$ in the $L^2(\R)$ sense, is minimal.
Letac, Gérard, Massam, Hélène
core +2 more sources
On a zonal polynomial integral
Journal of Applied Mathematics, Volume 2003, Issue 11, Page 569-573, 2003., 2003 A certain multiple integral occurring in the studies of Beherens‐Fisher multivariate problem has been evaluated by Mathai et al. (1995) in terms of invariant polynomials. However, this paper explicitly evaluates the context integral in terms of zonal polynomials, thus establishing a relationship between zonal polynomial integrals and invariant ...
A. K. Gupta, D. G. Kabe
wiley +1 more source
Null distribution of multiple correlation coefficient under mixture normal model
International Journal of Mathematics and Mathematical Sciences, Volume 30, Issue 4, Page 249-255, 2002., 2002 The multiple correlation coefficient is used in a large variety of statistical tests and regression problems. In this article, we derive the null distribution of the square of the sample multiple correlation coefficient, R2, when a sample is drawn from a mixture of two multivariate Gaussian populations.
Hydar Ali, Daya K. Nagar
wiley +1 more source
Tests for mean vectors with two-step monotone missing data for the k-sample problem
SUT Journal of Mathematics, 2012 We continue our recent work on the problem of testing the equality of two normal mean vectors when the data have two-step monotone pattern missing observations.
Noriko Seko
semanticscholar +1 more source
Matrix‐variate beta distribution
International Journal of Mathematics and Mathematical Sciences, Volume 24, Issue 7, Page 449-459, 2000., 2000 We propose matrix‐variate beta type III distribution. Several properties of this distribution including Laplace transform, marginal distribution and its relationship with matrix‐variate beta type I and type II distributions are also studied.
Arjun K. Gupta, Daya K. Nagar
wiley +1 more source
, 2017 In this paper, we derive the asymptotic distributions of the characteristic roots in multivariate linear models when the dimension p and the sample size n are large.
Y. Fujikoshi
semanticscholar +1 more source
Distribution of LRC for testing sphericity of a complex multivariate Gaussian model
International Journal of Mathematics and Mathematical Sciences, Volume 8, Issue 3, Page 555-562, 1985., 1985 In this paper, exact null distribution of the likelihood ratio criterion for testing sphericity structure in a complex multivariate normal covariance matrix is obtained in computable series form. The method of inverse Mellin transform and contour integration has been used.
D. K. Nagar, S. K. Jain, A. K. Gupta
wiley +1 more source
On the noncentral distribution of the ratio of the extreme roots of wishart matrix
International Journal of Mathematics and Mathematical Sciences, Volume 4, Issue 1, Page 147-154, 1981., 1981 The distribution of the ratio of the extreme latent roots of the Wishart matrix is useful in testing the sphericity hypothesis for a multivariate normal population. Let X be a p × n matrix whose columns are distributed independently as multivariate normal with zero mean vector and covariance matrix ∑.
V. B. Waikar
wiley +1 more source