A Simple Method for Weighted Bending of Genetic (Co)variance Matrices

Better estimates of genetic covariance matrices by "bending" using penalized maximum likelihood.

Obtaining accurate estimates of the genetic covariance matrix Sigma(G) for multivariate data is a fundamental task in quantitative genetics and important for both evolutionary biologists and plant or animal breeders. Classical methods for estimating Sigma(G) are well known to suffer from substantial sampling errors; importantly, its leading eigenvalues are systematically overestimated. This art...

Bending over Backwards: Better Estimates of Genetic Covariance Matrices by Penalized REML

Knowledge of genetic parameters and variances is an essential pre-requisite for tasks such as the design of selection programmes or prediction of breeding values. Reliable estimation of these quantities is thus paramount. There is a growing trend to consider more and more complex phenotypes, necessitating multivariate analyses comprising numerous traits. Problems inherent in such analyses, aris...

Information Covariance Matrices for Multivariate Burr III and Logistic Distributions

Main result of this paper is to derive the exact analytical expressions of information and covariance matrices for multivariate Burr III and logistic distributions. These distributions arise as tractable parametric models in price and income distributions, reliability, economics, Human population, some biological organisms to model agricultural population data and survival data. We showed that ...

A Method for Defuzzication by Weighted Distance

Interval Weighted Comparison Matrices – A Review

Nowadays, interval comparison matrices (ICM) take an important role in decision making under uncertainty. So it seems that a brief review on solution methods used in ICM should be useful. In this paper, the common methods are divided into four categories that are Goal Programming Method (GPM), Linear Programming Method (LPM), Non-Linear Programming Method (NLPM) and Statistic Analysis (SA). GPM...