Three contributions to robust regression diagnostics

Robust regression diagnostics with data transformations

The problems of non-normality or functional relationships between variables may often be simplified by an appropriate transformation. However, the evidence for transformations may sometimes depend crucially on one or a few observations. Therefore, the purpose of the paper is to develop a method that will not be influenced by potential outliers during the process of data transformations. The con...

Regression Diagnostics and Remedial Measures

Robust Regression

An introduction to robustness in statistics, with emphasis on its relevance to regression analysis. The weaknesses of the least squares estimator are highlighted, and the idea of error in data re ned. Properties such as breakdown, e ciency and equivariance are discussed and, through consideration of M, S and MM-estimators in relation to these properties, the progressive nature of robust estimat...

Robust Regression

1. Introduction One of the most important statistical tools is a linear regression analysis for many fields. Nearly all regression analysis relies on the method of least squares for estimation of the parameters in the model. A problem that we often encountered in the application of regression is the presence of an outlier or outliers in the data. Outliers can be generated by from a simple opera...

specialized methods to teach spelling: comparing three methods

چکیده: بررسی ادبیات مربوطه در کشور در زمینه یادگیری زبان انگلیسی نشان می‎دهد که علی‎رغم اهمیت املا در فرآیند یادگیری به طور عام و یادگیری زبان انگلیسی به طور خاص، این مولفه از جایگاهی متناسب با اهمیت آن برخوردار نیست و عمدتاً نادیده گرفته شده است. تحقیقات گسترده‎ای در خارج از کشور در مورد ماهیت این مولفه صورت گرفته است، در حالی که به جرأت می‎توان گفت در داخل کشور گامی در مورد درک ماهیت آن و فرآی...