Anderson–Darling Tests of Goodness-of-Fit

Abstract

In statistics, the technique of least squares is used for estimating the unknown parameters in a linear regres-sion model (see Linear Regression Models). is method minimizes the sum of squared distances between the observed responses in a set of data, and the responses from the regression model. Suppose we observe a collec-tion of data {y i , x i } n i= on n units, where y i s are responses and x i = (x i , x i , . . . , x ip) T is a vector of predictors. It is convenient to write the model in matrix notation, as, y = Xβ + ε, () where y is n ×  vector of responses, X is n × p matrix, known as the design matrix, β = (β  , β  , . . . , β p) T is the unknown parameter vector and ε is the vector of random errors. In ordinary least squares (OLS) regression, we esti-mate β by minimizing the residual sum of squares, RSS = (y − Xβ)