-
作者：YAJIMA, Y
摘要：We consider asymptotic properties of the least squares estimator (LSE) in a regression model with long-memory stationary errors. First we derive a necessary and sufficient condition that the LSE be asymptotically efficient relative to the best linear unbiased estimator (BLUE). Then we derive the asymptotic distribution of the LSE under a condition on the higher-order cumulants of the white-noise process of the errors.
-
作者：OWEN, A
摘要：Empirical likelihood is a nonparametric method of inference. It has sampling properties similar to the bootstrap, but where the bootstrap uses resampling, it profiles a multinomial likelihood supported on the sample, Its properties in i.i.d. settings have been investigated in works by Owen, by Hall and by DiCiccio, Hall and Romano. This article extends the method to regression problems. Fixed and random regressors are considered, as are robust and heteroscedastic regressions. To make the exten...
-
作者：RITOV, Y; GILULA, Z
摘要：The RC model has been proposed as a model for ordered contingency tables. It involves parametric scores that are assigned to the rows and columns of the table so that these scores reflect the ordinality of the row and column categories. Efficient estimation of these parameters subject to order constraints remained an open problem mainly due to severe difficulties in computing these estimates and difficulties in deriving an appropriate asymptotic goodness-of-fit test. A nonstandard yet very sim...
-
作者：KHAN, RA
摘要：Let {X(n), n greater-than-or-equal-to 1} be a sequence of random variables and let P-theta be a probability measure under which (X1, ..., X(n)) have joint pdf's f(n)(X1,..., X(n),theta)= L(n)(theta), n greater-than-or-equal-to 1. Suppose u(n) = u(n)(X1,..., X(n)), n greater-than-or-equal-to 1, are statistics such that (u(n) - c)(L(n)(theta') - L(n)(theta)) greater-than-or-equal-to 0, for all inverted A (X1,...,X(n)) n greater-than-or-equal-to 1, for some constant c = c(theta, theta'), theta no...
-
作者：BASAWA, IV; MALLIK, AK; MCCORMICK, WP; REEVES, JH; TAYLOR, RL
摘要：Consider a first-order autoregressive process X(t) = beta-X(t-1) + epsilon-t, where {epsilon-t} are independent and identically distributed random errors with mean 0 and variance 1. It is shown that when beta = 1 the standard bootstrap least squares estimate of beta-is asymptotically invalid, even if the error distribution is assumed to be normal. The conditional limit distribution of the bootstrap estimate at beta = 1 is shown to converge to a random distribution.
-
作者：DEJONG, P
摘要：The Kalman recursion for state space models is extended to allow for likelihood evaluation and minimum mean square estimation given states with an arbitrarily large covariance matrix. The extension is computationally minor. Application is made to likelihood evaluation, state estimation, prediction and smoothing.
-
作者：MESSER, K
摘要：It has been observed that to a smoothing spline operator there corresponds an equivalent kernel operator; these two operators have been compared in a variety of norms [Cox (1984), Silverman (1984)]. In this paper, we refine the existing bounds for the particular case of the spline estimator considered in Rice and Rosenblatt (1983) and its corresponding equivalent kernel estimator. We obtain detailed asymptotic expressions for the bias and covariance functions of the two estimates and provide r...
-
作者：DHAR, SK
摘要：In the additive effects outliers (A.O.) model considered here one observes Y(j,n) = X(j) + V(j,n), O less-than-or-equal-to j less-than-or-equal-to n, where {X(j)} is the first order autoregressive [AR(1)] process with the autoregressive parameter \rho\ < 1. The A.O.'s {V(j,n), O less-than-or-equal-to n} are i.i.d. with distribution function (d.f.) (1 - gamma-n)I[x greater-than-or-equal-to 0] + gamma-nL(n)(x), x epsilon R, 0 less-than-or-equal-to gamma-n less-than-or-equal-to 1, where the d.f.'...
-
作者：LOPUHAA, HP; ROUSSEEUW, PJ
作者单位：University of Antwerp
摘要：Finite-sample replacement breakdown points are derived for different types of estimators of multivariate location and covariance matrices. The role of various equivariance properties is illustrated. The breakdown point is related to a measure of performance based on large deviations probabilities. Finally, we show that one-step reweighting preserves the breakdown point.
-
作者：PUKELSHEIM, F; TORSNEY, B
作者单位：University of Glasgow
摘要：An explicit formula is derived to compute the A-optimal design weights on linearly independent regression vectors, for the mean parameters in a linear model with homoscedastic variances. The formula emerges as a special case of a general result which holds for a wide class of optimality criteria. There are close links to iterative algorithms for computing optimal weights.
