Asymptotically Efficient Estimation of Covariance Matrices with Linear Structure
نویسندگان
چکیده
منابع مشابه
Penalized maximum-likelihood estimation of covariance matrices with linear structure
y In this paper, a space-alternating generalized expectation-maximization (SAGE) algorithm is presented for the numerical computation of maximum-likelihood (ML) and penalized maximum-likelihood (PML) estimates of the parameters of covariance matrices with linear structure for complex Gaussian processes. By using a less informative hidden-data space and a sequential parameter-update scheme, a SA...
متن کاملStructure of Wavelet Covariance Matrices and Bayesian Wavelet Estimation of Autoregressive Moving Average Model with Long Memory Parameter’s
In the process of exploring and recognizing of statistical communities, the analysis of data obtained from these communities is considered essential. One of appropriate methods for data analysis is the structural study of the function fitting by these data. Wavelet transformation is one of the most powerful tool in analysis of these functions and structure of wavelet coefficients are very impor...
متن کاملLinear Parameter Estimation : Asymptotically Efficient Adaptive Strategies
This paper considers the problem of distributed adaptive linear parameter estimation in multiagent inference networks. Local sensing model information is only partially available at the agents, and interagent communication is assumed to be unpredictable. The paper develops a generic mixed time-scale stochastic procedure consisting of simultaneous distributed learning and estimation, in which th...
متن کاملComputationally efficient maximum-likelihood estimation of structured covariance matrices
A computationally e cient method for structured covariance matrix estimation is presented. The proposed method provides an Asymptotic (for large samples) Maximum Likelihood estimate of a structured covariance matrix and is referred to as AML. A closed-form formula for estimating Hermitian Toeplitz covariance matrices is derived which makes AML computationally much simpler than most existing Her...
متن کاملPartial Estimation of Covariance Matrices
A classical approach to accurately estimating the covariance matrix Σ of a p-variate normal distribution is to draw a sample of size n > p and form a sample covariance matrix. However, many modern applications operate with much smaller sample sizes, thus calling for estimation guarantees in the regime n p. We show that a sample of size n = O(m log p) is sufficient to accurately estimate in oper...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1973
ISSN: 0090-5364
DOI: 10.1214/aos/1193342389