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pro vyhledávání: '"Yo Sheena"'
Autor:
Yo Sheena
Publikováno v:
Statistical Papers. 64:117-137
For a parametric model of distributions, the closest distribution in the model to the true distribution located outside the model is considered. Measuring the closeness between two distributions with the Kullback–Leibler divergence, the closest dis
Autor:
Yo, Sheena1,2 (AUTHOR)
Publikováno v:
Communications in Statistics: Theory & Methods. 2022, Vol. 51 Issue 3, p701-723. 23p.
Autor:
Yo Sheena
Publikováno v:
Metrika. 82:339-360
For an unknown continuous distribution on the real line, we consider the approximate estimation by discretization. There are two methods for discretization. The first method is to divide the real line into several intervals before taking samples (“
Autor:
Yo Sheena
Publikováno v:
Communications in Statistics - Theory and Methods. 47:4059-4087
For a given parametric probability model, we consider the risk of the maximum likelihood estimator with respect to α-divergence, which includes the special cases of Kullback–Leibler divergence, the Hellinger distance, and essentially χ2-divergenc
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Autor:
Yo Sheena
Publikováno v:
Far East Journal of Theoretical Statistics. 53:187-230
Autor:
Yo Sheena
Publikováno v:
Journal of Multivariate Analysis. 115:301-316
Modified estimators for the contribution rates of population eigenvalues are given under an elliptically contoured distribution. These estimators decrease the bias of the classical estimator, i.e. the sample contribution rates. The improvement of the
Autor:
Yo Sheena, Akimichi Takemura
Publikováno v:
Journal of Multivariate Analysis. 102:801-815
An admissible estimator of the eigenvalues of the variance-covariance matrix is given for multivariate normal distributions with respect to the scale-invariant squared error loss.
Article
JOURNAL OF MULTIVARIATE ANALYSIS. 102(4): 801-815(20
Article
JOURNAL OF MULTIVARIATE ANALYSIS. 102(4): 801-815(20
Autor:
Yo Sheena, Akimichi Takemura
Publikováno v:
Journal of Multivariate Analysis. 99:751-775
This paper deals with the asymptotic distribution of Wishart matrix and its application to the estimation of the population matrix parameter when the population eigenvalues are block-wise infinitely dispersed. We show that the appropriately normalize
Autor:
Yo Sheena, Akimichi Takemura
Publikováno v:
Statistical Methodology. 4:158-184
Takemura and Sheena [A. Takemura, Y. Sheena, Distribution of eigenvalues and eigenvectors of Wishart matrix when the population eigenvalues are infinitely dispersed and its application to minimax estimation of covariance matrix, J. Multivariate Anal.