Consider a linear regression model y₁ = x₁β + u₁, where the u₁'S afe weakly dependent random variables, the x₁'s are known design nonrandom variables, and β is an unknown parameter. We define an M-estimator β_n of) β corresponding to a smooth score function. Then, the second-order Edgeworth...

Asymptotic normality is established for a class of statistics which includes as special cases weighted sum of independent and identically distributed (i.i.d.) random variables, unsigned linear rank statistics, signed rank statistics, linear combination of functions of order statistics, and...

In this note, we establish the convergence properties for a broad class of random variables of the form Sn = [integral operator]Fn(Tn - s)[nu]n(ds) where Tn is some random variable, Fn is an empirical distribution function based on an independent sample of size n, and [nu]n is some measure.

Nonparametric factorial designs for multivariate observations are considered under the framework of general rank-score statistics. Unlike most of the literature, we do not assume the continuity of the underlying distribution functions. The models studied include general repeated measures...

Harel and Puri (1989, J. Multivariate Anal. 29) studied the asymptotic behavior of the U-statistic and the one-sample rank order statistic for nonstationary absolutely regular processes. In this note, we present some applications of these results for Markov processes as well as ARMA processes.

In this paper, we study the weak invariance of the multidimensional rank statistic when the underlying random variables are nonstationary absolutely regular.

K. I. Yoshihara (1990,Comput. Math. Appl.19, No. 1, 149-158) proved the weak invariance of the conditional nearest neighbor regression function estimator called the conditional empirical process based on[phi]-mixing observations. In this paper, we extend the result for nonstationary and...

Suppose that {z(t)} is a non-Gaussian vector stationary process with spectral density matrixf([lambda]). In this paper we consider the testing problemH: [integral operator][pi]-[pi] K{f([lambda])} d[lambda]=cagainstA: [integral operator][pi]-[pi] K{f([lambda])} d[lambda][not...

Let be an estimator obtained by integrating a kernel type density estimator based on a random sample of size n from a (smooth) distribution function F. Sufficient conditions are given for the central limit theorem to hold for the target statistic where {Un} is a sequence of U-statistics.