The spatial sign covariance matrix (SSCM), also known as the normalized sample (NSCM), has been widely used in signal processing a robust alternative to (SCM). It is well-known that SSCM does not provide consistent estimates of eigenvalues shape (normalized scatter matrix). To alleviate this problem, we propose BASIC (Bias Adjusted SIgn Covariance), which performs an approximate bias correction...

Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. The intraclass covariance matrix is appropriate whenever the research design or mathematical mode...

Multivariate reward processes with reward functions of constant rates, defined on a semi-Markov process, first were studied by Masuda and Sumita, 1991. Reward processes with nonlinear reward functions were introduced in Soltani, 1996. In this work we study a multivariate process , , where are reward processes with nonlinear reward functions respectively. The Laplace transform of the covar...

Estimation of population covariance matrices from samples of multivariate data is important. (1) Estimation of principle components and eigenvalues. (2) Construction of linear discriminant functions. (3) Establishing independence and conditional independence. (4) Setting confidence intervals on linear functions. Suppose we observed p dimensional multivariate samples X1, X2, · · · , Xn i.i.d. wi...

By drawing an analogy between the logarithm of a probability distribution and a physical potential, it is natural to ask the question, “what is the effect of applying an external force on model parameters?" In Bayesian inference, parameters are frequently estimated as those that maximize the posterior, yielding the maximum a posteriori (MAP) solution, which corresponds to minimizing φ = −log(po...

We propose the construction of an appearance manifold with embedded view-dependent covariance matrix to recognize 3D objects which are influenced by geometric distortions and quality degradation effects. The appearance manifold is used to capture the pose variability, while the covariance matrix is used to learn the distribution of samples for gaining noise-invariance. However, since the appear...

Evolution Strategies (ES) are a class of Evolutionary Algorithms based on Gaussian mutation and deterministic selection. Gaussian mutation captures pair-wise dependencies between the variables through a covariance matrix. Covariance Matrix Adaptation (CMA) is a method to update this covariance matrix. In this paper, the CMA-ES, which has found many applications in solving continuous optimizatio...

Adaptive beamforming methods degrade in the presence of model mismatch. In this paper, we develop a modified interference covariance matrix reconstruction based beamformer that is robust against large array calibration errors. The calibration errors can come from the element position errors, and/or amplitude and phase errors, etc.. The proposed method is based on the fact that the sample covari...