This paper gives a short review of the development of genetic parameter estimation over the last 40 years. This shows the development of more statistically and computationally efficient methods that allow the fitting of more biologically appropriate models. Methods have evolved from direct methods based on covariances between relatives to methods based on individual animal models. Maximum-likel...

In the problems concerning prediction and modeling, parameters estimation constitutes one of the main uncertain items that must be taken into account. The easiest way to minimize this uncertainty is to collect great amounts of data. The aim of this work is to build a decision model able to choose the optimal position of the sample point used for the parameters estimation, minimizing the paramet...

One of the most important and valuable goal of software development life cycle is software cost estimation or SCE. During the recent years, SCE has attracted the attention of researchers due to huge amount of software project requests. There have been proposed so many models using heuristic and meta-heuristic algorithms to do machine learning process for SCE. COCOMO81 is one of the most popular...

as is well known, estimating parameters of the tree-parameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. weibull distribution involves in reliability studies frequently and has many applications in engineering. however estimating the parameters of weibull distribution is crucial in classical ways. this distribution has t...

We investigate the use of Robust Estimation in an application requiring the accurate location of the centres of circular objects in an image. A common approach used throughout computer vision for extracting shape information from a data set is to fit a feature model using the Least Squares method. The well known sensitivity of this method to outliers is traditionally accommodated by outlier rej...