This paper discusses the asymptotic representations of a class of L2-distance estimators based on weighted empirical processes in a multiple linear regression model when the errors are a function of stationary Gaussian random variables that are long-range dependent. Unlike the independent errors...

A standard assumption while deriving the asymptotic distribution of the quasi maximum likelihood estimator in ARCH models is that all ARCH parameters must be strictly positive. This assumption is also crucial in deriving the limit distribution of appropriate linear estimators (LE). We propose a...

This paper discusses the asymptotics of two-stage least squares estimator of the parameters of ARCH models. The estimator is easy to obtain since it involves solving two sets of linear equations. At the same time, the estimator has the same asymptotic efficiency as that of the widely used...

This paper develops an asymptotic theory for R-estimation based on a square-integrable, not necessarily bounded, score function in the pth order stationary autoregressive model. Asymptotic uniform linearity of a class of linear rank statistics is established and the asymptotic normality of the...

A Central Limit Theorem for a triangular array of row-wise independent Hilbert-valued random elements with finite second moment is proved under mild convergence requirements on the covariances of the row sums and the Lindeberg condition along the evaluations at an orthonormal basis. A Central...

In this paper, we discuss an asymptotic distributional theory of three broad classes of robust estimators of the regression parameter namely, L-, M- and R-estimators in a linear regression model when the errors are generated by an exponentially subordinated strongly dependent process. The...