Bayes estimation of number of signals
From MaRDI portal
Publication:1207627
DOI10.1007/BF00118633zbMath0761.62030OpenAlexW2071687055MaRDI QIDQ1207627
Madhusudan Bhandary, Naveen K. Bansal
Publication date: 1 April 1993
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf00118633
partitionseigenvaluesBayes estimationHaar measuresignal processingmonomial symmetric functionszonal polynomialssample covariance matrixbinomial prior distributioncolored-noise casenumber of signalsPosterior distributionswhite-noise case
Bayesian inference (62F15) Applications of statistics (62P99) Survival analysis and censored data (62N99) Inference from stochastic processes (62M99)
Related Items
A Selection Procedure for the Number of Signals in Presence of Colored Noise ⋮ Estimating the number of signals in the presence of white noise ⋮ Bayes estimation of intraclass correlation coefficient
Cites Work
- Unnamed Item
- Unnamed Item
- Formulas for zonal polynomials
- On the expansion of \(C^*_{\rho}(V+I)\) as a sum of zonal polynomials
- Modeling by shortest data description
- Invariant polynomials with two matrix arguments extending the zonal polynomials: Applications to multivariate distribution theory
- Some recent developments on complex multivariate distributions
- Zonal polynomials: an alternative approach
- Estimating the dimension of a model
- On detection of the number of signals in presence of white noise
- On detection of the number of signals when the noise covariance matrix is arbitrary
- On the distribution of the maximum latent root of a positive definite symmetric random matrix
- Special Functions of Matrix Argument. I: Algebraic Induction, Zonal Polynomials, and Hypergeometric Functions
- The Linearization of the Product of Two Zonal Polynomials
- Bayes estimation of the binomial parameter n
- On the Non-Central Distributions of Two Test Criteria in Multivariate Analysis of Variance
- The Distribution of the Latent Roots of the Covariance Matrix
- Asymptotic Theory for Principal Component Analysis
- Distributions of Matrix Variates and Latent Roots Derived from Normal Samples
- Some Non-Central Distribution Problems in Multivariate Analysis
