this article examines statistical inference for  where and are independent but not identically distributed pareto of the first kind (pareto (i)) random variables with same scale parameter but different shape parameters. the maximum likelihood, uniformly minimum variance unbiased and bayes estimators with gamma prior are used for this purpose. simulation studies which compare the estimators are ...

The minimum variance (MV) spectral estimator is a robust high-resolution frequencydomain analysis tool for short data records. The traditional formulation of the minimum variance spectral estimation (MVSE) depends on the inverse of a Toeplitz autocorrelation matrix, for which a fast computational algorithm exists that exploits this structure. This paper extends the MVSE approach to two data-onl...

Tuning a state estimator for a linear state space model requires knowledge of the characteristics of the independent disturbances entering the states and the measurements. In Odelson, Rajamani, and Rawlings (2006), the correlations between the innovations data were used to form a least-squares problem to determine the covariances for the disturbances. In this paper we present new and simpler ne...

Many different methods for statistical data editing can be found in the literature but only few of them are based on robust estimates (for example such as BACON-EEM, Epidemic algorithms (EA) and Transformed rank correlation (TRC) methods of Béguin and Hulliger). However, we can show that outlier detection is only reasonable if robust methods are applied, because the classical estimates are them...

We propose a simple algorithm for improving the MDL (minimum description length) estimator of the number of sources of signals impinging on multiple sensors. The algorithm is based on the norms of vectors whose elements are the normalized and nonlinearly scaled eigenvalues of the received signal covariance matrix and the corresponding normalized indexes. Such norms are used to discriminate the ...

In modern activities such as banking, homeland security, information transportation, telecommunication, etc., people work with large and high dimension data sets. But, the higher the dimension the higher the probability that outliers will be present in the data sets. The ability to detect outliers in high dimension multivariate data sets is a challenging task. In this circumstance, robust estim...

In this paper, we propose an alternative covariance estimator to the robust covariance estimator of generalized estimating equations (GEE). Hypothesis tests using the robust covariance estimator can have inflated size when the number of independent clusters is small. Resampling methods, such as the jackknife and bootstrap, have been suggested for covariance estimation when the number of cluster...