ABSTRACT
Consider the linear regression model https://www.w3.org/1998/Math/MathML"> Y i = x i ′ β + U i , i = 1 , … , n https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781315268125/d57fa0be-4eee-4d70-bfcd-ee2d3690ba91/content/math4_1.jpg" xmlns:xlink="https://www.w3.org/1999/xlink"/> with observations Y 1,…,Y n , unknown and unobservable parameter β ∈ ℝ p , where x i ∈ ℝ p , i = 1,…, n are either given deterministic vectors or observable random vectors (regressors) and U 1,…,U n are independent errors with a joint distribution function F. Often we consider the model in which the first component β 1 of β is an intercept: it means that x i1 = 1, i = 1,…, n. Distribution function F is generally unknown; we only assume that it belongs to some family https://www.w3.org/1998/Math/MathML"> F https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781315268125/d57fa0be-4eee-4d70-bfcd-ee2d3690ba91/content/inline-math4_1.jpg" xmlns:xlink="https://www.w3.org/1999/xlink"/> of distribution functions.
