Patterns of genetic variation and covariation strongly affect the rate and direction of evolutionary change by limiting the amount and form of genetic variation available to natural selection. We studied evolution of morphological variance-covariance structure among seven populations of house finches (Carpodacus mexicanus) with a known phylogenetic history. We examined the relationship between ...

The sandwich estimator, often known as the robust covariance matrix estimator or the empirical covariance matrix estimator, has achieved increasing use with the growing popularity of generalized estimating equations. Its virtue is that it provides consistent estimates of the covariance matrix for parameter estimates even when a parametric model fails to hold, or is not even specified. Surprisin...

In order to be able to design the likelihood ratio classifier, we make the following assumptions: • The conditional probability density functions p(x|ci) are normal with mean μi and covariance Ci • All classes have different mean, but the same covariance, i.e. Ci = Cw. This covariance is called the within class variance. The variance within a class is e.g. caused by expression and illumination ...

We give a definition for lower and upper covariance in Walley’s theory of imprecise probabilities (or coherent lower previsions) that is direct, i.e., does not refer to credal sets. It generalizes Walley’s definition for lower and upper variance. Just like Walley’s definition of lower and upper variance, our definition for lower and upper covariance is compatible with the credal set approach; i...

In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through deviations of transformed random process find an efficient estimate Hurst exponent eigenvalue regression covariance matrix. The results simulations experiments shown that performance proposed estimator was in bias but variance get increase as signal change from short long me...

Abstract In this paper we introduce a new criterion in order to measure the variance and covariance risks in financial markets. In an asset allocation framework with stochastic (co)variances, we consider the possibility to invest also in variance swaps, that are assets which span the volatility as well as the co-volatility risks. We provide explicit solutions for the portfolio optimization prob...

The specific growth rate for P. aeruginosa and four mutator strains mutT, mutY, mutM and mutY-mutM is estimated by a suggested Maximum Likelihood, ML, method which takes the autocorrelation of the observation into account. For each bacteria strain, six wells of optical density, OD, measurements are used for parameter estimation. The data is log-transformed such that a linear model can be applie...

Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly due to estimation error. Moreover, it is commonly accepted that estimation error in the sample mean is much larger than in the sample covariance matrix. For this reason, recent research has focused on the minimum-variance portfolio, which relies only on estimates of the covariance ma...

Principal component analysis (PCA) is a popular technique to reduce the dimension of the data at hand. Since PCA is based on the empirical variance-covariance matrix, the estimates can be severely damaged by outliers. To reduce these effects, several robust methods were developed, mostly by replacing the classical variance-covariance matrix by a robust version. In this paper we focus on Stahel-...

We develop and test robust methods for design construction, for estimation and for prediction in spatial studies. The designs are robust against misspecified variance/covariance structures, and against misspecified regression responses. Robustness against contaminated error distributions is provided by the use of generalized M-estimators in the estimation and prediction procedures. The loss fun...